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Top Data Science Certifications In 2022- Exclusive To Banking Professionals

Do you hold a finance or banking background? These 5 data science courses for banking professionals can make you eligible for a 400% hike.

In the last two years (2020 end to -2022 mid), folks of people have switched their jobs. The most hiked figures you could find from the fintech companies like HSBC, JP Morgan, etc. But by chance, if you think that such a hiring source was exclusive to IT pros- then you are damn wrong. The positions like ‘financial analyst,’ ‘credit risk analyst,’ AI experts, ML engineer, and data scientist were actually the top positions. 

Yes, the above roles have already overtaken traditional IT roles from the perspective of salary, job security, and future growth. But pursuing a random data science course to target these roles will be the biggest mistake of your life. Although it is the least exposed, there lies a bunch of financial analytics courses or exclusive data science certifications for banking professionals. 


Why such a huge surge of data science jobs in the BFSI domain?

The BFSI sector is currently quite technically advanced. In rare circumstances, the consumer needs a visit to the branches. Everything is now virtual. But the main consequence of this is the potential of secret data disclosure within the multiple IoT layers. And not only that, starting from competitor analysis to consumer targeting and strategy planning, everything is now an unending number of live data assessments. Hence, data scientists, financial analysts, and other analytical roles are in high demand within the existing BFSI industries.

The demand for senior or entry-level financial analysts keeps expanding. The field of financial analysis is growing in tandem with big data. The financial analyst’s job is to expand at a rate of roughly 11% between 2016 and 2026.


Financial analysts work for banks, insurance companies, real estate, and investment brokerages. Financial analysts are necessary for any company. They make crucial judgments about how to spend. Knowledge of relational databases, as well as statistical and graphical software, is critical. The Online Financial Analyst certification course is an excellent option. Even for current professionals looking to brush up on their skills.

The Best Data Science Course For Finance are:


1.The Complete Financial Analyst Course 2020 (Udemy)

With over 200,000 students enrolled, this is the best data science course for Finance on Udemy.

This course is a combination of 10 different aspects of a financial analyst, and those are:

  • Working Capital management
  • Fundamentals of Financial Analysis
  • Capital Budgeting
  • Financial Modeling
  • PowerPoint
  • Beginner and Intermediate Excel
  • Advanced Excel
  • Accounting
  • Advanced Accounting
  • Financial Statement Analysis

Course Highlights :

  • Learn about 14 financial analyst jobs and how they connect.
  • Learn about risk management
  • Profit-and-loss analysis with live data 
  • Learn how interest rates are customized and why it’s important for successful financial analysts to grasp them.
  • How Monetary and Fiscal Policy Function via live data analysis
  • Create financial statements from scratch. Build Excel-based templates (meaning income statements, balance sheets, cash flow statements, and more)
  • ou Learn financial modeling best practices
  • To add to your portfolio, earn a certificate of completion.

Duration – 17.5 hours of on-demand video


2.Credit Risk and Credit Analysis Certification  (edX)

EdX collaborates with the world’s top schools and universities. To give students what they want. It offers online data science finance courses from top universities. You can master financial analysis and become a great senior financial analyst. There are courses for entry-level financial analysts to senior financial analysts. Most of the data science courses in the finance industry and training programs are available to audit for free. But this particular financial analytics course costs approx 1 lac INR.

Course Highlights :

  • 5 skill-building courses with expert instruction 
  • Risk management and credit principles
  • Ratio Analysis 
  • Projections and Structuring
  • Cash Flow analysis
  • Certificate from New York Institute of Finance 
  • Ratio analysis 
  • Cash flow analysis 
  • Credit risk analysis

Duration: self-paced

Duration: 1 hour of on-demand video


3.Certified Data Scientist- Financial Certification (IABAC)

This IABAC Business Analytics in Banking certification focuses on the applications of business analytics in the banking industry. Specifically for identifying areas in the banking business where cost savings can be made. This Data Science finance course specially provides you with knowledge of the tools and best practices for deploying data science models in Finance. Finance essential processes, workflow optimization, and predictive analytics application in Finance are all covered in this course.

Course Highlights :

  1. Predictive Analytics Application in Finance
  2. Challenges
  3. Use Cases and Project
  4. Certified Data Science Syllabus
  5. Finance industry Overview
  6. Finance Key Processes
  7. The role of Data Scientists
  8. Workflow Optimization


4.Advanced data science and AI program by Learnbay

Studying from recorded videos can be tedious; Learnbay offers an instructor-led interactive program with live doubt-solving sessions. This particular course is a BFSI domain specialization. Students get to work on live capstone projects. This assists in making a career change as an entry-to-senior-level financial analyst. They have Data science expertise from FAANG. This course is all about putting your domain-specific knowledge to good use. Imply data-driven strategies to balance knowledge. Two or three units of the subject are insufficient for industrial requirements. It is full-stack data science and AI course with the option of BFSI domain specialization. Hence, you can consider it a financial analyst certification course also. Designed especially for working professionals. After your course completion, you will receive an IBM certificate for the same, with certificates for the tools you have expertise in. And not only that, you will even receive certificates for project completion.

Course highlight :

  • 12+ Real-Time Projects
  • 2 Capstone Projects
  • Every module will be BFSI domain focused
  • Mentorship & Guidance by FAANG Expert
  • one-on-one doubt clearance sessions.
  • 3-year session/classroom session and lifetime access to LMS
  • Industry Accredited Global Certification Course In collaboration with IBM.
  • Special classes for Non-programming background students
  • Fresh project from BFSI domain and project experience certificate from IBM

Course Duration: Weekday – 7.5 months

Weekend – 9 months


5.Python for Financial Analysis and Algorithmic Trading (Udemy)

This course consists of a thorough introduction to data science in the finance industry. The key focus lies on how Python helps to analyze financial data and perform algorithmic trading.

The course’s curriculum is comprehensive and well-organized. It begins with a basic crash course in Python so you can study the programming language. Before moving on to the core libraries for analysis, such as NumPy, pandas, and matplotlib. Then goes over how to use 

  • Time series
  • Big data 
  • Various stats models package is also included It also covers
  • Pandas-DataReader 
  • Quandl Python API. 
  • How to work with Python to get financial securities data 
  • Quantopian, one of the greatest online platforms for algorithmic trading 
  • Quantopian with Python

Course Highlights :

  • Use ARIMA models on Time Series Data
  • Calculate the Sharpe Ratio
  • Optimize Portfolio Allocations
  • Understand the Capital Asset Pricing Model
  • Learn about the Efficient Market Hypothesis
  • Conduct algorithmic Trading on Quantopian
  • Q&A forum
  • NumPy tool to work with Numerical Data
  • Use Pandas to Analyze and Visualize Data
  • Use Matplotlib to create custom plot
  • Time Series Analysis
  • Calculate Financial Statistics, Cumulative Returns, Daily Returns, Volatility, etc.
  • Use Weighted Moving Averages

Course Duration: 17 hrs on-demand video


6.Learnbay – data science and AI for managers and leaders

Get cutting-edge industrial leadership training for the domains of Data Science in the finance industry. The above courses will not work for the pros already having 7+ years of experience. And the occurrence of a data science course that is specially built for this much experienced BFSI pros is very rare. Learnbay data science and AI courses for Managers and Leaders to solve this problem. Here, you can choose the banking and finance domain as your specialization option. Even you get an option for upgrading with a data science course with a job guarantee. It ensures a financial analyst course with placement. If students do not get a job in 6 months, they can claim their fees back.

Course highlights:

  • 15+ Real-Time Projects
  • 2 Capstone Projects
  • Get 8 unique certifications by IBM on course completion 
  • Industry lead curated training 
  • 100% interview guarantee
  • Employer alliance 250+
  • Interview prep session and Mock interview
  • Guaranteed job referrals
  • Globally recognized Data Science and AI program certified by IBM.

Course Duration :

Weekday – 11 months

Weekend – 13 months

So, these are the list of best data science courses for banking professionals. All of the financial analyst courses are available online to assist you in your quest to master financial analysis. You will learn the necessary skills and tools to become a competent financial analyst- such as financial statement analysis and valuation, financial data analytics, financial planning, analysis, etc. However, choosing a course with a job guarantee seems the best decision if you want a lucrative career within a year. However, it costs a bit higher but offers the best ROI. But yes, whether you are taking a course or not even planning a career shift, still stay updated about data science and AI happenings worldwide. Because within the next few years non, a single job role will be beyond the basic analytical skills. You can get instant updates on the latest news on data science by following us on  Facebook Youtube,  Linkedin,  and Twitter.


How Does Data Science Promote Project Managers to a New Perspective of Success?

Old School Project Management expertise Is Now outdated? But Businesses Saved And Revived By Data Science orientation.

Businesses have changed their work process; consumers have changed their consumption behavior. Accordingly, the service experience expectations of customers are also changing their directions. In one word, the entire business process has been changed.

So it’s quite obvious that the old-school project management tactics are not in a good scene anymore. Folks of prudent managers have already upgraded themselves with analytical skills and started implementing data science in management tasks. The outcomes of the same have been’ just wow.’

Wait… The above scenario is from a few years back. Now data science skill has become one of the measures for successful project managers.

Data is new energy. It has moon-shot sales and performance by integrating data science and AI into systems. It not only generates revenue. But even predicts consumer preferences and trends in the market. And that’s why a  project manager needs it so keenly. Ignoring the target customer orientation,  a manager can never complete a successful project.

Organizations are integrating data science into their business to simplify regular processes. To reach the most profitable outcomes within a targeted timeline, companies are now massively hiring  Data science managers or data science project managers. But wait, here, you might have a pinch of misconception. All the time, it’s not like a data science manager handles data science and AI projects only. Some companies hire for such a designation to make their normal business project highly data-driven and precise.

It is a well-known truth that modern businesses are completely about data. In the previous year, McKinsey estimated that the U.S healthcare system has reduced healthcare project management spending. $2.6 trillion baselines around 12-17% cost on earlier spending on the same. And big data is roughly costing $3.1 trillion a year to the U.S. Data science is not so easy to implement technically and financially as it needs a lot of investment. But yes, the ultimate gain is quite lucrative.


How does Data Science Help Managers In Businesses?

  • Make Smarter Decisions :-

Business intelligence used to be detailed and static in the past. Since data science was introduced in management, it has evolved into a more dynamic discipline. Data Science up-lifted the scope of business intelligence with a range of features.
Managers need data scientists to analyze and draw relevant insights. This analysis is done from a huge volume of data.

These useful insights are aided by data science in management via the analysis of data on a wide scale. To develop appropriate decision-making processes. Reviewing and assessing data is a part of the decision-making process.

Decision making is a four-step process:

  • Understanding the context and nature of the problem, the manager’s task is to reveal the ultimate requirements.
  • Investigating and quantifying the data’s quality.
  • Implying appropriate problem-solving algorithms and tools.
  •  Storytelling-based communication to transform the findings into a greater knowledge of teams.


  • Managing Business

Today’s business works extensively with data. Plenty of data is generated every day, which allows them to get insights through good data analysis. Data Science shows hidden patterns in data and aid in studying and predicting occurrences.

Businesses may manage themselves more efficiently with Data Science. Huge corporations and small enterprises can both enjoy data science for expansion and growth.

Data Science helps managers in the analysis of corporate health. Managers can forecast the success rate through predictive analytics. Here, data scientists convert raw data to cooked data.

This summarizes the company’s success and the product’s health. Data Science identifies critical parameters for determining business performance for managers.

Managers analyze organizations’ performance based on quantitative data. It also assists managers in determining which business applications or problem-solving can surely boost business performance.

Managers also use data science to encourage leadership. Managers can track performance, success rate, and other metrics to know what is best for their business through workforce analytics. 


  • Product Development

Managers, after data analysis, can know which product has to be manufactured that attracts the biggest possible pool of customers. So data is essential in product development.

Take the example of a project manager handling an e-commerce data science project.

Customer review analysis is another side of data science applications in e-commerce. This helps such managers to know what customers think about the product and also helps them in knowing which new product customers want in the market.

Managers use current market trends to create a product for the general public. These market trends give companies insight into the product’s current demand. Innovation allows businesses to grow. Managers can not only sell newer items but also varied inventive techniques as data grows.


  • Predictive analysis and outcomes

The most crucial aspect of data science in management is predictive analytics. Companies’ ability to deal with various types of data has grown. This has led to the introduction of enhanced prediction tools and technology.

Predictive analytics is the statistical analysis of data. It works with other large machine learning algorithms. To forecast future outcomes based on historical data. SAS, IBM SPSS, SAP HANA, and other predictive analytics solutions are available.

Customer segmentation, market analysis, risk assessment, and sales forecasting- are a few business tools of predictive analytics. Managers operate predictive analytics to gain an advantage over their competitors because they can forecast future problems and can take necessary action in response.


  • Data-driven decision

Businesses must make predictions to learn about future outcomes. Businesses make data-driven decisions. Many made terrible decisions in the past owing to a lack of surveys or complete reliance on “gut feelings.” It would lead to bad decisions and millions of dollars in losses.

But, now that there is a wealth of data and the required data analytical tools. The data industries may make reasoned data-driven judgments.

Data science not only analyzes data faster but delivers reliable solutions. These solutions by data science in management help managers make terrific business decisions in the least possible amount of time.


  • Assessing Business Decisions

Once decisions are made, managers should examine them. By examining these decisions, managers can predict future events of those decisions. Many hypothesis testing tools are there for examining decisions after being taken.

Managers should understand how their actions affect performance and growth. If the decision negatively impacts us, we should investigate and resolve issues slowing down the process.

Managers review decisions for an appropriate action strategy using a variety of techniques. These judgments are based on

  •  Client requirements :- From the real-time data analysis reports from a set of customer purchasing and product preference behavior managers and easily plan and strategies their projects.
  •  Project executive’s requirements. :- Managers use data science to forecast future growth based on the requirements that their workforce needs. By this, they can increase performance and revenue hand-in-hand.


In this blog, we have shared how data science assists managers in business processes and decisions. By now, you would have known the data science abilities and how it helps managers in developing businesses. Every organization is taking the help of data science and AI, and managers can be more sure of the decisions they make. A job-ready data science certification course for managers can surely help you in this regard.

To get instant updates about data science and AI happenings around the world, you can follow us on  Facebook, Youtube, Linkedin, and Twitter.


Know The Top 10 Data Science Trends (2022)

Technology Has No Break- Know The Data Science Latest Trends Leading Different Industries.

Technology keeps advancing and innovating. This makes our lives better and so the business outcomes. But trends in such advancements come and go. Until you stay updated with the latest one, your career will also become obsolete over time. And if you ask ‘what’s trending now?’. The shortest and most perfect answer will be ‘Data science and AI.’But do you know in the last few years, so many data science skills and applications have become archaic? Here I will discuss the latest data science, latest trends, and the fate of different industries.

Data Science gave pathways to Deep Learning, computer vision, and Natural Language Processing. Data science even helped advance Machine Learning, a component of Artificial intelligence. These technologies are changing the way we work and live.

We are seeing new patterns emerge in the industries as organizations rely on data analytics to avoid and solve a variety of difficulties.

  • Accelerating change
  • Operationalizing business value
  • Distribution of everything

These three are the main trends that have been identified (data and insights).

We even know organizations have upgraded with time, accepting technology to increase customer satisfaction and complete their organizational goals. Data science, Artificial Intelligence, and Big Data are the leading technologies that have empowered businesses. We will tell you about the Latest Data Science trends in 2022 that you would love to know.


Top 10 Trends of Data Science in 2022


1. Augmented Data Analytics

Augmented Data Analytics is a type of data analytics that automates the analysis of massive amounts of data. Such analysis holds the blessings of NLP and advanced ML. It has eased the data scientist’s work process with real-time insights.

Augmented Data science or data analytics can merge data that is available inside an organization and the data from outside. The organization usually gets less time to process these data and extract any important insights. This data analysis provides deeper utterances and predictions by processing and preparing data and relevant visualization.

So many data analytics tools have been discovered in recent years based on business-specific visualization and explanation needs. It has been swiftly implemented in businesses, not just for data scientists but also for customers or service users. This made data analytics (Entry-level data science) and machine learning (Advanced-level data science) work together rather than operating them differently. In the next few years, you will see experts working more with Augmented analytics. Accordingly, there will be a surge in job openings in the same.

Because of the rapid evolution of this technology, augmented analytics modules are now required in all data analytics training programs, not just advanced data science courses.


2. Work more on Actionable data insights.

Data software is expensive and investing in it without any meaningful insight or evaluation is time and money-consuming. So this is where working on actionable data insight is better. Big Data integrates with the system so they can make better decisions. These insights help

  • Understand an organization’s difficulties
  • Create new opportunities
  • Study market trends.

Insights gained by actionable data can uplift an organization’s efficiency, work process, and scheduling of projects for different teams. Research by MIT stated that organizations that make data-driven decisions gain 4% in productivity and 6% in profit.


3. Data Regulation

Application of data science Data privacy and rules have been regulated, such as data ethics and trust. It has become more prominent as governments issue new rules and regulations so that AI will get reinstated with more rules and regulations. Companies have to create AI solutions according to the new regulations set by the government. But the AI regulations by the government can create barriers to international collaboration. Top leaders and the government can schedule a meeting on how to apply new regulations and how it can change the use of data. They can work together to address a common issue that it faces as a government and even the company. Data security is a very delicate issue to handle. Both the bodies can easily come up with better regulation that is not just best for data security but even for the way data can be used by organizations for their business process.


4. No code or Low code

Most organizations have integrated AI, and these organizations are using customized models. The key reason for model customization is reducing processing time. AI has initiated a lot of advancements in citizen development, anyone can become a Citizen developer, and this is because of AI with low code technologies. Citizen coders can look for problems in simple English, and AI will generate codes.

A TechRepublic poll resulted in over half of the companies have started using low-code and no-code in the operational process. 1/5th of the companies that have not started working with these trends in their system said they would do this in less than a year. So the adoption rate will gradually increase.


5. Cloud AI and Data science

In the past few years, and mainly during the Covid-19  pandemic era, there has been a massive stroll-in Cloud-based solution. As a result, data is getting produced more enormously. Collecting, arranging, labeling, formatting, and analyzing data in one platform is a huge issue, and cloud-based AI is the only way to deal with the same. As different insights indicate, the upcoming 3-5 years will be too crucial for AI and Machine learning. The cost of adopting AI has risen, and the developments of these technologies ensure cloud-based adoption in the future. So as the use of the cloud-based solution in various industries increases, the market will also expand with it. And obviously, professionals are working hard on reducing the cost of AI software development and implementation.


6. Auto-ML

The method of installing machine learning models to real-world scenarios through automation is called AUTO-ML. It automatically selects, parameterizes, and constructs machine learning models. Machine learning becomes user-friendly when automated and provides more precise results than hand-coded methods. AutoML will give access to non-experts to create and deploy models. Don’t you think this is charming information for non-techies?


7. Enhanced Natural Language Processing

The method of installing machine learning models to real-world scenarios through automation is called AUTO-ML. It automatically selects, parameterizes, and constructs machine learning models. Machine learning becomes user-friendly when automated and provides more precise results than hand-coded methods. AutoML will give access to non-experts to create and deploy models. Don’t you think this is charming information for non-techies?


8. Automated Data Cleaning

Any data that is not cleaned before analysis is useless. These useless data can be redundant, inaccurate, and duplicated without any structure and format. The data retrieval process is slowed because of these unfiltered data. This results in a loss for businesses. Many organizations are searching for automatic data cleansing to improve data analytics to acquire better insights with big data. Data cleaning needs huge system support from artificial intelligence and machine learning.


9. Blockchain

The utilization of decentralized ledgers makes managing vast amounts of data much easier. Due to the decentralized nature of the blockchain, data scientists can conduct analytics directly from their mobile devices. Because blockchain already tracks data’s origin, it’s much easier to validate the information.


9. AI as a service (AIaaS)

It is an organization that provides a unique AI solution allowing its customers to implement AI techniques at a low cost. A few months ago, OpenAI said it would make GPT-3, a transformer language model, available to the public as an API.

It refers to businesses that customize AI solutions to help clients to implement and scale AI techniques at a low cost. Recently, OpenAI announced that it would make GPT-3, its transformer language model, available as an API to the public. AIaaS is one of the best cutting-edge models provided as a service.

The Future of AIaas technology will be divided into well-defined and self-contained functions. I.e., a manufacturing business will opt for one service to build a chatbot for internal communication and another for predicting inventory processes.


What Are The Latest Trends In Data Scientist Salary?

Image by Author

Source: Career Foundry

The position of Data Scientist is one of the most coveted in today’s field of Data Science and Business Intelligence. It has been branded the “sexiest job of the twenty-first century” for all the right reasons. A data scientist can extract hidden knowledge from large amounts of raw data.

The worldwide focus has switched more and more towards data in recent years, with new-age tech sectors like Artificial Intelligence, Machine Learning, and Data Science seeing substantial growth. The need for data scientists is skyrocketing, as is their pay. Computer and information research scientists, as well as data scientists, will see a 14 percent increase in jobs through 2028, according to the Bureau of Labor Statistics (BLS).

There are various Data Scientist job openings in India, providing possibilities for data science professionals to advance in their careers and data enthusiasts who wish to enter the industry. Almost 50,000 data scientist positions are vacant in India.

The average income of a Data Scientist in India is 708,012 dollars, according to PayScale.

Data science uses technologies like big data, predictive analytics, and artificial intelligence to apply ideas in both practical and theoretical ways. We’ve covered ten of the most important data science trends for 2022 and beyond in this article. By 2027, the market for big data and data analytics is estimated to exceed $421 billion. The subject of data science is rapidly expanding, and businesses are adopting it enthusiastically in order to avoid falling behind. But I must mention this industry is making track changes also very fast. Hence, the trends we have mentioned above are not stagnant. It keeps changing over the years. Stay on track by following the updated latest trends in data science on our Facebook, Youtube, Linkedin, and Twitter pages.

How to Build a Rewarding Career As a Healthcare Data Scientist?

Data Science in Healthcare– Know The Hidden Scopes

Data science in Healthcare isn’t something new. It is the most common industry where data science and analytics are applied. The global pandemic has dramatically raised the demand and importance of healthcare data scientists. Over the years, we have seen how data science professionals have pulled together to work on Covid-19 Healthcare data and build AI/ML models to track the outbreak. This data was used for contact tracing, screening, and vaccine development. 

Thus, data science has the potential to improve the entire healthcare system.

Maybe you’ve worked in healthcare for a while and want to shift your career path to put your analytical skills to the test. Or perhaps, you have strong experience in data analysis and are seeking a field where you can put your knowledge and expertise to use. Even, the case might be that you are not happy with your current career growth in the healthcare industry and dreaming of a lucrative package like your IT friends. Believe me, that’s also possible and the key is nothing but data science and AI.

In either case, you are considering whether a career as a healthcare data scientist would be the right fit for you.

This blog will help you familiarize yourself with everything you need to know as before/ after stepping into the healthcare industry. 


  • What role does data play in the healthcare sector?

    The healthcare industry contributes a substantial amount of data to the global data pool

    Data science and AI have the potential to transform how care is delivered. 

    Today Medical Science has advanced rapidly, increasing life expectancy around the world. However, as longevity increases, the healthcare system faces a growing demand for their services, rising expenses, and a workforce struggling to meet the requirements of their patients. 

  • 5 Major Applications of Data Science in Healthcare

    Data Science has already begun to address all of these issues in order to achieve the desired result. As Data Science is now benefiting society, its applications undoubtedly will be more valuable than ever. It will propel the healthcare business forward. Doctors will have a lot of assistance, while patients will have a more personalized experience and treatments.  Let’s look at some of the essential applications in healthcare:

  • Predictive analysis: The predictive analysis model generates predictions about a patient’s status. It analyzes various correlations between symptoms, behaviors, and diseases and generates relevant predictions. It also enables healthcare to build predictive models using data science which in turn makes it possible to identify potential risks before they arrive.
  • Medical image analysis: Medical imaging is the most common application of the data science healthcare industry. Techniques like X-ray, MRI, and CT scan reveal the inner parts of the human body. With the advent of deep learning, it is now possible to identify the defects in the human body and help doctors in developing successful treatment options.
  • Drug Discovery:Today, pharmaceutical industries rely significantly on DS to provide better drugs for patients. They make use of patient information like mutation profiles and metadata to derive insights which in turn helps in the development of models.On the other hand, it is possible to improve drug discovery procedures by collecting historical data.
  • Genomics: Before the development of data analysis techniques, genomic research was a time-consuming task. But today Data science in healthcare has made it effortless and easier. Researchers now use DS to study DNA sequences to discover the link between the parameters within it and the diseases transferred by genes.

Monitoring patient health: IoT gadgets are being used by certain patients and clinicians as wearable monitors to track heartbeat and temperature. Data Science in healthcare collects data and analyzes it with the help of data science. Using analytical tools doctors can monitor a patient’s blood pressure, circadian cycle, and calorie intake.

  • What is the Role of a Data Scientist in Healthcare

Data scientists in healthcare help in exploring ways to predict drug behavior and gain a better understanding of human diseases. 

The primary role of a Healthcare data scientist is to apply all data science techniques to healthcare software and applications. They draw meaningful insights from data to make predictive models. 

In general, the core responsibilities of a healthcare data scientist are as follows:

  1. Performing data analysis with various analytical tools.
  2. Collection data/ health data. 
  3. Analyzing hospital requirements.
  4. Organizing and sorting data for use. 
  5. Implementing algorithms to extract insights.
  6. Building predictive models with the development team.
  7. Database management including data collection, retrieval, storage, and security.
  8. Converting data into easily digestible chunks for non-technical employees of an organization.
  9. Understanding hospital procedures and systems, as well as utilizing data to aid decision-making.
  10. Performing information-based audits.
  • What skills are required to become a successful data science professional in Healthcare?

The popularity of big data and its potential impact on the healthcare industry has driven the demand for more qualified data scientists. 

There isn’t a “one-size-fits-all” approach to data science. Instead, each role is unique, and because data scientists are in high demand across the healthcare industry, these jobs can require a wide range of talents. 

It is important to build a strong foundation of skills before moving forward based on your interest and strength.

  • Mathematics and statistical skills
  • Programming languages, Python and R
  • Database management languages like SQL and SAS
  • Machine learning and deep learning concepts
  • Data visualization
  • Quantitative skills and analytical skills
  • Communication and presentation skills

While these skills may help data scientists in analyzing the huge amount of data, healthcare data scientists are great at problem-solving and storytelling and are aware of their organization’s objectives. They need to discuss how to leverage data and insights with other data professionals, interact with laboratory staff and also engage with patients.


  • Healthcare data scientist and data analyst salaries: What to expect?

Today, Healthcare organizations are investing heavily in data science and analytics since it helps them in reducing administrative costs, minimizing fraudulent payments, delivering more accurate treatments and diagnostics, and overall decision making. 

Like any other industry, the salary of a healthcare data scientist is typically determined by qualification, skill set, experience, location, and organization.

On average, The annual salary of Data scientists in Healthcare and life science companies is expected to be around Rs. 40 LPA. 

Some of the popular life science companies are as follows:

Healthcare data analyst Salary:

According to Glassdoor, In India, the average income for a Healthcare Data Analyst is Rs. 7,61,298.


  • Data Science in Healthcare Projects ideas to level up your portfolio:

The majority of data science expertise is gained through data science projects which help in deeper understanding, greater retention, and awareness of real-world problems faced by data scientists. So, If you want to become a data scientist, your first step would be to learn how to work with data and then work on data science use cases.

Here are some project ideas you can work on to level-up your portfolio:

  1. Medical image segmentation: Medical image segmentation is the process of extracting areas of interest from 3D image data, such as Magnetic Resonance Imaging (MRI)  or Computerized Tomography (CT) scans. The main purpose of this project is to identify the areas of anatomy required for a particular investigation.

    Image segmentation dataset by Kaggle
  2. Ultrasound nerve segmentation: It is very crucial to accurately identify the neural structure in ultrasound images before inserting the patient’s pain catheter. In this project, you’ll learn how cutting-edge deep learning techniques are utilized to develop an end-to-end system where a person just feeds on an ultrasound image of the region to a deep learning model where it segments the nerve seen in the image.
    Ultrasound Nerve Segmentation dataset from Kaggle
  3. Heart failure prediction: Heart failure is a common consequence of cardiovascular diseases (CVDs) resulting in an increase in mortality rate. In this project, you’ll build a machine learning model that predicts mortality by heart failure. Throughout this project, you’ll learn multiple ML algorithms including Random Forest and K-NN, data wrangling, and filtering techniques.
    Heart failure prediction dataset from Kaggle
  4. Depression, anxiety, and stress prediction: Depression and stress Detection is the challenge of identifying signs of depression in individuals. This sign may be identified in several behavioral changes in a person. These symptoms can be predicted by developing a model with AI and ML algorithms such as CNN, support vector machine, KNN classifier, and linear regression.
    Depression analysis dataset by Kaggle
  5. Breast cancer prediction: Breast cancer affects approximately 12% of women worldwide and is on the verge to rise even more. This project helps doctors to predict whether a patient has breast cancer or not. You’ll be required to create an ML model to classify malignant and benign tumors by utilizing the supervised machine learning classifier technique.
    Breast cancer prediction dataset by Kaggle

The first crucial step in becoming a healthcare data scientist is earning a Bootcamp certification or a master’s degree that gives you the expertise and skills to succeed. 

Most people consider self-study or enrolling in a Bootcamp. 

Learnbay is a well-known institute for learning data science concepts. They offer affordable, flexible, and beginner-friendly Data science and AI courses in Bangalore and globally as well. You’ll gain knowledge of advanced DS and AI tools and concepts that are effectively used in the Healthcare domain. Their case studies and industry projects in healthcare will help you stand out from the competition.



By now, you’ve seen that DS has revolutionized the healthcare industry in large ways and how Healthcare and pharmaceutical industries have heavily utilized data science and AI to improve patient lifestyles. I hope this blog helped you understand the value of domain expertise in healthcare.

Also, Keep in mind that the pharma and healthcare industry will never be redundant since it is an integral part of human life. Hence, this is the perfect time for you to make that career move you have always wanted. 

Wake up and begin your journey in data science and AI from Learnbay institute! 

For more such content, do check out our site: Learnbay

You can subscribe to our social media channels to get regular Data science and AI updates. 




How Data Analytics Can Fast Track Your E-commerce, Retail, and Supply Chain Career?

What Role Does E-Commerce Play in the Post-Pandemic Retail Future?


Today’s retail data is exploding at a tremendous speed. Retailers are relying on data analysis, to turn insights into profitable margins by developing data-driven plans.  Owing to the growing volume of data, data scientists are higher in demand.

Some employees working in the e-commerce and retail industries are quite dissatisfied with their jobs. And wish to shift their profession without changing their domain. If you’re one of them, then you’re in the right place. If you love working with data and have some technical abilities, then Data science can be the ideal choice for your career. 

In this article, we will look at the impact of Data Science and Artificial Intelligence in the retail and e-commerce industry, the challenges that come while implementing it, career scopes, and how you can get started as a data science professional in the same. 

People are still changing how they shop in early 2021, according to a survey from EY, which has been polling customers since the epidemic started. That’s about 80% of the people (Digital Library). 60 percent of people no longer go to stores in person, and 43 percent are shopping more online for things they would have bought in stores before the pandemic. In Covid-19, many people don’t care where they are as long as they can connect to the web. People spent about $10 billion on e-commerce investments, acquisitions, and partnerships from May to July 2020 (by Kathy Gramling). This is about how much money they spent. A lot of money was spent on logistics to make last-mile options like ghost kitchens and shadow storefronts possible. There was also a lot of money spent on AI and blockchain to make more things. Let us discuss data science in e-commerce, retail, and supply chain domain.

But do you know even after such massive demand so many retail and e-comm employees are losing their jobs?

20000 jobs were laid off in retail sector due to the 2020 pandemic

                                                                               Source: Author


On the other hand, there is also an intelligent community of professionals to reach the top of success. And you can also be a part of that community. To know how, please continue reading this blog. 

Data Science in E-commerce Retail and Supply Chain Domain

                                                                            Image by Author

The final mile is crucial to e-commerce success: 21% said they would not forgive stores and brands if service was delayed because of Covid-19. It’s getting harder and harder for businesses to get last-mile delivery capacity because more people are shopping online. After Black Friday in 2020, many of us had to wait weeks for things to show up on our doorsteps. Delivery is now an important part of the whole experience. As a fulfillment center, the shop is used a lot. According to the Index, 37% of US customers plan to purchase online and pick up in-store more often in the future (online library). While using a shop as a fulfillment center may be a good idea, it needs systems and business divisions to work together to make the promise come true. Retailers’ ability to create a consistent experience must expand as services grow.

Retailers need to be ready to build better, deeper relationships with their customers, both online and in-person, no matter how people act.

For Retailers, the Future of E-Commerce is Bright

  • Emerging Markets Will Be Critical

In the future of eCommerce, India, China, Brazil, Russia, and South Africa are projected to play a key role. This may not be a surprise, but let’s look a little deeper into this. By 2022, it is expected that about 3 billion people from developing countries will be able to use the internet. That’s a lot of people who could be customers. There’s also a good chance that people who already live in these areas will make up 20% of total retail sales in 2022. A lot of people could buy this.

  • The Online vs. Physical Debate

It’s not possible to talk about the future of e-commerce without talking about the conflict between physical and online shopping. In the long run, people buy things online more than they buy things in stores. It doesn’t mean that physical stores aren’t very important for internet businesses at all. People think brick-and-mortar stores aren’t as important anymore because they don’t have as many things as their online businesses, which usually have a lot more. Take a look at Nike, which has already opened stores in both New York and Shanghai. They’re called “Houses of Innovation,” or “Experiential Shops.” Overall, we believe that unique experiences will be the future of physical retail sites. These are once-in-a-lifetime events that cannot be duplicated.

For Marketers, the Future of Ecommerce

  • The importance of device use will increase.

If you want to buy something from an e-commerce site, you usually have to use a computer to do it. It’s now on the other side. If you work for an eCommerce Data Science company, you have to make your website for mobile users before you make it for people who use their computers. This may seem to be an unusual shift, but it makes sense, particularly when you realize that 45 percent of all commerce choices were made on mobile devices last year. For comparison, it translates to $284 billion in sales. Buyers now want a seamless purchasing experience across all devices.

  • Video is becoming more popular.

In the future, the video will play a big role in e-commerce. E-commerce businesses will need to improve their videography skills. Research says that 60% of people would rather watch a video about a product than read about it in a text. After watching a brand’s social videos, 64% of people buy something. Facebook, Instagram, and Snapchat may be to blame for these changes in buying habits. All of these apps have made changes that make video content more important.

How is data science affecting the retail industry?

Data science is changing how people shop and how businesses order and ship things, say some retailers who are going in a different direction. Businesses can buy and ship things more cheaply because they don’t have to pay a lot for them. A lot of people have better experiences because of it. In the future, some algorithms can help retailers learn more about their customers and figure out how many people will buy in the future, too. It all helps the bottom line.

A Data Scientist’s Role in the Retail Industry

Every year, the number, diversity, and usefulness of retail data increase dramatically. When retailers make decisions based on data, they use data science to make money. This is how businesses are using data science in retail to stay competitive, improve customer service, and make more money and sales. And, as technology advances, data science in the retail business will have much more to give!

Data Science in E-commerce Retail and Supply Chain Domain                                                                             Image by Author

What Role Does Data Science Play in eCommerce?

  • Customer Lifetime Value:

It is a prediction of how much money a single customer will make for a company over time. It is based on what the shopper has bought and done on a certain eCommerce site in the past.

  • Customer service has improved:

Customer service is crucial for every eCommerce company owner. Business owners can use data science to make their websites better by getting feedback from people who use them and giving them stars and reviews. To figure out why people didn’t like them in the first place, you can sort them and do a Sentiment Analysis to figure out how they felt. E-commerce businesses can quickly look through all of the reviews and focus on improving and increasing customer happiness, with the issues raised by angry customers getting the most attention. This makes it easy for businesses.

  • Predictive Analytics:

If you run an eCommerce site, you need to be able to figure out what people want before they do. This means that each person who goes to the site does things differently. They also have different preferences. Use Predictive Analytics to see everything about how customers use the site and what they buy. This makes it easier for them to make decisions. Consequently, Data Science e-commerce businesses may be able to better serve their customers and set a price range for their items.

The benefits of using and analyzing data science in eCommerce are endless, and understanding how customers use and interact with your website is critical to your success, so don’t forget to use it. If you want better customer service and a more personalized experience, you’ll need to get more information from people. You can also make more money, improve the prices of your products, and decide where to open a new store.

Data Scientists Who specialize in Supply Chain Data

This means that more and more businesses see the benefits of using data science to manage their supply chains. This means that there is a growing need for data scientists who are qualified. Companies are paying data scientists a lot of money because there is a lot of demand for their services. It says that data scientists in the United States make an average of between $105,750 and $180,250 per year. Earnings are affected by factors like where you live, how much experience you have, and what kind of business you work in. According to statistics from other organizations, supply chain data scientists make an average of $82,100 per year, with some making as much as $156,000.

Supply Chain Management Using Data Science

  • Overall, this is a great time for supply chain experts and data scientists to work on important academic research and come up with ideas and solutions that will have a long-term impact on the world.
  • Employers are looking for skilled data scientists who can apply their knowledge to the problems their companies are having with their supply chains, as well as to academic research in the field.
  • One of the best ways to get the skills you need to become a data scientist or start a new job is to get more education, like Learnbay’s data science course.
  • Students learn how to process, model, evaluate, and draw conclusions from data through these programs, which will help them when they start their businesses in the future.

What do Supply Chain Data Scientists get paid?

People who work in Supply Chain data science make on average 14.3 lakhs a year, according to the 56 profiles. They make between 5.0 lakhs and 28.2 lakhs per year. Those in the top 10% earn more than £18.4 lakhs a year.


Data Science in E-commerce Retail and Supply Chain Domain                                           

                                          Why are Data Scientists getting paid at a higher level? 

                                                          Image Source: Supply Chain 24/7


Packages and Companies:

Data Science in E-commerce Retail and Supply Chain Domain                                                                             Image by Author

                                                                            Source: Linkedin

  • Amazon: Rs 5 lakh to Rs 45.57 lakh | Rs 15.56 lakh (average)  
  • Flipkart: Rs 14.5 lakh to Rs 42 lakh | Rs 24.2 lakh (average)
  • Walmart: Rs 14.5 lakh to Rs 33.5 lakh | Rs 24.6 lakh (average) 
  • IBM: Rs 1 lakh to Rs 44.62 lakh | Rs 10.91 lakh (average)
  • Deloitte: Rs 5.52 lakh to Rs 27 lakh | Rs 12.41 lakh (average)

What Qualifications/Skills do you need to work as a Supply Chain Data Scientist?

  • A bachelor’s degree in engineering, computer science, applied math, statistics, or a quantitative field is needed to work in this field. It is better if you have a master’s or certified degree than not.
  • A minimum of three to five years of experience using Data Science, Machine Learning, or AI to solve Supply Chain or Manufacturing problems is needed.
  • Supply Chain, Manufacturing, Warehousing, Distribution, and Logistics domain knowledge and familiarity.
  • Python experience creating and implementing machine learning and artificial intelligence algorithms.
  • Common statistical and Data Science packages and libraries as well as optimization tools are well known to him.
  • Advanced statistical methods and ideas are needed to do this (regression, decision trees, ensemble models, time series, forecasting, neural networks, network routing, linear programming, and optimization).
  • Expertise in SQL and experience with relational and non-relational databases, SQL query writing tools, and SQL debugging skills are needed.
  • Ability to operate in a fast-paced, quickly growing start-up environment.

What Are The Responsibilities of a Data Scientist in the Supply Chain?

  • To solve problems in Supply Chain, Manufacturing, Inventory Management, and Distribution, design, build and test machine learning models and algorithms.
  • Build features and functionality for ThroughPut’s ELI Flow platform with help from Product and Engineering.
  • Collaborate with Dev Ops and Quality Assurance to put models into a production environment that can grow with the business.
  • Participate in client-facing Sales Engineering conversations and help with data-related analysis and troubleshooting.
  • People who work in data science, machine learning, artificial intelligence, and supply chain management should stay up to date on the most recent tools and methods. They should also come up with new, unique solutions.

Data Science in E-commerce Retail and Supply Chain Domain

You may be wondering how Learnbay can help you with specializations like retail, eCommerce, and supply chain domains after reading all of the above.

It’s all about domain specializations at Learnbay, and one of them is Retail, Ecommerce, and Supply Chain.

Data Science in E-commerce Retail and Supply Chain Domain

                                                                             Image by Author

Let’s take a look at what you’ll receive if you study with Learnbay:

Learnbay is noted for its wide range of data scientific subjects. This is why it offers some of the top data science courses in Bangalore. But the best thing about it is that it has hybrid learning and IBM-approved courses, so you can take lessons both online and offline.

So, let’s have a look at what Learnbay’s Supply Chain domain has to offer.

  • This class is an option. It teaches students how to look at data and draw important conclusions that could help businesses get a better edge in the market.
  • There are many examples of the RSCA process. Sentiment Analysis is one of them. Google Analytics is another. Natural Language Processing, Recommendation Systems, Deep Learning Concepts, and Text Analysis are also examples. Operations Research is used in supply chain management in a separate class.
  • The Supply Chain Operation Reference (SCOR) framework also has models and metrics like ROE, ROA, APT, INVT, and PPET. These models and metrics are part of the framework, as well.
  • Simulators and time series forecasting are also important in supply chain management, and the people who come to the meeting will like that.
  • The purpose of this E-Commerce, Retail, and Supply Chain curriculum is to introduce participants to the fundamentals, components, business models, and other aspects of running an electronic commerce firm.
  • You will have a better grasp of the issue than anybody else in your firm if you have domain expertise. 
  • Learn the finest practices in your respective professions and become well-versed in them. Be mindful of potential problems that you and your firm may face in the future. Most importantly, a well-known Domain Specialist increases the market value of a firm.

Projects in the Retail, eCommerce, and Supply Chain Domain in which you will be working:

Retail Domain

  • Usage-based warranty analytics: Next, after you figure out how many items you need, it’s important to figure out the right reorder level. This will make sure that production doesn’t stop because there aren’t enough items in stock and that working capital doesn’t run out because of inaccurate orders.
  • Customer Sentiment Analysis: It is the most important part of sentiment analysis to look at data from inside a text to get a sense of the point of view and other important characteristics, like modality and mood.
  • Optimization of the price: The optimization methods have a big advantage when it comes to finding the best price for both the customer and the retailer.

E-Commerce Domain

  • Fraud Detection: Fraud in the e-commerce business is one of the most difficult to find because it can cost a lot of money.
  • Recommendation System: This technology aids firms in anticipating customer behavior.

E-Commerce Domain

                                                              Dataset for eCommerce Customers

                                                                   Image Source: Kaggle Dataset

Supply Chain Domain

  • Algorithm for routing the transportation network: This is because shipping costs have gone up recently because there aren’t enough containers to go around. Container loading optimization is now very important.
  • Identification of the Reorder Level: Next, after you figure out how many items you need, it’s important to figure out the right reorder level. This will make sure that production doesn’t stop because there aren’t enough items in stock and that working capital doesn’t run out because of inaccurate orders.
  • Planning a network: To have a strong supply chain and a profitable business, you need to make sure that all of your inventory and production facilities are properly connected.

Now that we’re done with the article on data science in e-commerce, retail, and supply chain domain, I hope it has helped you understand how important it is to know your field. Another point we wanted to emphasize was the possibility of this in the future, as well as in the present. Take a look at the Data Science & AI Certification| Domain Specialization For Professionals course to learn more about the Data Science course or visit Learnbay’s Linkedin, Twitter, Facebook accounts for updates. 










Banking, Finance, Services & Insurance Sector: Know How to Achieve The Most Lucrative Salary Package

Introduction to Banking, Financial Services, and Insurance

The BFSI  industry is witnessing a major transformation in the Indian economy, fueled by new FinTech competition, shifting business models, compliance demands, customer experience enhancement, and innovative technologies.

However, in 2020, this scenario changed due to an unprecedented event that shook the entire world, the BFSI sector was heavily hit like any other industry resulting in layoffs and halting of employment. 

 30000 jobs laid off in India due to 2020 Pandemic

Nevertheless, as the lockdown has been lifted and the world learned to live with this normalcy, the hiring trends in the BFSI sector are beginning to shine again. 

A report by the National skill development corporation (NSDC) reveals that in India, Banking and financial services need 1.6 million skilled workforces by 2022. 

Therefore, this can be the right time for you to get back on track and secure your career.

But what can be the best option for you? Data Science

In today’s world, Data science plays a major role in the BFSI sector. They help in analyzing data to improve the overall customer experience. 


Data science and AI can be the finest option to land a high-paying job in the BFSI sector. 

Throughout this blog, you’ll get an idea of how data science is influencing the financial industry and how it can help secure your career. Let us discuss data science in banking, finance, services, and insurance sector.


Financial Services are in high competition now. Even entrepreneurs are targeting this industry. As per the Goldman Sachs insight, more than 4.7 trillion dollar revenue might get directed to such startups from the traditional financial MNCs (Source: Global Hitachi).  Apart from that, the massive changes in regulatory compliance changes (such as the Dodd-Frank Act, ALLL of US ) are also making it the banking business harder to maintain profit. This is not the end the applications of Robo-advisory and algorithmic trading are making The competition is becoming harder day by day.

Indian Banks are also facing lots of stress due to several types of debt. In July 2021, SBI indicated highly increased stress from holding debt (due to the COVID-19 outbreak). In 2019-20, the Indian government opted for 10 public sector bank amalgamation to lower the number to 4. The reason behind this was to lower the debt risk and better financial operation. But, the situation is going in such a way that it might be possible, for private banks also face a similar amalgamation- this may lead to severe layoffs.

Until now, whatever disasters financial companies have faced everything got saved by proper implementation of data analytics and AI innovation. J.P Morgan, Accenture, Goldman-Sachs are the lightning examples in such cases. 

But the risk of layoffs in the BFSI sector can be easily overcome by adapting the DS and Ai skills- The sector is in massive need of such talents.

A lot of people who work for IT companies, BPO companies, or technical and professional service businesses use this word. It stands for “Banking, Financial Services, and Insurance.” They do data processing or test software applications or write software for this kind of business. This is because people have more money, the banking, financial services, and insurance (BFSI) industry in India are expected to develop dramatically. In the last 15 years, the BFSI sector has undergone a lot of changes, and it will be a big part of India’s economic development based on inclusive growth. 

Banking sector

                                                                       Source: By the Author

Possibilities and Challenges: 

  • The BFSI business is expected to grow a lot in the future as India’s economy grows and people become more aware of financial goods and services.
  • New and broader items will provide a plethora of options for specialized development.
  • When it comes to these kinds of computer systems, RSM is well-equipped to offer a wide range of services. This is why the business world now sees IT as an important part of its strategy.
  • People who have a lot of rules and regulations will need to be aware all the time and use a lot of risk-reduction strategies all of the time.

Data Science in Banking, Finance, Services & Insurance Sector

People have strived to handle money effectively since the invention of money. Temples were used as banks by the ancient Greeks and Romans. This was partly due to the temples’ ability to keep people’s hard-earned money secure. Money storage became insufficient after a while. Banks were expected to provide more to their customers. As a result, the financial business has grown significantly. The financial business began to expand in leaps and bounds. The banking and insurance industry has changed from being a business that cares about people to one that cares about big profits. The financial industry’s watchwords quickly became revenue and profit. They found that their customers were smarter than they thought. These people, too, wanted to beat the banks and other financial institutions at every turn. In order to stop the money from leaving, banks used historical data analysis to look for common trends from the past. This way, they could stop the money from going out of the door. This was most likely the start of data science. This project quickly evolved into a potential source of employment. 

Data science is a nebulous subset of computer science that has piqued the interest of many experts seeking new prospects. Finance is a manifestation of data at several levels in and of itself. Only that, this is financial data, which is critical for financial firms. History shows that data science was used before it became a separate field of computer science, as shown in the short history above. Decisions are being made based on data because there is so much information out there now. To make things even faster for banks and other financial institutions, they can now quickly look at a lot of customer data like their personal and security information a lot more quickly.

Application of Data science in Finance and Insurance Domain

                                                                          Source: By the Author

How did data science help the Banking, Financial Services, and Insurance businesses handle problems?

In the banking industry, data science is being used in a variety of ways.

  • Fraud detection: 

Because fraud can happen in a lot of different places, it’s important to find and stop it with the help of data science. A bank needs to be able to spot fraud before it happens, which is very important for the safety of both its customers and employees. The sooner a bank finds out about fraud, the sooner it can stop account activity and cut down on losses. When banks use a variety of fraud detection methods, they can get the protection they need and avoid a lot of money being lost. People do this for things like getting data samples for a model estimate and testing, as well as for things like model estimation.

  • Lifetime value prediction:

Client lifetime value (CLV) is a prediction of how much value a company will get from a customer over time. This is because these numbers help build and keep good relationships with specific clients, which helps the company make even more money and grow even faster than before. Banks are having a hard time getting and keeping customers who are worth their while. In order to spend money wisely, banks must now have a 360-degree view of each customer. This is because the competition is getting tougher. In this case, the data science field comes into play. Data about how many customers are added and how long they stay must be looked at first. Banking products and services that people use, how much money they make and where it comes from, and how many customers come from certain places all play a role in how people use banking services.

  • Customer segmentation:

Segmenting people into groups based on how they act or look is called customer segmentation. It’s important for data scientists to know how much each customer group is worth. Some of the tools they use to figure this out include clustering, decision trees, logistic regression, and more. These tools help them figure out which groups have the most and least value. Making groups of customers makes it easier to allocate marketing resources and make the point-based approach, as well as selling chances for each group of customers, the best they can be for each group customers. No one needs to see this. Remember that customer segmentation is intended to enhance customer service and aid in customer loyalty and retention, which is critical in the banking industry.

Data Science Applications in the Financial Services Industry:

  • Algorithmic trading:

An algorithm helps financial companies quickly make smart decisions based on the most up-to-date data because they can do this right away. People who trade this way look at both traditional and non-traditional data when they make their trades. Good at this kind of work needs to be able to quickly look at this data because it’s only useful for a short time. When real-time and predictive analytics are used together in this field, there is a new way to look at things. There used to be a lot of mathematicians who worked for financial companies, but that has changed. To make trading algorithms that could predict what would happen in the market, they made statistical models and used data that had already been collected. People who do data science now have tools that can help speed up and improve getting data.

  • Robo-advisory: 

A lot of people in the world of finance are using Robo-advisors all the time. In the app, people can write down how much money they have and what they want to do with their money. For example, they can write down how much they want to save by the age of 50. A robot adviser is then used to put the person’s current assets into different investment options based on their risk preferences and what they want to do with the money. Insurance is something that people buy online from a lot of companies that use robots to help them make unique insurance policies for each customer. It’s cheaper to hire a robot financial adviser because they can give personalized and calibrated advice that’s tailored to each person’s needs.

Learnbay Robo advisory

                                                                        Source: By the Author

In the insurance industry, data science is being used in a variety of ways.

  • Underwriting and credit scoring:

The Top Data science field is good at things like underwriting and credit scoring, which happen a lot in finance and insurance. There are tones of consumer profiles that data scientists use to train their models. Each one has a lot of data points. In real life, a well-trained algorithm can do the same job as an underwriter and credit scorer. Human workers may work considerably quicker and more precisely with the aid of such scoring algorithms.

  • Insurance for automobiles:

Wireless “telematics” devices could be used to send real-time driving data to an insurance company. Imagine a room full of car insurance agents drooling over their desks. Progressive introduced telematics-based insurance in 1998, and it has been around since then. But, in the intervening years, technology has advanced significantly.

  • Personalized marketing:

Personalized marketing is not an anomaly in the insurance sector. Insurers must ensure a digital connection with their clients to satisfy these needs. Data science jobs and advanced analytics use a lot of demographic data, preferences, interactions, behavior, attitude, lifestyle information, interests, hobbies, and other things to make insurance more personalized and relevant for each person. This makes insurance more personalized and relevant for each person.

Banking, Finance, Services & Insurance Domain Modules

BFSI will assist you:

  • Learn how to use modern tools and technology, as well as established methods, to win in an increasingly competitive industry.
  • Master data analysis and design a dynamic dashboard to summarize your findings.
  • A better leader can learn more about data and make smarter decisions about who to target, what to sell, and what to do in the market. This can help both you and your team.

What is the Data Science team’s role in the banking business?

The people who work in data science are very important. They can gather, summarize, and predict fraudulent activity in customer databases, which makes them very important people. Before data science and big data, it was impossible to look at customer records and come up with reliable data. Artificial intelligence (AI) and machine learning may assist banks in combating fraud.

Is Data science beneficial to finance?

People in the finance industry use data science a lot to manage risks and make decisions. This is called “data science.” In the end, businesses that deal with money make more money when people do more research. Businesses also use business intelligence tools to look at data trends.

What are the Modules for Banking, Financial Services, and Insurance training?

  1. Data Science in Banking, Finance, Services and Insurance Sector is introduced.
  2. Institutions of Finance and the Services They Provide
  3. How can financial institutions create profits?
  4. Customer data management, customer segmentation, and real-time and predictive analytics are just a few of the services that can be used to improve your business.
  5. Security, Process Automation
  6. For investment banks, fraud detection, underwriting and credit rating, and risk modeling are all important things to keep an eye out for.

Benefits of DS in Banking, Finance, Services & Insurance Domain

The most important benefits that data science certifications have had for the BFSI business as a whole should be talked about. These small changes have had a big impact on people’s lives, especially how they work.

  • Financial trend forecasting:

When businesses want to make good decisions about their goods and services, they need to know how much demand there will be for them and how much supply there will be. This is called forecasting. It also helps them tell their customers how to make smart financial decisions by using predictive models.

  • Automating tasks: 

Making tasks easier for financial services analysts, managers, and their coworkers to do makes them more productive and makes it easier for them to do their jobs. Online apps and algorithms make it much easier to figure out whether or not a customer is a financial drain. People who work at a bank can quickly figure out if they should give that personal service or not. A lot of people also like that they don’t have to go into a bank anymore to apply for things and services. Also, they may be able to fill out most of their applications online at home if their browser is set up to remember things like their address, phone number, and name when they come back to it. The more automation makes it easier for people to interact with businesses, the more happy people will be with it. Their productivity also goes up.

  • Assessing risk: 

Using a person’s credit score and financial activities, It is very easy for data science algorithms to figure out if a person or group is a bad investment. This will determine whether or not this person or company can get a loan, or if they should be turned down because of their bad credit history.

  • Fostering inclusivity: 

There can’t be any exceptions to this rule when financial companies use algorithms. They must treat everyone the same no matter what their ethnicity or sexual orientation is. This is because the whole decision-making process is based on what the customer does with their money. As a result, customers will be able to see more clearly how they can get the things they want. There is also no discrimination, which could happen in more subjective applications. This is because it doesn’t allow for that.

Banking, Finance, Services, and Insurance Capstone Projects

  • Prediction of Loan Default.
  • Fraudulent credit card transactions should be identified.
  • Prediction of Claims.
  • Estimating Insurance Premiums.
  • Risk Analysis in the Financial Industry.
  • Algorithmic Trading.

Scope of Banking, Financial Services, and Insurance in India

NSDC did a study and found that India is one of the few countries that have a strong foundation for high productivity and global integration in recent years. It’s important to note that two main things are at play during the digital transformation of the BFSI: These are digitization and the digitalization of things, and they are both very important. Learn about and test new technologies and business processes that could make your BFSI service better with these new tools, like:

  • Partnerships between payment banks and fintech firms.
  • Artificial Intelligence and Cognitive Analytics
  • Blockchain.
  • Automation of Robotic Processes.
  • Cybersecurity is an important topic.

Even though digitization promises more security and cost savings, its real value comes from giving people what they want. However, with the introduction of new fields like services and insurance in India and business consulting, banking has become one of the most popular jobs in India. It’s a big problem for the industry because the Indian government is building new offices to bring banking to more rural areas. It is also seen as a socially acceptable and stable occupation.

Companies in the banking, finance, services, and insurance sectors in India in 2021:

Package offered by companies                                                                             Image by Author

                                                                            Source: Glassdoor

Bajaj Finance Ltd: It focuses on consumer loans, small- and medium-sized business loans, and commercial loans, as well as many other types of loans. Several things are important to the company: fixed deposits and rural loans. Value-added services are also important.

Muthoot Finance Ltd: Finance and making electricity are two parts of Muthoot Finance. When it doesn’t have formal credit for a long time, it gives out personal and business loans to people who need short-term cash.

Tata Capital Financial Services ltd: If you want to buy something for yourself, your business, or the city itself, there are a lot of financial services that can help you. They come from here all the time. A lot of different things it does: managing wealth, home loans, and infrastructure management are just a few of them.

L & T Finance Holdings Ltd: As you can see, there are many different businesses that it does, such as information technology and financial services. They also build and make products, and so on. The company sells power and electrical equipment, as well as ships and heavy equipment. You can buy these things from the company. Also, people can buy other things from them.

Aditya Birla Finance Ltd.: There are a lot of different things it can help with. It can help with commercial mortgages, corporate finance, and more.

There are more and more of these businesses in the BFSI industry. If you want to work in the banking or financial sector, you need to learn about Data Science. There has been a huge rise in the amount of data that needs to be analyzed and used in this field.

Banking, Finance, Services, and Insurance job positions:

  • Agents in the insurance industry.
  • Sales representative for banks and financial products.
  • Sales representative for equity products.
  • Representatives of investment firms.
  • Stockbrokers.

Required abilities: 

In this field, there are a lot of different skills that are needed to get a job. Some of the most common are sales skills, math skills, knowledge of the stock market and mutual funds, and knowledge of how banks work.

Salary/Remuneration Package in Banking, Finance, Services, and Insurance

Those with one year or more of experience can expect to earn 4,62,321 per year. A seasoned expert may also receive a variety of incentives, such as a 7-30% share of revenue, based on the work level completed.


Annual Salary                                                                          Source: CollegeDunia

Banking, Finance, Services, and Insurance Course:

You should enroll at Learnbay institute, if you want to pursue a profession in the Banking, Finance, and Service Insurance area. It gives you a certificate that is recognized around the world. This will help you get more attention and make you stand out from the rest of the people. You’ll also be able to get live interactive sessions so that you can ask questions. Learnbay’s BFSI course includes Project Life Cycle Expertise, as well as two capstone projects and the opportunity to work on real-world projects. By visiting the Learnbay institute, you can learn more about the domain. Learnbay provides one of the greatest data science courses in Bangalore, and I definitely suggest it.

Prerequisites for BFSI:

Course Professionals with 1+ years of expertise in the BFSI area are required. Non-BFSI professionals who want to learn about the most up-to-date technology, data science, artificial intelligence, data analyst, and business analyst methodologies that drive strategic development can learn through Learnbay’s Facebook, Youtube, Linkedin, Twitter handles.


  • Bibliography:



Marketing, Sales, and HR: Is being a data scientist the only hope?

Sales, marketing, and HR have been among the most profitable industries in the 21st century. But there have been some hidden downfalls that you may not be aware of.

On the other hand, The covid-19 pandemic has heavily disrupted marketing resulting in the layoff of many employees.

960 roles in sales and talent a

                                                                              Source: Author

Due to this most of the sales and marketing professionals are struggling and freshers are confused at the same time. So, are the sales and marketing careers approaching a dead end!!

Obviously no. 

There’s no need to be concerned as the saying goes, there’s always a solution for every problem. 

Starting your career in Data Science and AI might be your one-stop solution to begin your career for breaking into the marketing and sales industry. 

Data science is the newest craze, and it’s swept the marketing world as well.

This blog will help you in taking the necessary steps toward launching a career in the same. 


First, let’s have a look at a few cases. One prime example is how Coca-Cola lost its market to Pepsi. It was one of the biggest sales disasters of all time. Coke even tried changing its formula but still couldn’t up to its game. This shows the tough and competitive nature of the industry that can cause people to change their opinion about the industry on the whole and not good opinions at that. 


But if you think that the competition was only between two separate companies, I suppose you might be wrong. Competition can exist within the same company as well. For example, Ford came out with Ford EDSEL, a new car performing great in the market. So what was the problem? It came during the economic recession. The new car was much more expensive than ford’s previous models in the mercury line without offering anything new or revolutionary; therefore, it started to die down. 

Data science in sales, marketing and HR

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Especially after the pandemic, faulty marketing strategies caused a lot of small businesses and even bigger chains to close down because they did no good to their business. This resulted in the unemployment of many people, and some left their jobs without having a fallback plan. So what is the solution to survive in this industry? Data Science! Applying DS techniques to sales and marketing can be a game-changer. 

Becoming a data scientist is never a bad idea. It is very in the now and is considered to be the sexiest job of the 21st century by the Harvard Business School. DS is a very lucrative subject no matter in which domain you apply it to. Data Science Courses fare well in the market owing to their importance in the coming times where every single thing will be driven by data.


Still not convinced? Let’s see why DS is necessary for the sales and marketing sector.


How is Data Science Used in Sales and Marketing?

Data science is the key to transforming multi-source data into actionable insights that improve the fundamental content. By gaining more data-backed insight, companies can transform their business strategies to maximize their market value.

McKinsey reports that 72 percent of fastest-growing B2Bs say their analytics help them plan sales, compared to half of those who are slowest growing. Their analytics are highly effective, they claim. Data science can be used in many aspects so that repetition is not a problem in the sales sector.


1. Analysis of customer sentiment

Customer emotional analysis can be used to extract emotions from communication. This allows us to understand emotions and use this understanding in our business. The algorithms are used to analyze sentiment. They can be used to assess the general attitude towards texts on social media, blogs, and review sites for text mining. With just a click, automated sentiment analysis techniques allow real-time insight. These tools highlight the subtext of comments, taking facts, emotions, and general views into account. These emotions can also be broadened beyond the general classification of positive, negative, or neutral observations.

growth by sales and marketing

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2.  Maximization of customer lifetime value (CLV)

Intelligent enterprise decisions are made based on the value of customer relationships. CLV is a measure of a customer’s profit over the entire term of their brand relationship. The lifetime value of your customers will give you a good idea of the future perspectives of your company.


There are many sub-matrics that can be used to measure these metrics: gross margin, frequency, order value, and so on. These metrics are used here. Intelligent algorithms are able to monitor, compare, and calculate any changes in data. You will maximize the lifetime value of your client with all these measures.


Here you will find customized recommendations, newsletter campaigns, and client loyalty programs. It would be best if you increased the measurements. These steps are easy: Take a few measurements, compare them, then determine the weakest metrics and then repeat.


3. Future sales prediction

Specific data is required for the prediction model. This data includes the number and type of customers acquired, lost clients, average sales volume, seasonal trends, as well as season trends. It is important to know your sales expectations – as changing conditions can dramatically affect sales – before you make any decisions.

These data are used to search for patterns in sales forecast systems. These patterns are used to determine the general trends in the pipeline to make forecasts more precise.


4. Churn Prevention

Sales professionals are now able to anticipate when clients will purchase their next product. It is also possible to predict when consumers will stop buying. Customer churn is the percentage of customers that have stopped using the product or bought it again. Machine learning algorithms can be used to identify patterns and features in customer behavior, communication, order, and order.


5. Inventory Management

Effective inventory management is essential for retailers to ensure that sales rise but supply remains stable. To achieve this, supply chains and inventory chains must be thoroughly examined. Machine learning algorithms can analyze and provide detailed supply data and identify patterns and correlations. An analyst then evaluates this data and provides a strategy to increase revenue, timely delivery, and inventory management.


6. Cross-sell recommendations

All companies use cross-sales to increase their revenue. For clients who wish to buy over-the-counter, offering complementary products is a good idea. Buyers have the option to buy a product that is superior to what they are used to when upselling.


The algorithm analyzes transaction sales data to determine if the products were purchased together. Therefore, data science’s role is to provide transaction and CRM data along with factual advice. These algorithms help to decide which products can be promoted or put in the catalog.


7. Merchandising

Rotating goods allow customers to retain their products’ freshness and quality, while appealing packaging and branding attract attention.


Marketing algorithms include data sets to gather insights and create priority customer sets that account for seasonality, relevance, and trends.


8. Optimizing the price

To optimize price, models can be used to analyze how demand changes with inventory costs and manufacturing costs to determine the best price at different price levels. These models can also be used to adjust prices for particular customer segments. Client satisfaction is directly affected by price optimization.


9. Chatbots – salespeople

Sales data science is best applied to bots, not salespeople. Chatbots automate consumer interactions and save time-solving problems. Modern chatbots can interpret customer messages using sentiment analysis algorithms.


Chatbots can also send hundreds of messages per second simultaneously. The selling bots are extremely efficient. Chatbots can offer better customer service in certain situations. They are able to process requests instantly. A bot can save you money.


10. Augmented Reality Implementation

Augmented reality provides a great outlook on sales implementation. Augmented reality is a way to give clients a more realistic buying experience, especially for online retailers.


The first use of virtual reality is to enhance product and shelf navigation in shops and online. Virtual fitting rooms are also available. Customers have the opportunity to meet with the product, which increases their chances of purchasing it.


MNCs Currently Using Data Science



Airbnb is a great example of data science applied to marketing. They hired a data scientist right from the beginning. There were seven people on the team at the time. Since the founder recognized the potential of data science to accelerate company growth, it has been a priority. This means that all levels have been adequately explored, and problems as well as opportunities.



Keeping its subscribers coming back is one of the top priorities for a content subscription service like Netflix. Netflix’s recommendation engine is designed to serve exactly this purpose. It recommends films and series based on the viewing history of similar users. Although the initial effect on the user is positive, enriching, and helpful, the ultimate goal of Netflix’s recommendation engine is to keep them subscribed month after month.



Spotify is similar to Netflix in that it aims to keep its subscribers happy by offering new and interesting ways for them to discover music. There is a significant difference between Spotify and Netflix in the amount of content they offer – which is necessary considering the differences in content types.



Meta, previously known as Facebook data science is multi-layered. They not only have their own insights to analyze and take action but also offer marketing tools and insights for the thousands of businesses that market through their platform. It is essential that they have effective strategies that work for customers.



Google, like Facebook, aims to provide a high return on investment for its business-owning customers. Google will provide these services for small businesses that do not have an in-house data scientist. It is our goal to make data and analytics as easy and intuitive as possible.


What is the advantage of using Data Science in Business?

There are many perks to using data science in the sales and marketing industry. Some of them are listed below.

  • Spend less time and money trying out marketing strategies that fail
  • Only target the most valuable customers
  • Increase the lifetime value of a customer
  • Learn quickly from customer feedback
  • You can predict which products or services will be most popular in the future
  • Refine your digital advertising
  • Convert more leads with cross- or up-selling
  • Increased Security.
  • Allows companies to calculate the Return on Investment or ROI of a marketing campaign
  • Information for marketing departments about which marketing strategies are effective
  • It can give a picture of target consumers. It can give a company a picture of its target consumers.

Now that we have covered the whys and hows, let’s see where and what kind of a job you can get in the Sales and Marketing department as a data scientist.


Data Science Jobs in Sales and Marketing


In India, the average salary for the position of Data Scientist in the sales and marketing sector, as reported by LinkedIn, is Rs 8,50,000. 


Based on Experience

Data scientists job linkedin

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                                                                             Sourse: Linkedin


  • Beginner (1-2 years)-₹ 6,11,000 PA
  • Mid-Senior (5-8 years)-₹ 10,00,000 PA
  • Expert (10-15 years)-₹ 20,00,000 PA


Based on Role

Data scientist salary

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                                                                             Source: Linkedin

  • Data Scientist – ₹ 8,00,000 PA
  • Data Science Engineer – ₹ 9,76,133 PA
  • Data Analyst – ₹ 6,02,784 PA


Based on location.

data scientist salary location

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                                                                            Source: Glassdoor


  • Mumbai – ₹ 7,88,789
  • Chennai – ₹ 7,94,403
  • Bangalore – ₹ 9,84,488
  • Hyderabad – ₹ 7,95,023
  • Pune – ₹ 7,25,146
  • Kolkata – ₹ 4,02,978


Where can Learnbay Help you?


Learnbay provides some of the best Data Science courses in Bangalore. It especially provides Data science and AI courses for working professionals. It also provides you with various domain electives, sales, marketing, and HR being one of them. Let us see about this elective in a bit more detail.


Learnbay’s teaching approach

  • Our methods are completely practical-oriented. This means you will learn through project work and other practical activities.
  • You can choose any two domains to learn with.
  • The module is separated into modules for each specialization, so it is easy for you to understand the concept in the order of precedence. 
  • This domain elective will also learn tools like Keras, Hadoop, MongoDB, Pytorch, TensorFlow, Seaborne, and OpenCV. 



  • Sales Prediction (Sales Domain)

Big-Bazar like companies employs this machine learning model in order to identify the characteristics of stores and products that are most important for increasing sales. Certain characteristics have been identified for each retailer and product. This machine learning project aims to build a predictive model that will determine the sales of each product at a particular retailer. It involves determining the future or present-day sales using data such as past sales, seasonality, and economic conditions. This model can predict sales on a specific day if it is given a set of inputs.

Sales and Marketing Forecasting Dataset from Kaggle                                               Sales and Marketing Forecasting Dataset from Kaggle


  • Resume Parsing(HR Domain)

All businesses face challenges in hiring the right talent. The challenge is made more difficult by a large number of applicants, especially if the business has high labor costs, is growing rapidly, or is subject to high attrition rates.IT departments lack the ability to access new markets. A typical service company will hire professionals who have a range of technical skills and business expertise to solve customer problems. Resume screening is the process of selecting the best talent from many applicants. Large companies don’t have the time to read every CV. Therefore, they use machine learning algorithms for Resume Screening.

Resume Screening criterion Dataset from Kaggle

Resume Screening criterion Dataset from Kaggle


  • Keyword Generation for social media ads(Marketing Domain)

Keyword generation can be defined as the task of automatically identifying a set of terms that best describe the subject matter of a document. This is an important technique in information retrieval systems (IR). Keywords simplify and speed up the search. Keyword generation can be used for text classification and topic modeling. S.Art and al extracted keywords to determine patent similarity. Keyword generation allows you to automatically index data, summarize text, or create tag clouds using the most representative keywords.

Kaggle dataset for keyword generation or extraction

Kaggle dataset for keyword generation or extraction



Throughout this blog, you have seen Data has become the cornerstone of all industries including HR, Sales, and Marketing. Data science techniques help sales leaders to manage their businesses efficiently, focus on viable plans, create leads, improve customer experience. This adoption of big data analytics is differentiating winners from the rest across sectors, resulting in an increase in the demand for skilled data professionals. Thus, You can undoubtedly build a rewarding career in this industry to secure a high-paying job. 


Hope you found this blog informative enough.


To know more about Learnbay courses, Do check out our site. Make sure to follow our social for more amazing content:








Media, hospitality, and transportation: Know-How Data Science Will Help you to Survive?

Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” – Angela Ahrendts Senior Vice President of Retail at Apple Inc


The Same is true for Media, Hospitality, and the transportation industry.


Media, Hospitality, and Transportation have been one of the most profitable industries as of now. Even a few countries’ GDPs become 70% dependent on the travel, tourism, and media business. Consequently, the pay scales of promising candidates in these sectors also kiss the sky. The role of data science in Media Hospitality and Transportation has been talked about in this blog.

But a pandemic hit in the last couple of years has created an immense impact on these industries. Pandemic has increased the unemployment rate in these industries. The New York Times has revealed that about 37000 media employees (highly paid) lost their jobs. Even 30 million American Citizens from these industries become jobless. If this is the image of the US, then you can easily imagine the picture of India and other Asian countries. Let us see how data science in media, hospitality, and Transportation has helped the businesses to boom.





The most depressing thing is the higher your salary is, the greater your chances of losing the job.

So, is not there any way for media, hospitality, and transportation pros to secure their lucrative career?


Certainly, there is a way.
In this article, I have described the challenges faced by the Media, Hospitality, and Transportation sectors. Based on my case study in these sectors, I believe that revolutionizing technology, such as Data Science and Artificial Intelligence, has benefited these industries in overcoming their obstacles to increasing corporate efficiency.


The media industry has been known to be one of the most lucrative industries. Due to the surge in Pandemic, many movie productions were stopped therefore incurring a lot of loss for the whole crew. The struggling actors especially had a very hard time, as discussed in this Times of India issue. The competitive algorithm of social media platforms has ultimately hit the media Industry Quiet hard.


The hospitality industry was affected gravely by the pandemic’s rise and the imposition of a global lockdown. Many small restaurant chains whose families depend on it had to shut down. Even hotels were affected when the majority of the people were being separated from their families due to COVID, and the hotels had to spend a large amount of revenue to equip these people whilst maintaining the integrity and safety of the hotel staff. This had a lot of people quitting their jobs and being unemployed for a very long time.

media transportation and hospitality

Image created by Author


Transportation is not indifferent to losses. The pressure on the organization during this coronavirus pandemic is from the relocation of citizens to the core transport with the basic workforce to keep cargo and key and important workers on the move. Shifted to system maintenance. The side effect of this shift is the sudden change in the revenue sources of transportation companies, many of which are experiencing unexpected financial constraints. Organizations need to plan to ensure that the transport network is ready to return to normal if the coronavirus pandemic blockade is lifted. An article by Deloitte has outlined all the shortcomings of the transport industry.


So what can be done? These industries need to make space for more data science jobs to revive their infrastructure. Data science tools will allow a much better understanding of the pain points in the domains and help with their growth even during a natural disaster. If this isn’t convincing, let us see the need for data science in the aforementioned sectors.


Need for Data Science in Media, Hospitality, and Transportation



Every day, digital reality presents new challenges to big players in the entertainment and media markets. Customers are more likely to seek out the best service, no matter the circumstance. This market is becoming more competitive by the day. The application of data science to various aspects of daily life is a new trend that requires entertainment and media professionals to think creatively.


  • Personalized Marketing

    A company’s ability to attract customers’ attention is crucial, especially if it is involved in the entertainment and media business. It is more difficult to keep customers’ attention when they have had a quick and memorable online experience.

  • Real-time analytics

    The name real-time analytics refers to the ability to process data quickly and present the results in a short time. The speed at which media and entertainment companies process the data is crucial.

  • Content distribution on social media

    Social media has given media and entertainment companies a wonderful opportunity to strengthen their marketing strategies through the powerful tool of social content distribution. For large media companies, it is now possible to view general tendencies, user preferences, experiences, interests, and histories in one click.

  • Analysis of media content usage

    Big media companies can use data science algorithms to make data work for them and generate profit. Media content analysis is a well-developed method that analyses the message and connotations of content. The media content analysis process consists of three main levels: capture and understand. The algorithms identify patterns and incidents within the text. The data can then be processed. These frameworks are used to define the tone of the text. Its influence on the user can be predicted.

Data science in Media Hospitality and Transportation

Image by Author



AI and DS technologies are being used in many industries, but I am most excited about how these can be applied to the hospitality industry. Recently, however, I noticed a lot more innovation in the hospitality industry. The industry is embracing these advanced technologies with open minds.


  • Marketing

Machine learning systems enable hotels to analyze historical data to make better decisions. Marketers can use all these inputs to target the right audience at the right moment with targeted campaigns.


  • Revenue management

When data drives revenue management, hotels can forecast demand and analyze customer behavior patterns more accurately. It automatically integrates and analyses stacks of data from multiple sources, saving a lot of labor.


  • Booking engine

AI-powered booking engines collect data each time a potential guest interacts with the website. These engines often have advanced learning algorithms to analyze the data to learn more about customers and provide the best price.


  • Reputation management

Reputation management systems have been gaining much traction in recent years within the hospitality industry. It makes sense, right? They help you build trust and brand loyalty. These systems can be AI-based, which increases their capabilities. Sentiment Analysis can be used as a great example. Natural language processing (NLP) is used to deduce the intent or sentiment behind an opinion or review. It is a highly effective method of gauging people’s thoughts about your brand.



The promise of data science Combines those data sources with external data sets (e.g., search, social media reviews, weather, traffic reports, weather, and weather) to solve problems, reduce costs, and predict future events.


  • Enhanced Customer Service

Personalization has another advantage. This is allowing the transportation industry to improve customer service. Delta Airlines provides a Guest Services Tool for their SkyPro devices to their flight attendants. This tool and device can be used by flight attendants to improve customer service by reviewing preferences. United Airlines attendants also have access to a tool that gives them information about customers, such as last flight details, dietary requirements, and their connection schedule.


  • Identifying MVCs (Most Valuable Customers)

    Some customers will travel more than others; it is a fact. Companies need to be aware of major players to avoid customer churn. Already, the travel industry has a huge legacy of data about MVCs from loyalty programs. Combining historical data with predictive and real-time analytics is the key to anticipating what MVCs want in the future. It is much more expensive to acquire a customer than keep an existing one.


  • Up-Selling and Cross-Selling

    Let’s suppose you are traveling to Buenos Aires to attend a four-day business conference and that you decide to spend a weekend exploring the city. You want a flight that departs Sunday and arrives early Monday. Your airline will offer cross-selling and upselling opportunities if it has DS.


  • Safer Travel

    DS can save lives when it comes to safety. A wide range of sensors is available in today’s automobiles, trains, and planes. These sensors provide control centers with continuous streaming data that provides real-time information on all aspects of the journey (e.g., driver behavior, environment, performance, etc. ). Transportation data scientists have this information and develop complex algorithms to predict and even prevent problems.

What are data science tools used in the media, hospitality, and transportation industries?

There are various DS tools used in all these industries. These are some of the tools and their uses.


1. SAS

It is one of the data science tools designed specifically for statistical operations. SAS is a closed-source proprietary software used by large organizations to analyze data. SAS uses the base SAS programming language to perform statistical modeling. Professionals and companies that develop reliable commercial software use it extensively. SAS provides many statistical libraries that Data Scientists can use to model and organize their data.


2. BigML

It is another popular Data Science Tool, BigML. It offers a cloud-based, fully interactive GUI environment for processing Machine Learning Algorithms. BigML is standard software that uses cloud computing to meet industry needs. Companies across their business can use Machine Learning algorithms. It can be used for product innovation, sales forecasting, and risk analysis.BigML is a leader in predictive modeling. BigML uses many Machine Learning algorithms, including classification, clustering, and time-series forecasting.


3. D3.js

Javascript can be used primarily as a client-side programming language. Interactive visualizations can be made with Javascript libraries D3.js and Javascript-tutorial. You can create dynamic visualizations and analyses of data using D3.js’s many APIs.D3.js also allows animated transitions to be used. D3.js allows for updates on the client and actively uses the data change to reflect visualizations in the browser.



MATLAB is a multiparadigm numerical computing environment that processes mathematical information. It is a closed source software that allows for the implementation of matrix functions and statistical modeling. MATLAB is used most often in many scientific disciplines. Data Science uses MATLAB to simulate neural networks, fuzzy logic, and more. You can create stunning visualizations with the MATLAB graphics library. MATLAB can also be used for signal and image processing. Data Scientists will find it very useful as they can use it to tackle any problem, including data cleaning and analysis, advanced DeepLearningalgorithms, and even data extraction.

5. Excel

The most popular Data Analysis tool. Microsoft originally developed Excel for spreadsheet calculations. Today, Excel is used extensively for data processing, visualization, and complex calculations. Excel is a powerful analytical tool for Data Science. Excel is still a powerful tool for data analysis. Excel is an excellent analytical tool for Data Science. Excel is still a powerful tool for Data Science. Excel has many formulae, tables, and filters. Excel allows you to create custom functions and formulae. Excel is not the best tool for large data sets, but it can create powerful visualizations and spreadsheets. Excel can be connected to SQL to manipulate and analyze data. Many Data Scientists use Excel for data cleaning because it offers an interactive GUI environment to pre-process information quickly.

Companies Hiring Data Scientists in the Media, Transportation, and Hospitality Industry


Data scientist jobs

Image by Author

                                                                 Source: Glassdoor Salary Insights


Data scientists’ salary in India is between Rs 4.9 Lakhs and Rs 28.0 Lakhs, with an average salary of Rs 13.2 Lakhs.
1. ABC offers about 19.2 Lakhs per annum for a data scientist with more than 10 years of experience.
2. Zee Entertainment offers 15.9 Lakhs per annum for DS specialists with 1-5 years of experience.
3. Times Internet offers 11.4 lakhs per annum for Data scientists with 2 to 3 years of experience in the field.
4. Fork Media offers 7.4 lakhs per annum for data scientists with 2 to 3 years of experience.
5. Pratilipi offers 30.9 lakhs per annum for DS specialists with 3 to 5 years of experience.

ambition box data scientist jobs

Image by Author

Source: Ambition box




The average Data Scientist’s salary in the hospitality industry is in India at Rs 14.9 Lakhs per annum. This includes employees with less experience than one year and up to five years. Data Scientist salaries range from Rs 10 Lakhs up to Rs 22 Lakhs.

1. Oyo Rooms offers about 14.9 Lakhs per annum for a data scientist with more than 10 years of experience.
2. Taj Coromandel offers 27.1 Lakhs per annum for DS specialists with 1-5 years of experience.
3. Citadel offers 12 lakhs per annum for Data scientists with 2 to 3 years of experience in the field.
4. Marriott offers 9.4 lakhs per annum for data scientists with 2 to 3 years of experience.
5. ITC offers 31.8 lakhs per annum for DS specialists with 3 to 5 years of experience.

Data science in Media Hospitality and Transportation

Image by Author

Source: Glassdoor Salary Insights



Data scientists’ salary in India, with less than one year experience or more than 12 years, is Rs 7.0 Lakh to R 33.6 Lakh. Based on 76 salaries, the average annual salary is 18.4 Lakhs.

  • Uber offers about 29.5 Lakhs per annum for a data scientist with more than 10 years of experience.
  • ElasticRun offers 9.3 Lakhs per annum for DS specialists with 1-5 years of experience.
  • Citadel offers 12 lakhs per annum for Data scientists with 2 to 3 years of experience in the field.
  • RedBus offers 15.6 lakhs per annum for data scientists with 2 to 3 years of experience.
  • Indian Railway Catering and Tourism offers 31.8 lakhs per annum for DS specialists with 2 to 4 years of experience.

    How can Learnbay help you?

    Learnbay offers one of the best data science courses in Bangalore and also offers an array of domain electives. One of which is Media, Hospitality, and Transportation. Let’s see some of the features of Learnbay and its domain electives.

Objectives of the electives

  • How to identify hotel problems
  • Data collection, storage, and manipulation
  • Processing data
  • Model selection algorithms
  • Security and deployment
  • Interpretation of data
  • Implementation of data insights
  • Taking the right business decisions
  • Improved business strategies
  • Improved identification of the target audience
  • Satisfying customer needs


Learnbay teaches you practically through projects. These are some of them.

1.   Ola/Uber Taxi Demand Prediction (Transportation Domain)

Taxi-hailing companies must predict taxi demand to optimize their fleet management and understand their needs.

We would build a model that would use users’ ride request data. It would include attributes such as ride-booking time, pickup location, and drop point latitude/longitude. This model would predict the demand for taxis in a specific city area. It would also help companies optimize taxi concentration to meet users’ needs.

Ola/Uber Taxi Demand Prediction

Resultant DataFrame Dataset

2.  Netflix Movies and TV Shows (Media and Entertainment Domain)

Discover what other insights you can get from the Netflix list of movies and tv shows available as of 2021. This project is Data Analysis with Python. It will analyze a data set of Netflix movies and TV shows. This data set was derived from Keras. To analyze the data and visualize the information about the movies and tv shows, you will be using Keras. This project uses Python and pandas and NumPy, NumPy, and matplotlib to analyze. It is also intended to help you complete the Jovian’s Data Analysis using Python – Zero to Pandas course. This course is well-structured and was delivered with great interest for the learners.

Netflix Movies and TV Shows

Netflix movies and TV shows dataset from Kaggle

3.  Airbnb New User Bookings(Hospitality Domain)

Users who are new to Airbnb can book a space to stay in over 34,000 cities spread across more than 190 countries. With the ability to predict accurately where the user is likely to book their first trip, Airbnb can share more customized content with their customers and reduce the length of time before booking their first trip and improve their forecast of the demand. This project mainly focuses on the advanced application of XgBoost.



The role of data science in media, transportation, and hospitality is huge. The media, Transportation, and Hospitality industries have a great future with data science. That includes even natural disasters like Pandemics or even a tsunami. With the reasons mentioned above and tips, we’re sure you will get a good chance in this domain.

To know more about domain specializations, click here and make sure to follow all our socials:





Data Science Jobs – The Busting Ways To Secure Attractive Packages In Health Care?

Got your master’s degree in genetic counseling, occupational therapy, health administration, or any other healthcare domain? Well, you might be very anxious to join a renowned organization with a six-figure salary package? But wait. It’s not that easy these days. I have found more than 70% of lucrative healthcare degree holders are still struggling to secure their position. Every single day they dreamt of losing their jobs.

But what’s the reason? They all are well qualified and experienced enough. 

Well, there has been a new ‘must-have’ skill requirement in this industry for the last few years. It’s Data Science and AI. 

Yes, data science and AI have changed the direction of the editor job market. Lots of vacancies are there, and lots of candidates are roaming around to fill those positions. Still, they are not getting hired. Some are working almost 24X7 with a package of 3 LPA-4 LPA, even after having a master’s degree. 

Average CTC in health care industry according to ambition box

However, there is a contrasting side also. A few are enjoying 10 to 12 LPA, with an average of 3 to 4 years of experience in the same domain.

This blog will help you to find out the root path to secure your data science jobs in health care sector. 

First, let me answer the toughest question coming to the mind of most readers, right now. 

Do data science job in health care sector come with attractive packages?

In present days, while we think about the healthcare system, we visualize highly tech upgraded and AI-powered patient care services. It includes SMART clinical apparatus and advanced medical research. But are you aware of the fact that for the last few years the healthcare industry is in the topmost positions on cyber criminals’ hit list? As per one of the Accenture Survey in 2017, in the first quarter, about 26% of US patients become victims of such birches. 

In India, during the 1st wave of the COVID-19 pandemic, lots of COVID-positive patients’ sensitive personal data got leaked from several governments and NABL healthcare portals. Such data types include residential address, contact number, Aadhar number, etc. Such incidents caused the suspension of so many frontlines as well as other associated employees. Wrong handling of data and lack of data management expertise was the key reason behind it.

Employees from a non-data science background in healthcare are also at the risk of job loss. So many pharmaceutical companies are now tieing up for better regional sales across the world. Recently, US-based pharma company Eli Lilly, fired 120 of their Indian office employees because of the transfer of anti-diabetic medicine selling rights to Cipla (Source: The Economic Times). This is very common in business but candidates with core DS skills and analytical knowledge are in quite a safer position. The Pharma industry needs them. Even the Parma industry is suffering from a huge lack of such talent. 

Healthcare data science jobs are all the rage because when you look at it, healthcare is that domain where DS has been used predominantly and for a very long time. The clinical industry cannot run without applying some DS concepts, a well-known fact. So there is no doubt that you will have a great scope in the healthcare industry as a data scientist. But does it provide just as great a salary as working in a product-based MNC? Let’s see what your data science salary depends on to find the answer.

Data Science Job in Health Care Sector

Image created by Author

The Drivers of Your  your data science salary 

We all know and hear that specialists in this field make a lot of money, but we are unaware that it varies from domain to domain. How much a data scientist makes in a field is dependent on these factors.

  • Need for DS in the industry.
  • Type of role that you will be playing
  • Your skills that are relevant to the field of interest
  • Your skillset as a data scientist.
  • Years of experience you have as a Specialist.

Now that this is in check, let us know more about the correlation between DS and healthcare.

Data Science Job in Health Care Sector

Why is data science important in healthcare?

  • According to one study, (source: dailymail uk) the amount of data produced by all human bodies is 2 terabytes per day. This data includes brain activity, stress levels, heart rate, sugar content, and more. There are now more advanced technologies for processing such large amounts of data, one of which is DS.
  •  It helps to monitor the patient’s health based on the recorded data. 
  • With the help of DS in medicine, it has become possible to detect the symptoms of disease very early. With the advent of various innovative tools and technologies, doctors can also remotely monitor the patient’s condition.
  • Any industry that generates large amounts of data needs DS. The medical industry produces large datasets of useful information about patient demographics, treatment plans, health check results, insurance, and more. The data collected by Internet of Things (IoT) devices has attracted the attention of data scientists.

All these techniques and methods make it important to have DS in the healthcare industry.

Can data scientists work in healthcare?

So far, we have established that data scientists play a major role in the healthcare industry, so the answer to this question is absolutely! These will be your responsibilities as a medical data scientist.

The role of the data scientist is to implement all the technologies of DS and incorporate them into health software. Data scientists extract useful insights from the data to create predictive models. Overall, the tasks of data scientists in health care are:

  • Data collection from patients 
  •  Analysis of hospital needs  
  •  Perform data analysis using a variety of tools 
  •  Implement algorithms in your data to gain insights 
  •  The development team creates a predictive model

Applications of Data Science in Healthcare

The uses of data science in healthcare are close to infinity. But let us see some of the most important applications. 

Medical Image Analysis

DS identifies scanned images to find defects in the human body and helps doctors develop effective treatment strategies. These medical imaging tests include x-rays, ultrasonography, MRI (Magnetic Resonance Imaging), and CT scans. Proper analysis of images from these tests helps doctors gain valuable knowledge to treat them better.

Algorithms used in medical image analysis

  • Anomaly detection algorithm: This algorithm helps identify conditions such as fractures and dislocations.
  •  Image Processing Algorithms: Image processing algorithms are useful for image analysis, improvement, and noise reduction.
  •  Descriptive image recognition algorithm: Visualises and extracts data from images and uses them to interpret them to form larger images (for example, merge images from brain scans and name them accordingly.)

Predictive Analytics in Healthcare

 A predictive analytics model based on DS makes predictions about a patient’s condition. It also helps develop strategies for the appropriate treatment for the patient. Therefore, predictive analytics is a very useful technique and plays an important role in the medical industry.

Algorithms used in predictive analysis

  • Classification Model: For categorizing patient data according to requirements and needs.
  • Clustering Model: The clustering model sorts the data into separate nested smart groups based on similar attributes. Suppose an e-commerce shoe company wants to run a targeted marketing campaign for its customers. In that case, they can explore hundreds of thousands of records and create a bespoke strategy for each individual.
  • Forecast Model: As one of the most widely used predictive analytics models, forecasting models involve predicting metric values ​​and estimating new data numbers based on insights from historical data.


In the past, genomics research was a tedious and time-consuming task before the advent of powerful data analysis techniques. There are millions of pairs of DNA cells in the human body. But now, DS applications in healthcare and genomics make that task easier. With the help of various DS and big data tools, we can analyze human genes with less effort and time. With these tools, researchers can easily find the drug that best responds to a particular genetic problem or a particular type of gene.

Algorithms used in Genomics

  • V Framework: The V framework categorizes volume, velocity, and variety of data. In genomics, it is used to analyze the current data concerning the other applications in data sciences.
  • 4M Framework: So what is the 4M framework? DS mining in the natural sciences is closely related to mathematical modeling. A concise way to understand this relationship is the 4M framework developed by Lauffenburger. This concept describes the overall process of systems biology, which is closely related to genomics in terms of 
    • quantity measurement
    • large-scale mining

Model the extracted observations, and finally manipulate or test this model to ensure accuracy.

So these are some of the applications of DS in the clinical industry. There are many more, but these are some of the prime examples. Let’s see what you will be doing as a specialist in this domain.

Role of a Data Scientist in the Healthcare domain

  • Most data scientists have technical skills such as probability and statistics, data visualization, machine learning, AI knowledge, and programming languages ​​such as R, Python, and SQL. While these skills can help you analyze through sources, healthcare data scientists must, first and foremost, be strong problem-solvers who understand the goals of their organization. 
  •  Another common requirement for Healthcare Data Science jobs is a good understanding of quantitative data analysis. Due to the large amount of data generated by hospitals and authorities, data scientists must organize, manage, and analyze different datasets without being overwhelmed. When a large amount of information is mixed, healthcare data scientists are expected to connect points and identify solutions and suggestions to help businesses achieve their goals. 

 Other common roles played by healthcare data scientists are: 

  • Convert data into digestible nibbles that non-technical members of the organization can understand.  
  • Understand hospital functions and systems and use data results to support decision-making. 
  •  Database management includes data collection, storage, acquisition, security, etc. 
  •  Creating reports and dashboards to give administrators access to results.
  • Agile enough to jump over different dashboards to types of records, from operational to clinical to financial.

How much do healthcare data scientists make?

This is the number one question everyone has regarding doing domain-specific roles in DS. But I am pleased to tell you that the domain discussed in this blog offers big bucks to specialists. 

On average, a DS specialist working in the medical field is about Rs.50 lakhs per year in India. These are some of the popular healthcare companies and how much they offer.

Companies and Packages

  • GE Healthcare offers Rs.18.6 lakhs per annum
  • SCIO Health Analytics offers Rs.7.10 lakhs to 8.64 lakhs per annum
  • Novartis Healthcare offers Rs.21.7 lakhs per annum
  • Dr. Reddy offers Rs.12,89,859 per annum.

Data Science Job in Health Care Sector

Image by Author

Source: Ambition box salary insights

Based on area

  • Analysts in the New Delhi region reported making Rs.18,70,000 a year. 
  • Data analysts in the Bangalore area reported making Rs.12,89,859 a year.   
  • Pharmaceutical data analysts in the Chennai region reported making Rs. 70,000 yen a month. 
  •  SAS programmers in the New Delhi region are reported to have made Rs.20,000 a month

Data Science Job in Health Care Sector

Image by Author

Source: Glassdoor salary insights

What skills does it take to become a Healthcare Data Scientist?

With each domain come certain requirements that you need to check to become a specialist in that field. Does that mean that you need to know bioinformatics to become a data scientist in the medical industry? Well, continue reading to find out.

  • Health DS is a relatively young discipline with epidemiology, statistics, mathematics, computer science, and computer science. 
  • Innovative skills are needed to unleash knowledge from complex health data and address some of the biggest health problems facing the world today. 
  • Health care data scientists manage and process vast and confusing health data records from various sources, putting them all together in an evaluable format. 
  •  They provide knowledge about analyzing data using a statistical machine learning approach and extracting useful insights from the data. 
  •  Quantitative methods, applied regression, statistical analysis, statistical inference calculations, machine learning, statistical advice and collaboration, and epidemiological methodologies. 
  • The major programming languages ​​used in most health-related DS courses include a combination of Python and R, and in some cases, SAS.
  • Requires a strong computational or math background. 
  • A solid quantitative background provides emerging health data scientists with several healthcare and medical research areas. 
  • These roles are ideal for students with a bachelor’s degree in mathematics or statistics or related disciplines. Up-and-coming healthcare data scientists need a deep understanding of statistics, linear algebra, and calculus.

One of the other skills needed is communicating results with different healthcare professionals. Health data scientists communicate with other data scientists about how to use the data and insights, discuss with clinicians to understand the illness they are studying, communicate with laboratory scientists, and most importantly, patients. It would help if you communicated clearly and transparently with the general public. The focus of all healthcare DS projects is to develop suitable applications that can work with patients or the general public in the healthcare sector and the commercial arena.

Having taken a look at all the above information, you may be wondering how Learnbay will help you with specializations such as healthcare? Learnbay Primarily focuses on domain specializations, and medicine is one of those domains. Let’s see what you will get when you learn with Learnbay.

Domain Specialization with Learnbay 

Learnbay is known for its diversity in subjects. This is why it provides some of the best data science courses in Bangalore. But the best part is it comes with a hybrid model of learning, which means you can take classes online and offline. So let’s see what the Healthcare domain of Learnbay provides you with.

What can you expect?

  • To learn about the application of advanced tools that will allow you to effectively use them in this domain.
  • You will also be developing leadership skills that will enable you to deliver products that stand rightly with the customer’s needs.
  • You can also get to take a look at more than 20 Case Studies.
  • Graded Assignments to test your skills along the way.
  • About 6 live projects in the domain to make you more well-versed with your skills.
  • You can also expect to land interviews and placement opportunities if you decide to take a course with Learnbay.

Who is it suitable for?

  • If you are a seasoned professional in the healthcare, pharmaceutical, or clinical domain looking for that upgrade in your career, this is suitable for you.
  • Or if you are a professional who wants to learn about DS and its methodologies in the Pharma/Health sector to make that switch even then, this course is highly compatible for you. 
  • It is completely fine if you do not have a mathematical or statistical background.

Projects you will get to work on

  • Personalized Medicine: Much has happened in recent years. How precision medicine and, more specifically, genetic testing change the way a disease like cancer will be treated. But because of the large amount of respective data, manual labor is still needed but only to a partial extent. We should strive to maximize the potential of personalized medicine for this project. Malignant tumor undergoes thousands of genetic changes as soon as they develop. proper analysis of relevant data can make such research much easier. Well-trained ML models can easily handle such data.
  • Ultrasound Nerve Segmentation: It is important to properly identify the neural structure on ultrasound images before placing the patient’s pain catheter. This DS project in Python needs to create a model that can detect neural components in a collection of ultrasound images of the neck. This will help improve catheter placement and contribute to a pain-free future.
  • Healthy Diet chart for COVID-19 Patients: The right food intake fosters the recovery of Corona patients. But the measure of diet is different for each patient. Advanced analytical surveys have made it possible. Even a few smart healthcare devices and apps can provide effective outcomes. You can easily calculate the required level of each nutrient. 

Image Source: Kaggle Data Set

To know more about domain specialization or if you want to take an entry-level DS course, then take a look at the Data Science & AI Certification| Domain Specialization For Professionals course


Now that we have reached the end of the blog, I hope it has served the purpose of educating you on the importance of domain expertise. Another thing that we wanted to make clear was the potential of this in the coming future and even the present. Pharma and the medical industry will never be outdated as long as humans exist. So if you want to make that switch, then I would say that you can change into this industry with your eyes closed.


How To Make a Rewarding Career in the Energy, Oil, and Gas Domain as a Data Scientist?

The oil and gas sectors have been the most lucrative arena for most chemical engineers, petroleum engineers, mechanical engineers, and even geologists (petroleum). Not only the private occupations but also the government job scopes are quite high in this subject. But beyond all kinds of expectations, people in these industries are at significant risk of losing their employment. So what occurred all of a sudden!!

Well, no need to get panic. We all know that if one road gets blocked, certainly there are other ways to escape. Data Science and AI are the ultimate escape route from this roadblock. This blog talks about data science in energy, oil, and gas industry. But first, let’s have a look into the ‘what’s happenings?’’

Unemployment Rate Based out of Petrolmi

According to PetroLMI, which provides industry labor market information, the unemployment rate, especially for oil and gas workers, peaked at 16.1% in 2020, 26% below 2014 levels. People working in this industry are quitting by themselves due to the lack of growth. People who have more experience are also not in the green zone. What is important to keep your ground in the domain is the skills that you have. You need to keep upskilling only then you will be safe. Even the oil and gas industry lives and breathes on data and has been going digital as well. It is very possible that not only will you retain your position in the industry but also have good pay as a Data science specialist.

Data Science salary is a hot topic everyone thinks about when becoming a data scientist. We all have heard praises about how lucrative the DS field is. So much so that data scientists are notorious for being pompous. But I wouldn’t blame them. A data scientist makes big bucks in any and every domain. It is a very versatile field, and the domain-specific approach of specific organizations makes it a game-changer for many people looking to make that career switch.

Now that we have established that DS is a very profitable field and there is a need for it in any field, I would like to tell you something very particular. That is the importance of data science in energy, oil, and gas industry. So let us start by asking some critical questions.


Use of Data Science in Energy, Oil, and Gas Industry

Image by Author

The oil and gas industry misses boats in data science, a reinterpretation of the inherent value of data and its strategic assets. Like other industries, today’s oil and gas industry is looking for ways to improve efficiency, thereby reducing operating costs and increasing revenue.

However, oil and gas companies are also subject to exceptional safety, environmental, and regulatory reporting requirements in contrast to many industries. Therefore, data science has many advantages that can help improve data efficiency and increase sales when adopted in the industry. With this, I think you can make out that DS is essential in the domain we are talking about.


Need for Data Science in the Energy domain

  • The amount of data in the oil and gas industry increases exponentially due to advances in information technology.
  •  This includes everything from sensor recording during exploration, drilling, production, and seismic manipulation to in-drill logging (LWD) technology that enables real-time recording of drilling data.
  •  Also includes fiber optic solutions that provide a wide range of data on environmental conditions such as temperature, oil reserves, equipment performance, and condition. Managing this data and using it as a strategic asset can significantly impact its financial performance.
  • The plunge in oil prices has forced oil and gas companies to go beyond traditional methods to seek broader changes in business practices to improve performance and reduce costs.
  • Better data analysis and technology are essential in determining the success of oil and gas companies.

Advantages of Data Science in the Energy, Oil and Gas Industry.

As of now, we have established that data science has found its uses in various fields. That is because the DS has multiple advantages no matter where you want to use it. Such is the malleability of this field. It has numerous advantages in any field that it steps foot in.

Here are some high-level examples of how data science can help the oil and gas industry. 

  • Exploration and Discovery-Geological data such as seismic data and rock types in nearby drilling holes can predict oil pockets.
  • Production Accounting-You can link production data to alarms. 
  • Drilling and Completion-Predictive Analytics can use geological completion and drilling data to determine preferred and optimal drilling sites. 
  • Equipment Maintenance-Compare real-time streaming data from oil rigs with past drills to better predict and avoid problems and understand operational risk. These examples show the operational goals of oil and gas data science. 

 As with other technological advances, there are barriers to the successful use of data science: (Disadvantages)

  • Taxing Compute Resources-You may not have enough resources to store and  process large amounts of structured and unstructured data.  
  • Poor data quality-Data is stored in multiple locations and can be subject to inconsistent governance. 
  • Incorrect Modeling– The correct question may not have been asked or misunderstood.
  • Relentless Corporate – C-suite support is essential from the start. Communication between employees, SMBs, and data scientists is essential.
  • Talent Gap: Data science and data engineering talent is new to the oil and gas industry. These skills are still under development, and it can be challenging to put together the right team.

Projects To Level Up Your Resume

When we talk about entering into a specific specialization job market, it is essential to have the proper skills displayed on your resume that make you fit for the role. The best way to achieve that is through projects. These are some of the Oil and Gas domain-based projects that you can do to become a DS specialist in the field.

  • Prediction of cost overruns in Oil and Gas Engineering
  • Developing a Failure Prediction Model
  • Model for determining the optimum and efficient use of machines.

What are companies hiring for Energy, oil, and gas data scientists?

Data science, a versatile field, is also very lucrative for various fields and companies. These are some of the oil and gas companies that are looking for data scientists.

Image by Author

Source: Linkedin

  • Schlumberger, Cambridge will offer INR 1,468,040 per annum
  • Saudi Aramco offers INR 1,986,586 per annum
  • Praxair pays INR 997,500 per annum
  • BP will offer you INR 1,350,000 per annum
  • The total gives INR 1,080,000 per annum
  • And finally, National Iranian Oil Co (NIOC) offers INR 1,750,000 per annum

Now that the whole domain is sorted. Let us come to the salaries that you have been waiting for. Brace yourself for the mind-blowing information you will be bombarded with.

How much do data scientists earn with different variables?

Since we will primarily be discussing the pay range and living of a data scientist, let us see exactly how much data scientists in different roles and fields earn.

On average, it is estimated that a fresher in data science earns about Rs.6,98,412 as base pay in a year. This is subject to variation with every organization. However, the figures will be more or less near to the given value. Let’s see how much DS can get you according to your experience in the field.

Based on Experience

  • Freshers: The average income of entry-level data scientists is Rs 511,468 per year for recent graduates.
  • 1-4 years of experience: With 1-4 years of experience, his early career data scientist earns an average of 773,442 rupees per year.
  • 5-9 years of experience: Employees with 5-9 years of experience can expect an annual income of 12-14 rupees. The average salary-scale income of mid-sized data scientists is Rs 1,367,306 per year.
  • Over 10-15 years of experience: Very experienced employees with decades of experience or managerial positions can expect to earn anywhere from 24 lakh rupees a year to a healthy crore.

Based on Location (India)

The essential factor that can affect your salary as a scientist can vary from place to place based on the demand in the region. So let’s see how much you’ll get paid in a particular place.

Image by Author

Source- Glassdoor

  • In Mumbai, you’ll get paid Rs.788,789 per annum
  • Chennai will pay you Rs.794,403 per annum
  • In Bangalore, you will make Rs.984,488 per annum
  • In Hyderabad, you can get Rs.795,023 per annum
  • In Pune, you will get a salary of Rs.725,146 per annum
  • In Kolkata, you will get paid Rs. 402,978 per annum

Based on your skills

Believe it or not, the salary you get depends heavily on the field’s skills you have learned. So let’s see how much you can make.

  • Knowing R is the most critical and demanding skill, followed by Python. Python’s salary in India is projected to be around 10.2 lakh rupees per year. 
  • If data analysts have both big data and data science, their income will increase by 26% compared to just one piece of knowledge. 
  • SAS users are paid 9.1-10.8 lux per year, and SPSS Professionals are paid 7.3 lakhs per year. 
  • Machine learning salaries in India start at around Rs 3-5 lakhs and can rise to Rs 16 lakhs as the industry progresses. Python is one of the most popular machine learning languages, and Python developers in India have the highest salaries globally. 
  • Knowledge of artificial intelligence generally helps advance your career. Artificial intelligence payments in India are over 5-6 lakhs rupees for beginners in this field.

So these are all the variables that can affect your salary and how much you will get paid. To me, it is impressive. So I think you should start thinking about that career switch carefully.

Data Science Salaries in Other Countries

If you decide to go to a different country with the skills you have learned, you might be interested in learning about how much you can make. Don’t worry; I got you covered.

  • United States 

The United States is at the top of the list of countries that pay high salaries to data scientists willing to work for it. The average annual salary for US data scientists is $ 120,000 per year. Data scientist rewards are higher than in any other country. 

  •  Australia 

Australia is ranked second in the list of countries that make high payments to data scientists. This is evidenced by the influx of data scientists from Australia and other countries into the United States. Average salaries for data scientists range from $ 75,233 to $ 121,578 per year, based on experience. 

  • Germany

In Germany, job seekers in the data science sector earn up to € 5,960 a month. Working data scientists in Germany earn € 2,740-9,470 a month.

If you have stuck with this blog till now, you are either a budding DS aspirant or someone interested in the field or to make a career switch. So you might be thinking about how you can develop your skills. So let me tell you some resources to help you get those skills to land you that beautiful package.

How would Learnbay help you in this domain?

I highly recommend Learnbay because it provides one of the best data science training in Bangalore. It is also available online and provides some of the best features even in online sessions. Let me tell you about some of the features, and then you will know why I love this institute so much. According to an article by analytics insight, Learnbay is one of the most acknowledged data science institutes.

I prefer Learnbay so much because of the choices of electives it provides. Out of the leading electives, Oil and Gas is also one of them.

What will you learn if you choose this elective?

  • You will get to learn about tools like GitHub, Python, Power BI, Tableau, and more such tools regarding the particular domain.
  • You will learn about concepts like:
    • Analysis of seismic data and microseismic data, 
    • Decreasing drilling time and boosting safety
    • Improving occupational safety
    • Production pump performance and so on
  • You will also get to work on energy-based sectors like detection and prediction of a power outage, Power Failure Prediction, Dynamic Energy Management, and many more.
  • This domain comes with 2 modules:
    • In the first module, you will learn about the role of analytics and data science in the field of Energy.
    • In the second module, you will see the role of analytics and DS in the Oil and Gas field.
  • There are about 2 Capstone projects in this domain specialization.

Projects in the Energy and Gas domain

With Learnbay, you get to see the theoretical applications of the concepts and apply them in practice by working on various projects that will increase your expertise in DS concerning the domain you have chosen. So let’s now see the projects that you will get to work on if you choose this specialization by Learnbay.

  • Developing Smart Grid Security and Theft Detection Model (Energy Domain)

In this project, you will learn to build intelligent grids that usually go directly to the distribution cable. This will detect any kind of malicious activity in the energy resources and therefore lower the rate of resource-based thefts and fraudulent actions.

  • Developing Model Developing a Model for High Accuracy in Drilling Methods and Oil & Gas Exploration.

Drilling is a significant part of the process of extraction. Many of us will also be aware of the risks that it may pose to both the people working and those around them. Therefore, it is essential to develop a safe and effective method that will increase both the safety and accuracy of the whole process, which is why this project will allow you to develop such a model.

Kaggle dataset for oil industry price fluctuations

These are some of the DS courses on the platform.

  • Data Science and AI Certification | Domain Specialization for Professionals: This course is intended for professionals with at least one year of professional experience. The course lasts 7.5 months. 
  •  Data Science and AI Certification Program for Managers and Executives This is a unique course for professionals with over 8 years of experience as managers, team leaders, or in other prominent positions. The project period is 11 months. 
  •  Data Science and Business Analysis Program | Fast Track Course This 4-month course is intended for those who have taken a professional break of 6 months or more. 

To conclude it is very evident by now that data science in energy, oil, and gas industry has been booming. You can make a good career in the discussed domain while being a data scientist if you follow the tips and tricks that have been mentioned above. I hope you continue learning and growing in the data science field and prove to be an asset to the community.

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