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Category: Data Science

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. 










NLP and Deep Learning for Data Scientists

Deep learning and natural language processing (NLP and Deep Learning)are as busy as they’ve always been. The most in-demand technologies are deep learning and natural language processing (NLP). Advances in natural language processing and deep learning (NLP and Deep Learning) are produced nearly every day. Despite the fact that quarantine regulations in many nations have hampered numerous businesses, the machine learning industry continues to advance.

Aside from the fact that the Covid-19 has caused problems for a number of organizations, new-age tech skills such as Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP and Deep Learning) is in high demand. For budding Data Scientists, here are some must-read publications. In this article, we at Learnbay try to go over some of the most crucial and current breakthroughs.

How Deep Learning can keep you safe

Nukanj Aggarwal, ML Lead at Citizen, compiled a list of instances of how deep learning is being utilized to produce life-changing technology in his article that written. This article, ideally written by Citizen’s Machine Learning Lead, that shows how deep learning is being utilized to produce life-changing (or life-saving) technology.

  • Citizen is nothing but a company that analyses first and foremost responder radio frequencies that using speech-to-text engines as well as convolutional neural networks.
  • Citizen is a real-time emergency as well as safety alert app that quite notifies users of occurrences and crimes that have specially occurred in their neighborhood.
  • The company has been able to expand its apps to a number of US cities.
  • In the coming years, this technology could signify a significant shift in the police and first responder infrastructure.
  • The NLP-driven has the ability to transform the police and response infrastructure dramatically.

The Publication of the Open AI API

The publication of GPT-3 by Open AI was arguably the most significant development in the field of natural language processing this year. The API allows businesses and individuals to integrate OpenAI’s new AI technologies into their products and services. The publication of Open AI’s API, on the other hand, may have gone unnoticed by many.

  • The API’s goal is technically keeps their focus on to provide users with access to future models built by the corporation, such as GPT-3.
  • The API is general-purpose and can be used on nearly any natural language work; its success is inversely proportional to the task’s complexity.
  • This is significant since it represents a departure from the company’s usual practice of open-sourcing its models (as they did with GPT-2)
  • The company discusses why they opted to produce a commercial product this time, why they avoided open-source this time, and how they will manage any API misuse in the post.
  • This official blog discusses how the corporation moved away from open source in order to prevent API exploitation


IBM will no longer offer, develop, or research facial recognition technology

This official blog discusses how the corporation moved away from open source in order to prevent API exploitation. The CEO of IBM publicly indicated in a letter to Congress that the business would certainly be ceasing development as well as service offers of general-purpose facial recognition technologies and methodologies.

  • Artificial intelligence advancements have substantially enhanced facial recognition software during the last decade.
  • This was a significant step forward for the organisation, as well as a strong message to the data science community at large.
  • Face recognition technology will no longer be developed or researched by IBM, according to the company.
  • IBM’s decision to prioritise ethics and safety may have influenced other large IT firms (including Microsoft) to follow suit.
  • They feel that now is the right time to start a national conversation about whether and how domestic law enforcement organisations should use facial recognition methodologies.

Conversational AI: Neural Approaches

It examines neural approaches to conversational AI that have been developed in recent times as well. Audiences are interested in Natural Language Processing and Information Retrieval.

  • The researchers divided into three categories: question answering agents, task-oriented dialogue agents, and chatbots in this paper.
  • It offers a complete overview of the various approaches to conversational AI that have been developed in recent years, including quality assurance, task-oriented, and social bots, as well as a unified view of optimum decision-making.
  • An overview of state-of-the-art neural techniques is offered for each category, along with a comparison of them to traditional approaches, as well
  • Its a discussion of progress made and obstacles still faced, using specific systems and models as case studies and sets.

It offers a coherent perspective as well as a full presentation of the key concepts and insights required to comprehend and develop modern dialogue agents that will be critical in making world knowledge and services accessible to millions of people in natural and intuitive ways.

Language Models Are Unsupervised Multitask Learners

Question answering, machine translation, reading comprehension, and summarization are all examples of natural language processing (NLP) problems that are often ideally tackled using supervised learning on task-specific data models as well.

  • When trained on a new dataset of millions of online pages called WebText, the authors proved that language models began to learn these tasks without any explicit administration as well.
  • The language model’s capacity is nothing but critical to zero-shot task transfer’s effectiveness just because of the increase whilst it certainly enhances performance in a log-linear pattern-wise across tasks.
  • These findings point to a possible avenue for developing language processing algorithms that learn to fulfill tasks based on natural demonstrations.


Generative Pre-Training Improves Language Understanding

The researchers discussed natural language processing and how discriminatively trained models can struggle to perform effectively in this paper published by OpenAI.

  • Most deep learning approaches necessitate a large amount of manually labelled data, which limits their usefulness in many sectors where annotated resources are few.
  • The approach’s effectiveness was technically proved on a numeric of natural language processing criteria, as according to the specific researchers.
  • These target tasks do not have to be in the same domain as the unlabeled corpus in our configuration.

They suggested a broad task-agnostic model that beat discriminatively trained models that use architectures specifically generated for each specific task in around 9 of the 12 tasks that studied, greatly outperforming the state-of-the-art. Their goal is to learn a universal representation that can be used for a variety of tasks with minimum change.

Deep Learning Generalization

Many difficult research areas, like image recognition and natural language processing, have seen considerable success using deep learning.

  • Deep learning has had a substantial impact on the conceptual foundations of machine learning and artificial intelligence and has achieved significant practical success.
  • They would demonstrate in this certain Deep Learning Generalization article that deep learning technology nowadays is a strong contender for increasing sensing abilities.

The Model Card Toolkit for Easier Model Transparency Reporting

Transparency in machine learning (ML) models is crucial in a range of sectors that affect people’s lives, including healthcare, personal finance, and implementation as well. It gets more difficult to convey the intended use cases and other information to consumers downstream whenever larger and also possibly more and more intricate deep learning models are developed.

  • The details that developers need to assess whether or not a model is appropriate for their use case may vary, as will the information required by downstream users.
  • To help and assess that how to tackle this particular difficulty, as Google researchers ideally developed the “Model Card Toolkit,” which particularly simplifies the creation of model transparency reports.

The Complete Guide to Deep Learning Algorithms

This article, written by Sergios Karagiannakos, the founder of AI Summer, provides a comprehensive guide to deep learning.

  • Deep Learning is getting a lot of traction in both the scientific and corporate worlds.
  • Sergios Karagiannakos, certainly the founder of AI Summer, who has written a comprehensive handbook.
  • More and more businesses are incorporating them into their regular operations. It covers far too many topics, ranging from various types of neural networks to deep learning baselines.

Deepfake Detection Tools and AI-Generated Text

With the widespread dissemination of misinformation on social media, I was alarmed when I noticed it had reached my own inner surrounding. The consequences of such deepfakes have been disastrous, with hacked videos of public personalities circulating, putting their reputations at risk. I wanted to help counteract the nefarious use of these technologies as it has become easier to make deepfakes and manufacture fake articles using AI.

  • Given the catastrophic consequences of deepfakes, many attempts to develop relevant tools to detect them have been attempted, with variable degrees of success.
  • Furthermore, the digital behemoth unveiled a new tool that can detect doctored information and ensure readers of its veracity.
  • This article explains a few easy strategies and browser plugins for detecting deepfakes and AI-generated text.
  • Binghamton University and Intel researchers developed a method that goes beyond deepfake identification to identify the deepfake model behind the hacked video.

GPT-3 Philosophers (updated with replies by GPT-3)

This is a fascinating thinking piece in which nine philosophers go into Open AI’s GPT-3. It’s not only a matter of correcting the linguistic biases that have arisen (or used in training.) This is an intriguing thinking article from Daily Nous, in which nine philosophers delve into Open AI’s GPT-3.

  • It isn’t a case of discovering a technological panacea to eliminate bias.
  • The thought leaders ponder the ethical and moral challenges that technology may raise, as well as the remaining questions that it may raise.

Bridging The Gap Between Training & Inference For Neural Machine Translation

This paper is one of the top NLP papers that published from the premier conference, Association for Computational Linguistics (ACL). Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words.

  • This paper bridging The Gap Between Training & Inference For Neural Machine Translation talks about the error accumulation.
  • The researchers certainly addressed such specific problems by sampling context words, not only from the ground truth sequence. But also from the predicted sequence particularly by the model during training, whereas the predicted sequence is technically selected with a sentence-level optimum.
  • In this paper, they address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence.
  • According to the specific researchers, this approach can technically achieve significant improvements in multiple datasets.

The Matrix Calculus You Need For Deep Learning

This document attempts to teach all of the matrix mathematics required to comprehend deep neural network training. Using the automatic differentiation built into modern deep learning libraries. This certainly explains how to become a world-class and relevant deep learning practitioner with only a basic understanding of scalar calculus.

  • They presume you know nothing about arithmetic beyond what you studied in calculus 1 and provide resources to assist you refresh your math skills if necessary.
  • This material is for those who are already familiar with the basics of neural networks and want to deepen their understanding of the underlying math.

You do not need to understand this material before learning to train and use deep learning in practise; rather, this material is for those who are already familiar with the basics of neural networks and want to deepen their understanding of the underlying math.

Final lines

We hope that these articles and instructions on natural language processing and NLP and Deep Learning helped you keep up with some of the major developments in machine learning this year. Increased focus with NLP and Deep Learning means more internet materials are available. But a good article is sometimes required to gain a solid understanding of such a complicated and multi-faceted subject. Articles can help you improve your overall data literacy by providing basic background information, such as an introduction to deep learning and natural language processing (NLP) or clarification on significant ideas and real-world illustrations very well. Keep growing, my fellow members of the A.I. community.

Different Job Roles After A Data Science Course

Data Science Is Not The Future; It Is The Present!

Data science has existed since the 1990s. However, its significance was only realised when firms were unable to make decisions based on massive amounts of data. Most firms out there collect and analyze a large amount of particular data in their everyday operations in this age of technology and today we will discuss different job roles after the data science course.

Data science has aided firms in expanding beyond the traditional data aggregation rules. Data is exchanged in practically every encounter with technology. It quite enables organizations to have access to more and more specific information and so also allows seeing new things in a finest and better way, from a different perspective. The role of a data scientist is to evaluate this data and interpret the conclusions in order to put them into practice for organisational advantage. Apart from data scientists, there are many other different job roles that you can get after completing a data science course

Data Scientists not only play a vital key role in business analysis, but they are also responsible for building relevant data products as well as software platforms. Data Science encloses many breakthrough technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and Deep Learning to name a few. Data science is, in fact, a mix of computer science, statistics, and mathematics. Data science’s advances and technological advancements have increased its impact across all industries. With advanced technologies, different job roles have been generated which you can check further related to the data science courses.

Considering all this, it is a good idea to think of a career in this dynamically expanding industry. The article below simply discusses the scope and job opportunities out there in the field of Data Science.

Why choose Data Science?

Every day, around 3.6 quintillion bytes of data are processed and generated in the modern world. The volume of data has increased as contemporary technology has facilitated the creation and storage of ever-increasing amounts of data. A data scientist can gather and analyze this massive amount of relevant data in such a way that it can be used to run a lucrative business. The tremendous amount of data collected and saved by modern technologies has the potential to revolutionise businesses and communities all around the world, but only if we can comprehend it. That’s where data science and its world enters into the picture.

Do you know why data science is in high demand: Different Job Roles?

This is a simple question with a simple response. Experts in data science are quite required in practically every industry out there, from government security to dating apps nowadays. Millions of businesses and government agencies rely on big data to flourish and better serve their customers. When evaluating whether or not a job in data science is right for you, it’s more than just a question of whether or not you enjoy dealing with numbers.

  • Data science jobs are in very high demand nowadays, and this trend is unlikely to change in the near future, if at all.
  • Businesses and industries are now embracing the potential of the particular data rather than relying on age-old data calculating approaches as well.
  • It’s all about generating and determining whether you enjoy working on complex, those confusing situations and whether you have the particular and needy talent and perseverance to expand your skillset.

Pursuing an advanced degree programme in your field of interest is one method to gain such abilities and expertise. Regardless of the vertical, the massive digitization of promotion platforms is increasingly based on data insights. With zillions of bytes of data generated every day, the role of data scientists is so vital and critical, as they are certainly responsible for providing intelligent and specific solutions to help their businesses make better decisions and grow as well.

Data Analyst

Data analysts are responsible for a wide range of duties, including data visualisation, munging, and processing. Although not all data analysts are junior, and compensation can vary greatly, this is often regarded as an “entry-level” role in the data science industry.

  • They must also run different queries against particular databases from time to time. Optimization is one of a data analyst’s most significant talents.
  • The primary responsibility of a data analyst is to examine corporate or industry data and use it to answer business issues, then convey those answers to other departments within the organisation for action.
  • This is due to the fact that they must develop and modify algorithms that can be utilised to extract data from some of the world’s largest databases without causing data corruption.
  • Data analysts frequently collaborate with a range of teams inside a firm over time; for example, you might focus on marketing analytics for one month and then help the CEO utilise data to uncover reasons for the company’s growth.

Infographics explaining about job roles after a data science course

Data Scientist

Data scientists must technically comprehend business difficulties and doubts as well as provide the finest and better solutions through data analysis and solve it.

  • Many of the same tasks are performed by data analysts, but data scientists additionally use machine learning models to generate and analyze accurate predictions about the future based on historical data.
  • A data scientist has more leeway to experiment and explore their own ideas in order to uncover fascinating patterns and trends in the data that management may not have considered.
  • They can also do so by spotting different trends and specific patterns that might aid businesses in making better and finest judgments.

Lets us check other different job roles in which you can upgrade your career after data science course.

Data Engineers

The data infrastructure of a specific corporation is certainly managed by a data engineer. Data engineers create and test scalable Big Data ecosystems for businesses so that data scientists may run their algorithms on robust, well-optimized data platforms.

  • Their job necessitates a lot more software development and programming competence than statistical analysis.
  • To boost database performance, data engineers also update existing systems with newer or improved versions of current technologies.
  • A data engineer may be in charge of designing data pipelines that transmit the most up-to-date sales, marketing, and revenue data to data analysts and scientists quickly and in a usable format in a corporation with a data team.

Machine Learning Engineer

Engineers who specialise in machine learning are in high demand right now. Between a machine learning engineer and a data scientist, there is a lot of overlap. However, the work profile has its own set of difficulties.

  • Aside from having extensive knowledge of some of the most powerful technologies, the different relevant term simply refers to a data scientist with machine learning outcomes.
  • Regardless of the specifics, almost all machine learning engineer positions will necessitate at least data science programming skills and a somewhat deep understanding of machine learning algorithms.

Data Architect

Basically, this is a sub-field of data engineering for people who want to be in control of a company’s data storage systems. A data architect builds data management plans so that databases may be readily connected, consolidated, and safeguarded with the greatest security methods possible.

  • SQL abilities are a must for this job, but you’ll also need a strong command of a variety of other tech skills, which will vary depending on the employer’s tech stack.
  • They have nothing but the most up-to-date and new modernized tools and with those systems with which to operate.
  • Although you won’t get hired exclusively on the basis of your data science talents, the SQL skills and data management knowledge you’ll gain through mastering data science make it a position worth considering if you’re interested in the data engineering side of the organization.


A statistician, unlike a data scientist, will not be expected to know how to develop and train machine learning models. As the name implies here, a statistician is finite well-versed in statistical theory as well as in data organisation. Before the keyword data scientist was invented in this era, then it was 1st referred to as “statisticians.”

  • They not only extract and give valuable and particular insights from data clusters, but also they help the different department developers design new techniques.
  • The skills necessary vary greatly depending on the job, but they always require a solid grasp of probability and statistics.

Business Analyst

Business analysts have a slightly distinct role from other data scientists. The word “business analyst” refers to a wide range of positions, but in the broadest sense, a business analyst assists firms in answering questions and solving problems.

  • They understand how data-oriented technologies function and how to handle massive volumes of data, but they also technically know how to distinguish and analyze high-value data from low-value data.
  • However, many business analyst roles certainly involve the analyst collecting and making suggestions based on a company’s data, and having data skills will almost certainly make you a more appealing candidate for nearly any business analyst position.
  • To put it another way, they figure out how Big Data can be linked to valuable business insights to help companies grow.

Market Research Analyst

Promote research experts to analyse customer behaviour to assist firms in determining how to design, market, and commercialise their services. To review and improve the efficacy of marketing campaigns, marketing analysts examine sales and marketing data.

  • Several market research analysts work for consulting businesses that are employed on a contract basis.
  • Market research experts gather and analyse data about customers and competitors.
  • Analysts of market research technically do examine different market dynamics to forecast future product or service sales as well.

In addition, a marketing analyst whose research has a big influence can aim for a Chief Marketing Officer post, which earns an average of $157,960 per year. They assist businesses with identifying and producing things that people desire.

Database Administrator

Working for financial and medical institutions, social media firms, research institutes, legal firms, and other organisations.

  • A database administrator’s job description is fairly self-explanatory: they are responsible for the proper operation of all of an enterprise’s databases.
  • They also do work like backup and restore.

Final Words

In an unpredictable world, data is more vital than ever. Data science has been applied in practically every area in recent years, resulting in a strong 45 per cent increase in total data science-related employment or different job roles related to data science. Businesses will be searching for personnel with data science and analytical abilities to assist them to maximise resources and making data-driven choices as they continue to evolve. The growing prominence of data scientists in the data analyst career path will indicate data science’s future potential and will generate different job roles.

Learnbay has a path for you whether you want to learn about data science for the first time, obtain valuable analytics skills that can be used in a variety of sectors or earn a degree. It’s no wonder that Data Science professions are becoming increasingly popular, thanks to high compensation and intriguing work. Our programmes ensure that you obtain the needy skills to develop a rewarding career. You can choose different job roles related to data science after studying from Learnbay which is considered as best institute of data science.

Know The Best Strategy To Find The Right Data Science Job in Delhi?

Data science careers are buzzing everywhere, and so the data science courses. It’s true that data science salaries are too lucrative and offer sample scopes of career growth. But the majority of candidates struggle a lot to grab the right data science job after competing in their data science courses. After Bengaluru, Mumbai, Hyderabad, and Chennai, Delhi will be the next promising destination for data science aspirants. In this blog, I’ll discuss the best strategy for grabbing the right data science job in Delhi and a brief understanding of the growth orientation of the data science salary in India.

Is data science a good career in India?

We always keep our concerned eyes on the 1st world countries job market and keep regretting the lack of opportunities in our own country. In some cases, this becomes a very hard truth that our country lacks job opportunities and growth, but if it comes to data science, then India is also proudly participating in the data science advancement race.

According to the Analytics Insight survey, by the mid of 2025, India will experience a huge data science job boom. It’s expected that the number of data science and associated job vacancies at that time in India will be around 1,37,630. The Indian job market has already experienced massive demand for a data scientist in the first phase of 2021. Even after the pandemic effect, 50,000 data science, AI, and ML job vacancies have been filled from 2020 January to May 2021. So, there is no confusion that the data science discipline is holding a promising option as a future proof career in India.

What is the data science salary in India?

According to the data available in Glassdoor (as of June 15, 2021), the average data scientist salary in India have already reached the figure of 10,00,000 INR/ year with a lower limit of 4,00,000 INR/ year (freshers) and a higher limit of 20,98,000 INR/ year (for senior-level). In the case of the other subdomains of data science, such as machine learning engineers, AI experts, deep learning experts, India’s companies offer more lucrative packages.

And not only the MNCs but SMEs are also stepping forward to invest in sky-high salary packages for data science professionals.

Is data science in demand in Delhi?

Now let’s enter into our core topic. What is the position of data science skill demand in Delhi?

According to the Linkedin job search, including all sub-domain like ML, AI, data analytics, etc., around 2000, data science jobs are now available in Delhi. At the same time, Naukri has listed an additional 4800 data science job approx.

If you search for the salary insight of data science in Delhi, then you will land on a result that indicates the average yearly salary of 10,10,000 INR. While for senior roles, the figure easily reaches 16,31,000 INR. (Source: Glassdoor Salary insight).

Which companies keep hiring a data scientist around the year in Delhi?

Below are the companies that keep hiring data science professionals of different expertise levels throughout the year in Delhi.

These are the top companies of Delhi location that offer lucrative salaries and career opportunity growth and keep recruiting a data scientist (not in bulk) 365 days a year. Apart from these, there are plenty of other options for data scientists and ML engineers in Delhi.

To find the right data science job in Delhi?

Delhi is indeed growing very rapidly in terms of job opportunities but compared to the three prime locations, Mumbai, Bangalore, and Hyderabad, digging out the opportunities is a bit hard in Delhi. But that does not mean the capital of India lacks data science job opportunities. Rather, if you follow the right strategy of job searching, you can land on the best data science opportunities in this location of India.
Let’s explore the 6-step data science job searching strategy to grab the first data science job in Delhi.

  1. Target the right Job title
  2. Typing ‘data science job’ in the job search bar and hitting ‘enter’ is the biggest and most common mistake related to the data science job search.

    The keyword ‘Data science’ indicates the entire data science domain, but while searching for a job, you need to focus on specific job roles like

    • Data scientist
    • Data analyst
    • Machine learning engineer
    • AI expert
    • Business intelligence analyst
    • Marketing data analyst
    • Database administrator, etc.

    To land on the appropriate list of available job opportunities, you need to target your job title first.
    Apart from this, to make sure your profile gets shortlisted for the interview, check the job description and skills required section before applying. Applying randomly doesn’t increase the chances of getting a job. Rather continuous rejection due to relevant skill lack might discourage you.

  3. Don’t roam across different domains.
  4. The Data science job field is highly domain-specific. Even for freshers candidates, it is always recommended to study data science, keeping a specific domain in mind.

    At present, about 70% of data science candidates remain associated with career switch. Even such candidates are very high on demand. But why so?

    Well, data science is not a completely new domain. Rather, it’s such a discipline that introduced magical, rapid, and sky-kissing advancement across all types of industries like BFSI, Health and Social CareMarketing and sales, FMCG, and so on.

    Hence every data science job roles demand appealing domain expertise in terms of

    • Core working concept
    • Domain-specific business theories and postulates
    • Customised working strategies
    • Dynamic trends
    • Special skills like extremely proficient time management or highly polished communication skills, extraordinary negotiation skills etc.

    In case you switch for the domain, then you will lack in the above-mentioned expertise aspect, which seems too harmful to your data science career initiation. Hence Stick to your domain and target for an associated data science job role.

    For example, you have been working in the FMCG industry as a marketing executive. While switching to a data science career, your target should be securing a marketing data analyst or BI analyst career only in FMCG companies.

  5. Invest sufficient time in making your online portfolio and CV
  6. No matter how credible your skill sets are or how unique your capstone project. The shortlisting for your CV, as well as visibility of your online portfolio to the right recruiters and talent acquisition team itself, undergoes several data analytics.

    Yes. Starting from possibilities of your profile view to resuming selection includes automated keywords matching processes. The associated AI-powered data analytics tools select the profiles based on keyword research. Hence to ensure the higher chances of your profile visibility and resume selection, you need to describe your skill sets and domain experience using the exact keywords that recruiters use. While making the online profile and portfolio, keep the following things in mind.

    • Make your profile to the point.
    • Mention only those skills that are relevant to your targeted job role and you own in reality. (always be loyal in this regard).
    • Keep it more important to list your working experience, hands-on achievements rather than academic achievements.
    • Mention your project in the resume briefly and provide an elaborated (but to the point) description of the same in your project portfolio.
    • Your online resume must contain information about your specific requirements such as location, work-timing, etc.
    • For insane, as you are searching for a data scientist job in Delhi, set the preferred location as Delhi only. This will help you to find a customised job opening based on the Delhi location.

  7. Don’t be conventional regarding job board choosing
  8. What are the first few names that come to your mind while someone discusses a job search? Linkedin, Naukri, Glassdoor, Indeed, etc. Right?

    No doubt these are the most popular and exposed job searching platforms, and securing the right job from such a platform, especially when you are going to grab your first data science job, will be too tough. As mentioned, these platforms are extensively exposed, so the competition per job post remains too high. Such platforms are a better option for the expertise and senior-level candidates. So, are there no chances for data science new bees like you?
    Well. Now I am going to tell you the biggest secret that most data science aspirants don’t know.

    The field of data science has its own dedicated job boards, where you can find the right job as per your domain specifications, locations, and years of working experience. Even the majority of MNCs nowadays have stopped using generic recruiting sites like Linkedin, Naukri for filling up their various data science positions. Rather, they post their vacancies on the job boards dedicated to data science. Below are a few examples of such job boards.

    • Outer Join
    • Analytics Vidhya
    • Kaggle Jobs
    • Github Jobs

    Apart from these sites, parallelly, you need to keep your eyes on the dedicated career portals of your targeted companies. The best options in this regard are to join the Linkedin and other social media groups of those companies. You can even find location-specific groups too.
    Such groups will provide you with the present as well as upcoming data science opportunities of respective companies.

  9. Target the designation as per your experience level
  10. Switching to a data career does not mean initiation of a fresh career restart. Rather, it is a kind of career up-gradation.

    So if you are already at the leadership level, then don’t target for a normal BI analyst, marketing analyst role. Rather target for leadership and managerial level in the data science field too.

    At present, data science is offering equal opportunities to all aspirants from variable working experiences. And especially in the case of leadership positions, the data science domain is suffering from a talent shortage. So to land on the right job that you actually deserve, target the higher or at least the similar level designation.

    But keep in mind to grab the right job, you need to be very cautious from the initial state of your data science career transition trajectory. The data science course you choose must be according to your experience level. This is the key to grab the right data science job at the earliest.

So, what’s next?

If you need personalised career guidance for a data science career switch, you can contact Learbay. We are providing data science IBM certified AI, ML, BI analyst and other data science courses in Delhi.
Each of our course modules is designed according to the work experience and domain experience of the candidates. Instead of providing generalised data science training, we have different courses for candidates with different degrees of working experience. Not only that, all of our courses include a live industrial capstone project that will be done directly from any product based MNCs in Delhi.

To know more, get the latest update about our courses, blogs, and data science tricks and tips, follow us on: LinkedIn, Twitter, Facebook, Youtube, Instagram, Medium.

Investing 3 lakhs on Data science Certification Course? Is it really worth it

Should a working professional invest 2-3 lakhs on Data science Certification Course?

The world of data science comes with endless possibilities. With the advancement of time the scope of data science career is getting extremely rewarding. Data scientists, artificial intelligence and machine learning engineers are high in demand. Not only the fresher, but also the working professionals are becoming crazy about data science career transition. The craze has reached such a level, where professionals are ready to invest 2-3 lac in pursuing data science courses or its certifications.
Are you also going to do the same? If so, then please hold back your application for a few minutes and read this post, then decide.
Nothing is wrong in investing in data science career transformation. Rather, it’s an intelligent decision but doubt comes with the investment amount. 2 to 3 lakhs. Is this investment really worth it? Certainly, ‘no’.
Certification is the key for a successful career switch to data science career switch: Myths Vs Facts.
Lots of certification, master degree programs on data science advertisements comes throughout the professional network sites, social media sites, and rode-side hoardings. Massiveness of data science course promotions are making everyone believe that certification is must to shift your domain into data science.
But the fact is this is nothing but a myth. Yes, as a working professional, certification can never be the entry gate of your data science career. Instead, at this ‘level ‘hands on experience’ becomes the key to your data science career.
Is a data science course or certification a complete waste?
The answer is ‘yes’ and ‘no’ at the same time.
Getting confused?
Well, let me explain.
Perusing a data science course is too worthy if it makes you competent in the data scientist Job market . But the same becomes a complete waste of money if it makes you only knowledgeable, not job ready.
Remember, you are going to shift your career toward the data since domain, not starting a new career.
Your goal is to get a hike not getting an entry level job in the data science domain. So, to ensure the maximum possible return on investment, choose such a course of certification that makes you a successful competitor of the current data science job market.
How to choose the right data science course for you?
To choose the right course you need look into following aspects:

    • Course Curriculum: There is no defined, universal module for data science certification/ Master degree program. Every institution and universities build up their own course on the basis of contemporary market demands and upcoming scopes. So, you should be very cautious while choosing such a course.
      Check out for the course that offers in-depth learning options for programming languages and analytical tools like python, R, java, SAS, SPSS, mathematical and statistical modules like numpy, pandas, Matplotlib, and algorithms on demands. As you are at the intermediate level of your career, dive deep into the programming and algorithm.
      The basic courses of data science remain limited to the entry level projects and data analysis. So as a professional choose such a course that includes k-means algorithm, word frequency algorithm for NLP sentiment analysis, ARIMA model associated with machine learning, Tensorflow, CNN associated with deep learning.

    • Timing and class type: Being a working professional, it’s obvious that you can’t opt for full time courses. So choose courses that offer flexible timing. Live classes (online/offline) are always best but if it’s impossible to commit for scheduled classes, then choose a flexible one that offers both recorded and live classes options. If you enjoy offline learning choose courses offering weekend classes. But keep in mind, your learning should not hamper your present job.
    • Project experience: If your chosen course is not offering any real-time data science project option, immediately discard it. Companies only search for candidates having hands-on project experience. As a working professional, experience is everything for your next job. Some institutions let you practice your data science skills on a few completed projects. Be cautious in this regard. Before joining any data science course verify the offered projects are real time or not. Choose only that course, where you will get to work on hands-on industry projects. No matters if the projects are from MNC or startups. If you can manage time then choose a course with a part-time internship.
    • Throughout assistance: Being a dynamic field, data science needs more personalized assistance. As there is no domain limitation in data science, your chosen course must fit your targeted domain. Doing an investment on a generalized course is nothing but wasting your hard earned money. A valuable data science course assists you with domain specific interview questions, mock tests, and interview calls from growing companies.
    • Certification/ non certification courses: As mentioned earlier, certificates become only a decorative entity for a working professional’s CV. So don’t run after certification courses, rather you can choose any non certification course that really benefits your next job application in the field of data science. If you are already working in a core technical domain and own an impressive amount of python, R, java, etc, then you can choose a specific course like Tensorflow, a machine learning algorithm that will fill up the gap between your current job and targeted data science jobs.

How much money should you invest in a data science course?
Here comes the final answer. Up to 80k INR investment is fair enough to crack a promising career transformation. Yes, it’s true. Because, the main goal of doing a data science course is to upgrade your current experience to such a stage that will let you enter into the world of data science with a good hike.
You don’t need to master every subdomain of data science, in fact it’s impossible. Rather you need to learn and up-skill yourself in the data science subdomain of your interest or offer huge possibilities with respect to your present experience….and yes, again, the first priority of real-time industry projects.
Fulfilling above criteria doesn’t need investments of 2 to 3 lacs INR. Rather, plenty of promising and reliable online and offline courses are available that can make you highly competent in the data science and AI job market by investing 40k to 90K INR.
You can check Data science and AI courses offered by Learnbay. They offer customized courses for candidates of every working experience level. Their courses cost between 59,000 INR and 75,000 INR (without taxes). The top most benefits for their courses are multiple real-time industry projects with IBM, Amazon, Uber, Rapido, etc. You will get a change to work on your domain specific projects. They offer both in class (online/offline), and recorded session video classes.
Best of Luck ☺.

Data Science for working professionals

To secure a job in any domain one has to give it a lot of preparation, should be trained for the role and should have absolute knowledge about the field, usually people will dedicate years in preparing for their desired roles. Shifting from a prepared role of domain to a different domain will not usually be easy, strong gust of skepticism would surely haunt. The process of shifting from one domain to another is hard, it gets harder to learn data science for working professionals because they will have to prepare for the new job role while maintaining their current one.

If and only if you plan the whole process of domain shifting in an organised and rational way, you can have a win-win situation.

Have a vision and plan your strategy

You must win in both the games of learning and working, for that you will have to strategize in such a way that your time in learning data science should not in any way collide with your work life and vice-versa. Because both of the activities are equally important as they require immense attention and individual preference.

let us start from the scratch, here are some possible concerns of a working professional:

  1. Time management
  2. Balancing the energy between two activities
  3. Scheduling
  4. Risk of affording a wrong move
  5. Risk of inefficient or improper execution

As a working professional you will have to manage your responsibilities in a way that you will have control over every single thing that happens to exist. With proper planning and the right way of approach, the above mentioned concerns could be easily tamed.

Firmly state your purpose of learning data science
Why do you want to change your domain into Data Science while you already have a job? firmly define the purpose. You should know that by shifting to data science everything will change, you will have to develop new skill sets for the role that you are targeting, processing of workflow will be different, your future job role will have different goals, purpose and aim. Act consciously when you are risking to give up on the comfort and expertise you have in your current job, be very sure about the purpose of doing so. Doing this will eliminate the skepticism about the risk of getting out of your comfort zone. The efforts that you put over learning Data Science will never go in vain because you will learn about the currently trending technologies and tools, that will help you survive not only in data science but anywhere in the IT firm.

Have a soft target
People think only the role of ‘data scientist’ matters the most but the fact is that there are several other roles in data science which significantly matter in the field, choose one role that which you want to become and start preparing for it. Doing this should be good for the starters, because you do not have to be a scholar in every tool that has ever been used in the field, smartly target those topics that are the essentials in Data Science. When you specifically work on a targeted role you will have the chance to completely know about it and its importance in the field. This way of approach will be a very smart move because you will not be confused regarding what exactly to study in the vast field of data science and the field generally prioritizes those who holds master expertise in specified field. So be very sure about the role you want to serve in, in data science.

Plan the execution
To perfectly plan the execution part you will first have to design the implementation part, do it wise and rationally. Revise your daily-life activities, reschedule it for the sake of balancing between learning and working.

Exercise on the way you spend time on everyday things, revise it according to your daily schedules. Practice to make a note of your tasks everyday, according to that plan on how much time you would invest on the things and try your best to act as decided. In other words, this way of dealing with the things is called as discipline, to have a structured day you will have to practice discipline in all possible ways. Revise your activities from sleeping habits to break sessions, reschedule them in such a way that the things will itself fall in the right place. Set targets, set your own deadlines and design the way that you want things to work in.

Networking and understanding the field
Involve with the people that come from the field of Data Science, know about the insider story of the field and about how it works. Having field knowledge is very much necessary, remember that when you get into data science you will have to work in teams, so practice skills in communication and confidence. Get interactive with the people by asking them about the ways to reach to the field, this way you will build good connections and will get great suggestions as well. Start associating yourself with the people who belongs to Data science, you will need to get used to that.

A good course
Everything that you do and every effort that you put is only to learn Data Science, but if you make the mistake of choosing a wrong course every effort of yours will go in vain. Your purpose of learning Data Science is to shift your domain into that of Data science, you cannot do this without the help of a good course. The course that you choose should not only help you to have fine knowledge in data science but also should help you to manage your planned schedules. There are many data science courses that are specially built for working professionals, it will greatly help if you choose the right one among them.

With the right approach and proper planning you can triumph in learning Data Science while maintaining a full time job. Stick to your plans and preparations, seek help from a good course, practice as much as you could and start involving yourself with the field. If you manage to everyday execute the plans you will surely reach your destination in ease.

Learnbay could help you
The data science course of Learnbay is specially designed for working professionals, the benefits provided in the course will help you balance your scheduling. Learnbay powered by IBM will help you throughout the journey of learning and experiencing data science.

Win the COVID-19

If you slightly change your perspective towards the lock-down situation you can find hope of this pandemic to end and can hope of a brighter than ever future. Go for Data Science, it will be worth it.

Text Stemming In NLP

Human language is an unsolved problem that there are more than 6500 languages worldwide. The tons of data are generated every day as we speak, we text, we tweet, from voice to text on every social application and to get the insights of these text data we need technology as Text Stemming In NLP. If you know there are two types of data are there one is structured and unstructured data. Structured data used for Machine learning models and unstructured data is used for Natural language processing. There are only 21% of structured data is available, so now you can estimate how much Text Stemming In NLP is required to handle unstructured data. 

To get the insights of the dataset of unstructured data to take out the important information from it. The important technique to analyze the text data is text mining. Text mining is the technique to extract useful information from the unstructured data by identifying and exploring a large amount of text data. Or we can say that text mining is used to convert the unstructured data to the structured dataset.

Normalization, lemmatization, stemming, tokenization is the technique in NLP to get out the insights from the data.

Now we will see how text it works?

Stemming is the process of reducing inflection in words to their “root” forms such as mapping a group of words to the same stem. Stem words mean the suffix and prefix that have added to the root word. It is the process to produce grammatically variants of root words.  A stemming is provided by the NLP algorithms that are stemming algorithms or stemmers. The stemming algorithm removes the stem from the word. For example, eats, eating, eatery, they are made from the root word “eat“. so here the stemmer removes s, ing, very from the above words to take out meaning that the sentence is about eating something. The words are nothing but different tenses forms of verbs.

Text stemming example

This is the general idea to reduce the different forms of the word to their root word.
Words that are derived from one another can be mapped to a base word or symbol, especially if they have the same meaning.

As we can not sure that it will give us a 100% result so we have two types of error in stemming they are: over stemming and under stemming.

Over stemming occurs when there are too many words have cut out.
This could be known as non-sensical items, where the meaning of the word has lost, or it can not be able to distinguish between two stems or resolve the same stem where they should differ from each other.

For example, take out the four words university, universities, universal, and universe. A stemmer that resolves these four stems to “Univers” that is over stemming. It should be the universe stemmer that stemmed together and university, universities stemmed together they all four are not fit for the single stem.

Under stemming: Under-stemming is the opposite of stemming. It comes from when we have different words that actually are forms of one another. It would be nice for them to all resolve to the same stem, but unfortunately, they do not.

This can be seen if we have a stemming algorithm that stems from the words data and datum to “dat” and “datu.” And you might be thinking, well, just resolve these both to “dat.” However, then what do we do with the date? And is there a good general rule? So there under stemming occurs.

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning,Text Stemming In NLP, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

Normal and Gaussian Distribution

Gaussian Distribution

Gaussian distribution is a bell-shaped curve, it follows the normal distribution with the equal number of measurements right side and left side of the mean value. Mean is situated in the centre of the curve, the right side values from the mean are greater than the mean value and the left side values from the mean are smaller than the mean. It is used for mean, median, and mode for continuous values. You all know the basic meaning of mean, median, and mod. The mean is an average of the values, the median is the centre value of the distribution and the mode is the value of the distribution which is frequently occurred. In the normal distribution, the values of mean, median, and are all same. If the values generate skewness then it is not normally distributed. The normal distribution is very important in statistics because it fits for many occurrences such as heights, blood pressure, measurement error, and many numerical values.

Histogram for normal distribution

A gaussian and normal distribution is the same in statistics theory. Gaussian distribution is also known as a normal distribution. The curve is made with the help of probability density function with the random values. F(x) is the PDF function and x is the value of gaussian & used to represent the real values of random variables having unknown distribution.

There is a property of Gaussian distribution which is known as Empirical formula which shows that in which confidence interval the value comes under. The normal distribution contains the mean value as 0 and standard deviation 1.

Empirical formula

The empirical rule also referred to as the three-sigma rule or 68-95-99.7 rule, is a statistical rule which states that for a normal distribution, almost all data falls within three standard deviations (denoted by σ) of the mean (denoted by µ). Broken down, the empirical rule shows that 68% falls within the first standard deviation (µ ± σ), 95% within the first two standard deviations (µ ± 2σ), and 99.7% within the first three standard deviations (µ ± 3σ).

Python code for plotting the gaussian graph:

import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import math
mu = 0
variance = 1
sigma = math.sqrt(variance)
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)
plt.plot(x, stats.norm.pdf(x, mu, sigma)) gaussian graph

The above code shows the Gaussian distribution with 99% of the confidence interval with a standard deviation of 3 with mean 0.

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.


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