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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. 










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.

Clustering & Types Of Clustering

Clustering & Types Of Clustering is the process of finding similar groups in data, called a cluster. It groups data instances that are similar to each other in one cluster and data instances that are very different(far away) from each other into different clusters. A cluster is, therefore, a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.

Cluster graph

The method of identifying similar groups of data in a dataset is called clustering. It is one of the most popular techniques in data science. Entities in each group and is comparatively more similar to entities of that group than those of the other groups. In this article, I will be taking you through the types of clustering, different clustering algorithms and a comparison between two of the most commonly used clustering methods.

Steps involved in Clustering analysis:

1. Formulate the problem – select variables to be used for clustering.

2. Decide the clustering procedure whether it will be Hierarchical or Non-Hierarchical.

3. Select the measure of similarity or dissimilarity.

4. Choose clustering algorithms.

5. Decide the number of clusters.

6. Interpret the cluster output(profile the clusters).

7. Validate the clusters.

Types of clustering technique:

Broadly speaking, clustering can be divided into two subgroups :

  • Hard Clustering: In hard clustering, each data point either belongs to a cluster completely or not. For example, in the above example, each customer is put into one group out of the 10 groups.
  • Soft Clustering: In soft clustering, instead of putting each data point into a separate cluster, a probability or likelihood of that data point to be in those clusters is assigned. For example, from the above scenario, each customer is assigned a probability to be in either of 10 clusters of the retail store.

Types of clustering are:

k-means clustering:

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-Means minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. Better Euclidean solutions can, for example, be found using k-medians and k-medoids.

K-Means Clustering example

K means is an iterative clustering algorithm that aims to find local maxima in each iteration. This algorithm works in these 5 steps :

  1. Specify the desired number of clusters K : Let us choose k=2 for these 5 data points in 2-D space.
  2. Randomly assign each data point to a cluster: Let’s assign three points in cluster 1 shown using red color and two points in cluster 2 shown using grey color.
  3. Compute cluster centroids: The centroid of data points in the red cluster is shown using a red cross and those in a grey cluster using the grey cross.
  4. Re-assign each point to the closest cluster centroid: Note that only the data point at the bottom is assigned to the red cluster even though its closer to the centroid of the grey cluster. Thus, we assign that data point into a grey cluster
  5. Re-compute cluster centroids: Now, re-computing the centroids for both the clusters.
  6. Repeat steps 4 and 5 until no improvements are possible: Similarly, we’ll repeat the 4th and 5th steps until we’ll reach global optima. When there will be no further switching of data points between two clusters for two successive repeats. It will mark the termination of the algorithm if not explicitly mentioned.

from pandas import DataFrame
Data = {'x': [25,34,22,27,33,33,31,22,35,34,67,54,57,43,50,57,59,52,65,47,49,48,35,33,44,45,38,43,51,46],
'y': [79,51,53,78,59,74,73,57,69,75,51,32,40,47,53,36,35,58,59,50,25,20,14,12,20,5,29,27,8,7] }
df = DataFrame(Data,columns=['x','y'])
print (df) 

k-means for cluster=3

from pandas import DataFrame
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
Data = {'x': [25,34,22,27,33,33,31,22,35,34,67,54,57,43,50,57,59,52,65,47,49,48,35,33,44,45,38,43,51,46],
'y': [79,51,53,78,59,74,73,57,69,75,51,32,40,47,53,36,35,58,59,50,25,20,14,12,20,5,29,27,8,7] }
df = DataFrame(Data,columns=['x','y'])
kmeans = KMeans(n_clusters=3).fit(df)
centroids = kmeans.cluster_centers_
plt.scatter(df['x'], df['y'], c= kmeans.labels_.astype(float), s=50, alpha=0.5)
plt.scatter(centroids[:, 0], centroids[:, 1], c='red', s=50) 
Hierarchical Clustering: 

Hierarchical clustering, as the name suggests is an algorithm that builds the hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left.

The results of hierarchical clustering can be shown using the dendrogram. The dendrogram can be interpreted as:

The results of hierarchical clustering the dendrogram.

Two important things that you should know about hierarchical clustering are:

  • This algorithm has been implemented above using a bottom-up approach. It is also possible to follow the top-down approach starting with all data points assigned in the same cluster and recursively performing splits till each data point is assigned a separate cluster.
  • The decision of merging two clusters is taken on the basis of closeness of these clusters. There are multiple metrics for deciding the closeness of two clusters :
    • Euclidean distance: ||a-b||2 = √(Σ(ai-bi))
    • Squared Euclidean distance: ||a-b||22 = Σ((ai-bi)2)
    • Manhattan distance: ||a-b||1 = Σ|ai-bi|
    • Maximum distance:||a-b||INFINITY = maxi|ai-bi|
    • Mahalanobis distance: √((a-b)T S-1 (-b))   {where, s : covariance matrix}

import numpy as np
X = np.array([[5,3],
import matplotlib.pyplot as plt
labels = range(1, 11)
plt.figure(figsize=(10, 7))
plt.scatter(X[:,0],X[:,1], label='True Position')
for label, x, y in zip(labels, X[:, 0], X[:, 1]):
xy=(x, y), xytext=(-3, 3),
textcoords='offset points', ha='right', va='bottom')


Data point plot

from scipy.cluster.hierarchy import dendrogram, linkage
from matplotlib import pyplot as plt

linked = linkage(X, 'single')
labelList = range(1, 11)
plt.figure(figsize=(10, 7))

Dendrogram plot

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.

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.

Regression techniques in Machine Learning

Machine learning has become the sexiest and very trendy technology in this world of technologies, Machine learning is used every day in our life such as Virtual assistance, for making future predictions, Videos surveillance, Social media services, spam mail detection, online customer support, search engine resulting prediction, fraud detection, recommendation systems, etc. In machine learning, Regression is the most important topic that needed to be learned. There are different types of Regression techniques in Machine Learning which we will know in this article.


Regression techniques in Machine Learning such as Linear regression and Logistic regression are the most important algorithms that people learn while they study about Machine learning algorithms. There are numerous forms of regression that are used to perform regression and each has its own specific features, that are applied accordingly. The regression techniques are used to find out the relationship between the dependent and independent variables or features. It is a part of data analysis that is used to analyze the infinite variables and the main aim of this is forecasting, time series analysis, modeling.

What is Regression?

Regression is a statistical method that mainly used for finance, investing and sales forecasting, and other business disciplines that make attempts to find out the strength and relationship among the variables.

There are two types of the variable into the dataset for apply regression techniques:

  1. Dependent Variable that is mainly denoted as Y
  2. Independent variable that is denoted as x.

And, There are two types of regression

  1. Simple Regression: Only with a single independent feature /variable
  2. Multiple Regression: With two or more than two independent features/variables.

Indeed, in all regression studies, mainly seven types of regression techniques are used firmly for complex problems.

  • Linear regression
  • Logistics regression
  • Polynomial regression
  • Stepwise Regression
  • Ridge Regression
  • Lasso Regression

Linear regression:

It is basically used for predictive analysis, and this is a supervised machine learning algorithm. Linear regression is linear approach to modeling the relationship between scalar response and the parameters or multiple predictor variables. It focuses on the conditional probability distribution. The formula for linear regression is Y = mX+c.

Where Y is the target variable, m is the slope of the line, X is the independent feature, and c is the intercept.

Simple Linear Regression in Machine learning - Javatpoint

Additional points on Linear regression:

  1. There should be a linear relationship between the variables.
  2. It is very sensitive to Outliers and can give a high variance and bias model.
  3. The problem of occurring multi colinearity with multiple independent features

Logistic regression:

It is used for classification problems with a linear dataset. In layman’s term, if the depending or target variable is in the binary form (1 0r 0), true or false, yes or no. It is better to decide whether an occurrence is possibly either success or failure.


Logistic Regression

Additional point:

  1. It is used for classification problems.
  2. It does not require any relation between the dependent and independent features.
  3. It can after by the outliers and can occur underfitting and overfishing.
  4. It needs a large sample size to make the estimation more accurate.
  5. It needs to avoid collinearity and multicollinearity.

Polynomial regression:

The polynomial regression technique is used to execute a model that is suitable for handling non-linear separated data. It gives a curve that is best suited to data points, rather than a straight line.
The polynomial regression suits the least-squares form. The purpose of an analysis of regression to model the expected y value for the independent x of the dependent variable. 
The formula for this Y=  β0+ β0x1+e
Polynomial Regression - Towards Data Science
Additional  features: 
Look particularly for curve towards the ends to see if those shapes to patterns make logical sense. More polynomials can lead to weird extrapolation results. 

Step-wise Regression:

It is used for statistical model fitting regression with predictive models. It is done automatically. 
The variable is supplemented or removed from the explanatory variable set at every step. The main approaches for the regression are reverse elimination and bidirectional elimination and step by step approaches. 
The formula for this: b = b(sxi/sy)
Additional points: 
  1. This regression provides two things, the very first one is to add prediction for each steep and remove predictors fro each step.
  2. It starts with the most significant predictor into the ML model and then adds features for each step.
  3. The backward elimination starts with all the predictors into the model and then removes the least significant variable.

Ridge Regression: 

It is a method that used when the dataset having multicollinearity which means, the independent variables are strongly related to each other. Although the least-squares estimates are unbiased in multicollinearity, So after adding the degree of bias to the regression, ridge regression can reduce the standard errors.
Ridge Regression for Better Usage - Towards Data Science

Additional points:

  1. In this regression, normality is not to be estimated the same as Least squares regression.
  2. In this regression, the value could be varied but doesn’t come to zero.
  3. This uses the l2 regularization method as it is also a regularization method.

Lasso Regression:

Lasso is an abbreviation of the Least Absolute shrinkage and selection operator. This is similar to the ridge regression as it also analyzes the absolute size of the regression coefficients. And the additional features of that are it is capable of reducing the accuracy and variability of the coefficients of the Linear regression models.

Lasso regression in matlab - Stack Overflow


Additional points: 
  1. Lasso regression shrinks the coefficients aero, which will help in feature selection for building a proper ML model.
  2. It is also a regularization method that uses l1 regularization.
  3. If there are many correlated features, it picks only one of them and shrinks it to the zero.


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, Regression techniques in 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.

Model vs Algorithm in ML

Model vs Algorithm in ML: Introduction

Machine Learning works with “models” and “algorithms”, and both play an important role in machine learning where the algorithm tells about the process and model is built by following those rules. So, let’s study further how Model vs Algorithm in ML( Machine Learning).

Algorithms have derived by the statistician or mathematician very long ago and those algorithms are studies and applied by the individuals for their business purposes.

A model in machine learning nothing but a function that is used to take some certain input, perform a certain operation which is told by algorithms to its best on the given input, and gives a suitable output.

Some of the machine learning algorithms are:

  1. Linear regression
  2. Logistic regression
  3. Decision tree
  4. Random forest
  5. K-nearest neighbor
  6. K-means learning

What is an algorithm in Machine learning?

An algorithm is a step by step approach powered by statistics that guides the machine learning in its learning process. An algorithm is nothing but one of the several components that constitute a model.

There are several characteristics of machine learning algorithms:

  1. Machine learning algorithms can be represented by the use of mathematics and pseudo code.
  2. The effectiveness of machine learning algorithms can be measured and represented.
  3. With any of the popular programming languages, machine learning algorithms can be implemented.

What is the Model in Machine learning?

The model is dependent on factors such as features selection, tuning parameters, cost functions along with the algorithm the model just not fully dependent on algorithms.

Model is the result of an algorithm when we implement the algorithm with the code when we train the algorithms with the real data. A model is something that tells what your program learned from the data by following the rules of those algorithms. The model is used to predict the future result that is observed by the algorithm implementation of small data.

Model = Data + Algorithm 

A model contains four major steps that are:

  1. Data preprocessing
  2. Feature engineering
  3. Data management
  4. performance measurement.

How the model and algorithms work together in machine learning?

For example:

y = mx+c is an equation for a line where m is the slope of the line and c is the y-intercept, this is nothing but linear regression with only one variable.
similarly, the decision tree and random forest have something like the Gini index and K-nearest having Euclidean distance formula.

So take the linear regression algorithm:

  1. Start with a training set with x1, x2,…, and y.
  2. Find out the parameters c0, c1, c2 with the random variables.
  3. Find out the learning rate alpha
  4. Then repeat the following updates such as c0 = co-alpha +h(x)-y and for c1, c2 also.
  5. Repeat these processes till converged.

when you employing this algorithm, you are employing these exact 5 steps in your model without changing the steps, your model initiated by the algorithm and also treat all the dataset same.

If you want to apply that algorithm to the model, the model finds out the value of m and c that we don’t know, then how will you find out?
suppose you have 3 variables that are having values of x and y now your model will find the value of m1, m2, m3, and c1, c2, c3 for three variables.
The model will work with three slopes and three intercepts to find out the result of the dataset to predict the future.

The “algorithm” might be treating all the data the same but it is the “model” that actually solves the problems. An algorithm is something that you use to train the model on the data.

After building a model, a data science enthusiasts test it to get the accuracy of that model and fine-tuning to improve the results.


This article may help you yo understand about the algorithm and model (Model Vs Algorithm in ML) in Machine learning and its relationship. In summary, an algorithm is a process or a technique that we follow to get the result or to find the solution to a problem.
And a model is a computation or a formula that formed as an output of an algorithm that takes some input, so you can say that you are building a model using a given algorithm.


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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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.

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