How to Become a Data Scientist in India

The article covers important skills and the level of expertise you must possess to crack interviews for becoming a Data Scientist in India. 

The field of Data Science involves studying data to extract relevant information from it and then using it to build machine learning and deep learning models. This is a booming career in 2022 and there is a great demand for professionals who can analyze data and uncover actionable insight that can provide enormous value to any business by stimulating creative solutions. This article will try to guide you through the entire learning process of becoming a data scientist in India. We will give you a proper run-through of the important skills and the level of expertise you must possess to crack interviews for becoming a Data Scientist. 

However, you need to keep in mind that the transformation from a newbie programmer to a full-fledged expert in data science is not going to be a cakewalk. This domain demands hours of dedicated study and practice. Rest be assured, that you will thoroughly enjoy your learning journey and find plenty of help along the way!

So, let’s get started, shall we?

We will be covering the following topics: 

• Build your Foundations
• Learn Statistics and Probability
• Learn Python or R Programming
• Perform Exploratory Data Analysis
• Build Machine Learning Models
• Work with Big Data Tools
• Explore Deep Learning Models
• Visualize your Outcome
• Get help from the right resources
• Undertake a Machine Learning Project
• Data Scientist Jobs in India
• Data Scientist Salaries in India

Build your Foundations

Before you delve into data science, you need to get your basics about data science and machine learning clear. You might already be familiar with some concepts, but a little brush-up before any big exam never hurts! So, you need to dedicate a week or two to understanding the basics to the point where you can explain them to an interviewer.

Topics of importance:

• What is Data Analytics?
• Learn about Data Science?
• What is Machine Learning?
• What is Deep Learning?
• How data science helps businesses tackle their data problems
• Applications of data science in the real-world
• What are the job roles of a Data Science expert?

Learn Statistics and Probability 

Whether you enjoy mathematics or not, it is an important skill to possess if you’re going to be a data scientist. Although you need not be a mathematician or statistician to become an expert in data science, a good knowledge of certain important concepts does work in your favor. More specifically, basic concepts of probability and statistics are a must-know.   

Topics of importance:

• Basics of Probability
• Types of Probability
• Bayes’ Theorem
• Random Variables
• Probability Distributions
• What is Sampling?
• Descriptive Statistics 
• Inferential Statistics 
• Hypothesis Testing

Learn Python or R Programming

Having great programming skills is a must-have to work on absolutely anything in this domain. Unlike common misconceptions, programming is actually not that hard to learn. All you need to do is put in a few hours consistently for at least six weeks to gain enough expertise to write well-structured codes. 

While Python is the choice of language for data science in The US and Europe, it does share its most popular language title with R in India. 

• Python for data science is used for performing data preprocessing, and analysis.
• R for data science focuses on the language’s statistical and graphical uses. 

Perform Exploratory Data Analysis 

Exploratory Data Analysis (EDA) refers to the critical process of investigating data to gain insight, discover patterns, spot anomalies, etc. through graphical representations and summary statistics.

EDA can be learned in just a couple of weeks. It is a fundamental step in data science since it allows us to get closer to the certainty that future results will be correctly interpreted. It is also perhaps the most interesting part of the entire learning process.

EDA topics of importance:

• Univariate non-graphical EDA
• Univariate graphical EDA
• Bivariate or multivariate non-graphical EDA
• Bivariate or multivariate graphical EDA
• Different types of graphs and charts – namely histograms, scatter plots, heat maps, etc.

Build Machine Learning Models

Machine Learning makes use of data and algorithms that enable machines to learn and improve from experience so that they become capable of making decisions or predictions. Machine learning imitates human learning and helps us to solve practical problems. 

As an aspiring data scientist, you will need to dedicate multiple weeks to understand and practice all the important algorithms – how to build models, train them, evaluate and optimize them to predict outcomes accurately.

Algorithms of importance:

• Supervised learning techniques – Classification and Regression
• Supervised learning algorithms
  • Linear Regression
  • Logistic Regression
  • K-Nearest Neighbours (KNN)
  • Support Vector Machines (SVM)
  • Naïve Bayes Classifier 
  • Decision Tree Classifier 
  • Decision Tree Regressor
  • Random Forest Classifier
  • Random Forest Regressor
• Ensemble models – Bagging [insert hyperlink here] and Boosting [insert hyperlink here]
• Unsupervised learning algorithms
  • K-means clustering
  • KNN (k-nearest neighbors)
  • Hierarchal clustering
  • Association Rule Mining – Apriori algorithm
  • Recommender Systems
• Dimensionality Reduction
  • Principle Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)

Work with Big Data Tools

According to The Economist, data is the new oil in the 21^st Century. 

“If data is the crude oil, databases and data warehouses are the drilling rigs that dig and pump the data on the internet.”

Data Scientists are expected to work with humongous volumes of data. Big Data tools provide the required assistance and help for mining, exploring, and analyzing all the data. 

Topics of importance:

• SQL
• Databases like MySQL
• Big Data overview and ecosystem
• Hadoop Framework – HDFS, MapReduce, Pig, and Hive
• Apache Spark
• Data Warehousing
• ETL pipeline 

Explore Deep Learning Models

“Think of deep learning as the oil refinery that finally turns crude oil into all the useful and insightful final products. ”

Deep Learning falls under the umbrella of Machine Learning. It mimics the structure and functions of the human brain through Artificial Neural Networks. Sounds interesting, doesn’t it? Here are a few examples of its applications in the real world:

• Image classification (Think of facial recognition systems like Apple Face ID)
• Speech recognition (Think of Siri and Alexa)
• Language translations (Think of Google Translate)
• Self-driving cars (Think of Tesla)

So, you get why it is a hot topic right? Deep learning is a vast domain and mastering it is an endless quest. However, you can get a basic idea of its working and start by creating simple models. Eventually, you will learn the ropes and be able to create much more complex models someday.

Topics of importance:

• What are Artificial Neural Networks?
• Single Layer Perceptron
• Multi-layer Perceptron
• Natural Language Processing (NLP)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Keras and TensorFlow
• Open CV

Visualize your Outcome 

“Humans are visual creatures.  Half of the human brain is directly or indirectly devoted to processing visual information.”

Data Visualization is a way of creating a visual display of the results using charts, graphs, etc. Visualizations allow data scientists to analyze and recognize trends and patterns in the data, thus making it easier for them to propose solutions to business problems.

Tableau is a very popular data visualization tool that is widely used for creating interactive graphs and charts in the form of dashboards to gain business insight. 

Topics of importance:

• Data Connection and Visualization using Tableau
• Storytelling using Tableau
• Sharing insights using Tableau Dashboards

Get help from the right resources

To become a data science expert, you need first-hand experience from working on actual data science projects. Project-based learning is an effective approach to ensure that you have a clear understanding and required knowledge of all the important concepts.

To help you with that, many online tutorials and courses are available to provide you with the guidance you need. You will find tutorial videos on YouTube for any topic that you wish to learn. Also, if you seek professional assistance from industry experts, you can enroll yourself in live courses that will also provide you with a certificate to add to your CV. I am listing down a few popular courses here for your reference:

• Data Science Foundations with R Specialization offered by Coursera and Johns Hopkins University. 

• Applied Data Science with Python Specialization offered by Coursera and the University of Michigan.

• Post Graduate Program in Data Science offered by Edureka and E & ICT Academy, NIT Warangal. 

• Data Science Masters Program offered by Edureka. 

• Executive Data Science Specialization by Coursera

Undertake a Machine Learning Project

Before appearing for Data Scientist interviews, make sure you have a few projects listed on your Resume. Once you’re done with your learning process, you will be ready to showcase your skills by building detailed ML models.

These programs also involve working on projects as a part of your certification. But in case you’re following a self-learning approach, you will find amazing projects on platforms like Kaggle and GitHub. You can make use of online IDEs like Google Colab to run your ML codes seamlessly and build your models.

Completing one or two projects is a necessary step as it will help you demonstrate your knowledge and skills. Hope you enjoy your journey of becoming an expert in Data Science thoroughly!

Tasks you need to perform in your project:

• Data ingestion
• Preparation of data
• Data pre-processing
• Exploratory data analysis
• Build a machine learning or basic deep learning model
• Evaluate and optimize your model
• Create a project report

Data Scientist Jobs in India

The biggest players in the data science domain who are currently hiring data scientists are listed below:

• IBM – Bangalore
• Amazon – Bangalore, Hyderabad
• Walmart – Bangalore
• Oracle – Bangalore, Hyderabad

Data Scientist Salaries in India

According to the average salary range listed on Ambitionbox, a typical data scientist in India can earn anywhere between ₹4.5 Lakhs to ₹ 25.0 Lakhs  depending upon the individual experience.

Endnotes

In this article we learnt how to become data scientist in India. Hope this article helped you find your ropes to begin your Data Science journey. Data Science has hugely impacted big businesses worldwide and has created the most job demands in the last few years. Interested in being a part of this frenzy? Explore related articles here.

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