Data Science Course in India: The Complete Roadmap

  • By Bhavya Thakkar
  • 26 July 2022
Data Science Course in India: The Complete Roadmap

In the era of disruptive technologies such as Artificial Learning, Machine Learning, Neural Networking and Big Data Management, when businesses are dealing with petabytes and exabytes of data every week, the role of a data scientist offers a lucrative employment opportunity. With the newly emerging solutions to manage and derive insights from the colossal influx of data, there has been phenomenal growth in the field of data science. Besides, there is a plethora of data science course in India.

If you wish to pursue a career in data science and are wondering about the data science scope in India, this article is for you.

What is Data Science?

Data science can have multiple definitions. It is so because data science can have different meanings for different people leveraging the process on its nature, scope and core functionalities. However, on a broader note, data science can be defined as the process of analysing raw data using statistical and other quantitative and qualitative methods to derive insights from it. 

Data Science graduates focus on answering, analysing, inferring from, and critiquing different questions in any existing data set. The field of data science includes several subjects such as statistics, mathematics, computer science, data formatting, database management, and data visualisation, to name a few.

Why Data Science? (Decide the goal first)

While data science has become an increasingly popular career option for students with a bachelor of computer application or BCA degree, many other fields can also leverage the teachings of science. For instance, in social sciences, a data science candidate can emulate multiple models of public policy spheres given the raw data. In business, data scientists can help recognise patterns of consumer behaviour, track user actions, streamline internal productivity and manage large-scale employee databases. 

You have to decide whether you want to pursue data science for academic projects in your college or whether you want to approach it as a long-term career option. How you will learn and apply data science depends on your end goal. For example, suppose you want a successful data science career. In that case, you should focus on continually upskilling yourself and learning new data science processes alongside figuring out which industry suits your profile the best. With growing competition in the field, it is good to stay prepared to be ahead of the curve. 

How to learn data science?

Although data scientists hail from different educational backgrounds and have varied work experiences, they are usually proficient in four major areas. The key skills you should master to be a successful data scientist are listed in the paragraphs below:

Domain Knowledge

There is a common misconception that data science does not require the knowledge of specific business domains. This is not true. In fact, it is very important to have specific domain knowledge to understand how consumers react and the overarching vision of the business. It also helps with identifying and analysing competitors in the space. Besides, different industries have different business processes which determine how the data is going to be used and different privacy levels and data breach policies regarding the same as well.

Math Skills

Mathematics is one of the fundamental pillars of data science. Hence, maths and statistics skills are crucial for data scientist enthusiasts. The core areas in mathematics include multivariable calculus, linear algebra, and optimisation techniques. Statistics is one of the major subsets of math that is integral for data-centric research and analysis. Probability is one of the most important subjects that facilitates data science. 

Computer Science

A grasp of computer science is of great importance in the field of data science. In this regard, R and Python are the two vital programming languages which are used in this course. This is because both- languages have rich libraries to execute complex AI or ML-driven algorithms and data structures, data cleaning and data visualisation possibilities. Other than programming languages, you might have to be proficient in skills such as DBMS, Data Structure and Algorithms, Linux, MongoDB, SQL, Distributed Computing, Git, and Scala, to name a few.

Communication Skills

Both verbal and written communication skills are essential for you to be a successful data scientist in the long run. Whenever you draw insights from a set of bulk data, it has to be communicated to other stakeholders, such as your colleagues and clients, to complete your given task. Strong written and verbal skills assist you in comprehending your analysis and insights in an easy-to-understand manner. 

Key Elements of Data Science: A Roadmap to Learn

Here we will discuss the roadmap you need to prepare for a successful career in data science. First, if you have motivated yourself to become a data scientist, you must acquire the following skills:


Maths is a key skill for a data scientist. Therefore, a myriad of advanced mathematical concepts, including linear algebra, vector calculus, matrix, analytical geometry, optimisation, regression, classification, and dimensionality reduction, is of paramount importance in the field. 


If you want to master using artificial intelligence or machine learning for data sciences, it is imperative you have a good understanding of probability. You should go through an introduction to probability, joint probability distribution, 1D random variables, the function of random variables, discrete distribution, continuous distribution, and normal distribution in this respect.


Key statistical skills such as data description, random sampling, sampling distribution, parameter estimation, hypothesis testing, reliability engineering, stochastic processes, designing experiments, simple linear regression, correlation, and nonparametric statistics are integral. Alongside this, the basics of graphical representation are also of value to a data science aspirant.  


Programming concepts such as data structures and algorithms are pivotal if you want to pursue a career in data science. Languages including Python, R, C++, Java, and Scala are absolutely essential. It is also preferred if you have knowledge of database management and web scraping techniques. 

Machine Learning

Some of the key machine learning skills required in data science are basic data exploration, knowledge of model function, model validation, handling missing values and categorical variables, cross-validation, and leakages. 

Deep Learning

Data scientists should be thoroughly acquainted with deep learning concepts such as artificial neural networks, recurrent neural networks, binary classification, deep neural networks, and dropout batch normalisation. This aids in understanding old data in a new, quantitative and predictive light. 

Feature Engineering

Key feature engineering skills include concepts such as baseline modelling, categorical encoding, feature generation, and feature selection. A few of these ease the burden on any data science student and are crucial to grasp in the long run. 

Natural Language Processing

Natural Language Processing teaches you to work with text data and derive insights with processes like text classification and word vectors. This is a relatively new domain and has immense scope in India and abroad. 

Data Visualization Tools

Data Visualization is the process of representing the insights acquired by analysing data from raw data sets. Many Data Visualization Tools such as Excel VBA, Tableau, QlikView, and Power BI are changing the way data science is learnt and applied throughout the leading industries. 


Deployment is a key skill you must possess whether you are a fresher or a professional with experience. Heroku, Microsoft Azure, Django, and Google Cloud Platform are some of the major tools for deploying data science projects.

Keep Practicing

Practice is essential in becoming a successful data scientist, just like in any other field. Practising different data visualisation ecosystems, different types of data-based problems, and exploring new tools, are fundamental for your progress in the field of data science. 

Other points to learn

If you are a data scient enthusiast or aspirant, you should also focus on the following:

  • Get adequate domain knowledge in the field of your choice
  • Maintaining active and good communication skills
  • Learning the case studies of industry-leading organisations that use data
  • Stay updated on the Data Science course fees in India
  • Stay updated on the data science salary in India

Visit the official Sunstone website today for more information on data science courses. Then, get in touch with an industry-leading mentor who can answer all your queries about the field and more. 

About Sunstone

Sunstone is a leading higher education services provider that works with academic institutions in upskilling students for employability. Sunstone offers career-oriented training interventions for undergraduate and postgraduate students at 40+ institutions across 30+ cities.

Sunstone’s training programs are industry-endorsed to enhance employability. These programs are designed for hybrid delivery with an unparalleled focus on soft skills and personality development. The focus is to provide students with a holistic educational experience.

Frequently Asked Questions

Which institution is best for Data Science in India?

The Indian Institute of Technology or IIT  is the best institution in India to learn data science. However, there are many newer institutions with world-class faculty as well. 

Is data science available in India?

Several educational institutions in India teach an array of data science courses. Besides, there is an immense scope after the successful completion of a data science program in India as well. 

Are data scientists well paid in India?

Data science is a highly sought-after career. It is a challenging course with competitive pay packages offered by the top businesses in all sectors of the Indian industry.


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