Data Science Course Syllabus: What to Expect to Learn

India’s data science education market is poised to grow faster than the global average as its benefits, including data-driven business decisions, data-backed innovation, and improvements to cost containment and efficiency, gain increasing traction across all industries. The country’s business leaders increasingly rely on data to help them make informed business decisions and the demand for skilled data science professionals only continues to grow. 

If you’re interested in gaining a job as a data professional, the first step is to complete the required data science courses to develop key skills. Discover what you can expect from a data science course syllabus and explore why learning data science is so essential.

What is data science?

Data science uses mathematical and statistical tools and models to study data and derive insight from it. It’s a robust practice involving gathering, cleaning, organising, and analysing data. Data science professionals handle immense sets of raw data and use it to identify trends and patterns, which help inform future strategies.

Why is data science important?

This vital field provides the insights necessary for businesses to make data-driven decisions, which are critical for saving money, promoting growth, and creating more efficient processes. The value of data science will continue increasing due to businesses' growing reliance on technologies such as the internet of things (IoT), artificial intelligence, and digital media platforms. With substantial earning potential and quickly evolving technologies requiring skilled data professionals, data science has also emerged as an in-demand career choice. 

Data science course syllabus basics

Developing your knowledge and skills is vital to building a solid foundation for any data science career, starting with a data science course, which provides the key to helping you develop a robust understanding of the fundamentals and developing your data science skills to ultimately gain employment in the data science field. You might expect to see these basics on a data science course syllabus:

Data engineering

Data engineering involves building pipelines and transporting vast amounts of raw data to storage. It also deals with collecting and managing that data. In data engineering classes, you’ll learn how to create and implement the necessary systems and infrastructure to handle vast data sets. 

Data visualisation

Data visualisation uses charts, graphs, and visual models to communicate complicated data trends or relationships in ways that make it easy for other professionals to understand. Data visualisation helps drive idea generation and empowers others to gain helpful insights from data by illustrating data trends and patterns.

Statistical analysis

Statistics play a vital role in working with data, aiding data professionals in identifying trends, patterns, and other essential insights. As you learn about statistical analysis, you will explore the two types of statistics: descriptive and inferential. Descriptive statistics involves organising and explaining data, whilst inferential statistics focuses on deriving meaning from data. You can expect to use statistical analysis for various tasks, including data classification, prediction, and resampling.

Machine learning

Machine learning is quickly becoming an indispensable part of data science, helping machines “learn” based on the data you provide them. It also streamlines the processes for analysing and gaining insights from data. Machine learning can remove the potential for bias in data analysis and identify trends that the human eye could otherwise miss. 

R, Python, SQL

As a data science learner or professional, you’ll often use programming languages like R, Python, and SQL to transform, analyse, and use data successfully. Your data science course will provide opportunities to learn and practice these critical languages and gain experience using them to gather and interpret data.

Research design

Data science is a research-driven field. Research design is like a guide for using data, providing a planned outline for collecting and assessing it. Throughout the process, you will define your methods and approaches, identify potential challenges, and provide a hypothesis you will test against the data. 

Data mining

This process uses several methods and techniques, including artificial intelligence, machine learning, and statistics, to help you analyse data to find patterns and irregularities. You can also expect to use descriptive, prescriptive, and predictive modelling to parse extensive data sets more efficiently, locate relevant, usable data, and make faster data-driven decisions. 

• Descriptive modelling looks at shared characteristics amongst past data

• Predictive modelling analyses past data to predict future results

• Prescriptive modelling leverages artificial intelligence to help support predictive analysis

Ethics and privacy

Data science courses typically incorporate classes on the ethics and privacy concerns of data science, including data governance and regulations. Having a firm grasp on these concepts is increasingly important in light of the growing use of data worldwide and in light of India’s Digital Personal Data Protection Act of 2023 and other laws enacted to encourage the ethical use of data. 

Communicating results

Data professionals need to have strong communication skills to successfully present and translate their findings in ways that are useful and easily understood by other professionals. Developing robust abilities, including active listening and speaking clearly to others, is essential for professional growth. These skills empower you to help non-technical stakeholders, leaders, and team members understand data-driven insights and the value of data analysis projects. 

Data science vs. data analytics: What’s the difference?

Data science is the broad practice involving every step of the data use process, from gathering and cleaning it to organising and analysing it. Data analytics is one of the techniques or processes within data science used to derive insight, patterns, and recommendations from a data set.

Prerequisites for learning data science

Although each programme has unique requirements, you typically have minimal requirements prior to admission into a data science course. Familiarity with specific concepts and proficiencies in areas like maths and computer programming can help support your educational success. Some of these prerequisites include:

Mathematics and statistics

Strong mathematical skills are crucial to success in a data science course. You’ll want to make sure you have a good understanding of algebra, geometry, calculus, statistics, working with graphs, and linear mapping, for example. Basic statistical knowledge and knowing how to manipulate data using statistical tools and software offer a solid foundation for taking a data science course.

Computation basics

Computational skills and a robust understanding of computer languages and programming software are necessary for success in a data science course. You’ll also want basic R, Python, and SQL knowledge.

Academic requirements

To enter a bachelor’s degree programme, you need a score of greater than 50 per cent on your exams after 12th. You should also have a strong maths, computer science, or statistics background. If you want to enter a diploma or postgraduate programme in data science, you need a bachelor’s degree in the science or engineering stream and aggregate scores of 50 to 70 per cent. Depending on the programme, you may also have to complete entrance exams like JEE Main or JEE Advanced successfully. 

Roles that require data science course work

Completing a data science course can open the door to several professional opportunities, depending on your goals. Five data science careers to consider, and the average base salary of each, according to May 2025 data, include the following:

1. Data scientist

Average annual salary: ₹12,19,000 [1]

Data scientists are professionals who analyse collected data and use it to make recommendations for data-driven decisions. In this job, you can expect to perform various tasks, including collecting and cleaning data, analysing it to identify patterns and trends, and preparing reports to present your findings. This role typically requires a bachelor’s degree in computer science, statistics, or mathematics. 

 2. Data architect

Average annual salary: ₹24,75,000 [2]

Data architects design and manage databases tailored to a company’s unique needs and goals. They work with teams of other data professionals to identify what the database should accomplish and create the necessary infrastructure based on those results. This role typically requires a bachelor’s degree, at a minimum, in an area of study like information technology or computer science. 

3. Machine learning engineer

Average annual salary: ₹12,04,000 [3]

Machine learning engineers design, build, and maintain machine learning models. As a machine learning engineer, you will focus on creating models that take in data and use automated processes to prepare it for professional use. To enter this field, you’ll likely need a minimum of a bachelor’s degree in computer science, physics, or engineering. 

4. Data engineer

Average annual salary: ₹11,00,00 [4]

Data engineers plan, build, and maintain the infrastructure needed data to organise and work with data. You’ll likely need a bachelor’s degree in data science, maths, statistics, or information technology to qualify for this position. Completing internships and certifications can also be valuable.

5. Business intelligence developer

Average annual salary: ₹6,70,000 [5]

Business intelligence developers create, build, and manage systems that collect and analyse business intelligence data. In doing so, you take an active role in helping businesses become more efficient and capable of reaching their goals. You will likely need a degree in a computer science-related field and experience in engineering or computer programming. Familiarity with business intelligence tools like Tableau and Microsoft Power BI is also a plus. 

Get started on Coursera

Data science courses are in demand amongst Indian learners because of the field’s positive job outlook and strong earning potential. Now that you know more about what you might expect from a data science course syllabus, all that’s left is to start learning. Whether you opt for on-campus experiences at one of India’s high-calibre educational institutions or online options, your data science education awaits. 

Explore the field further on Coursera with options such as Google’s Foundations: Data, Data, Everywhere. You can also build specific skills with courses like IBM’s Python for Data Science, AI, & Development or get your Bachelor of Science (Honours) in Data Science & AI from the Indian Institute of Technology Guwahati.