What Subjects Are Covered in a Master’s in Data Science Program?

A Master’s in Data Science brings together mathematical foundations, programming, analytics, and advanced computing. But what you actually study depends completely on how the program is built, since not every data science master’s covers the same ground in the same order.

The Master of Applied Science (MAS) in Data Science, offered through edept in partnership with Mahindra University and Illinois Tech, follows a defined two-stage structure. The India Track takes place at Mahindra University and covers foundational and applied Data Science learning. The USA Track is completed at Illinois Tech in Chicago and delivers the advanced, STEM-designated MAS in Data Science curriculum.

This means students build core skills first in India, then move into advanced machine learning, AI, big data, and data strategy subjects once they reach Chicago. Here is a subject-by-subject look at what this program actually teaches, based on the official program brochure.

What Do You Study in a Master’s in Data Science?

Before looking at individual subjects, it helps to see the subject areas this program covers as a whole. Data Science, as taught in this MAS program, brings together:

• Programming
• Statistics and Probability
• Data Management
• Data Visualisation
• Machine Learning
• Cloud and Data Infrastructure
• Artificial Intelligence
• Big Data
• Data Engineering
• Data Strategy and Governance
• Applied projects

A well-rounded curriculum does not jump straight into advanced topics. It moves from fundamentals to advanced, industry-oriented application, which is exactly how this program is structured across its two academic phases.

Foundational Subjects vs. Advanced Data Science Subjects

Foundational subjects are covered mainly during the India phase at Mahindra University: programming, statistics, data handling, and an introduction to machine learning. Advanced subjects are introduced once students reach Illinois Tech, where the focus shifts from understanding data to building and applying advanced, data-driven solutions. This progression is what makes the curriculum work as one connected pathway rather than two separate study blocks.

Subjects Covered in the India Track at Mahindra University

The India Track is the first academic stage of the Global Pathway. According to the program brochure, this phase covers six core academic subject areas, plus a component of applied projects and assessments.

Programming for Data Science (Python)

Python is the programming subject that forms the base of the India Track. It builds the coding foundation needed to work with data and run analysis. The brochure lists programming exposure in Python, C++, or Java as preferred for admission, so this subject also strengthens whatever coding background a student already has.

Statistics and Probability for Analytics

Statistics and probability form the analytical backbone of the program. This subject helps students understand data patterns and supports the kind of analytical decision-making that data science work depends on every day. Statistical thinking shows up again in later subjects, from machine learning to data strategy.

Data Management & SQL

Data management is a core part of the curriculum, with SQL covered as the main tool for working with structured data. Before students can build models or run analysis, they need to know how data is stored, organised, and retrieved. This subject lays the foundation for the more advanced data subjects at Illinois Tech.

Data Visualisation & Business

This subject focuses on turning data into insights people can actually understand and use. It connects the technical side of data science with the business side, teaching students to communicate findings clearly rather than just produce numbers. The focus is on interpretation and communication, not on any particular software.

Introduction to Machine Learning

This is the first machine learning subject in the program, and it acts as a bridge. Students move from foundational analytics into the basics of machine learning, setting them up for the advanced machine learning subject covered later in the USA Track.

Cloud & Data Infrastructure Fundamentals

Modern data science does not run on a single laptop. This subject introduces the role that cloud computing and infrastructure play in handling and processing data. As the brochure describes it, this is an introductory, fundamentals-level subject that lays the groundwork for the more advanced infrastructure topics at Illinois Tech.

Applied Projects & Assessments

The India phase is not only classroom learning. Applied projects and assessments run alongside the six subject areas, giving students a chance to put what they learn into practice before moving to the USA phase. This continuous preparation is part of what the brochure describes as a seamless academic progression, since credits earned in India align with the U.S. curriculum.

Advanced Subjects Covered in the USA Track at Illinois Tech

The USA Track is the Master of Applied Science in Data Science (STEM), completed on campus at Illinois Tech in Chicago. This is where the curriculum becomes more advanced and specialised. According to the brochure, this stage covers seven major components.

Advanced Machine Learning

This subject builds directly on the Introduction to Machine Learning covered in India. Students move from basic ML concepts to a more advanced stage of learning, applying machine learning techniques to more complex, real-world problems.

Artificial Intelligence Foundations

AI is treated as its own dedicated component of the advanced curriculum, not just an extension of machine learning. This subject introduces students to what AI represents as a discipline within Data Science and how it fits into the broader field.

Big Data Analytics

This subject focuses on analysing large and complex datasets, the kind that go beyond what standard tools can easily handle. It connects directly to the need to work with data at a much bigger scale than in the India phase.

Data Engineering & High-Performance Computing

Data engineering covers the technical infrastructure built around data, while high-performance computing addresses the demanding computational workloads of large-scale data science work. Together, these subjects round out the technical, infrastructure-facing side of the program.

Data Strategy & Governance

This subject goes beyond technical model-building. It covers how data is managed, governed, and used strategically within an organisation, showing that the MAS curriculum is about handling data responsibly, not just building models.

Capstone Project / Master’s Thesis

The capstone project or master’s thesis is where students apply everything they have learned across both phases. It is a substantial, culminating academic component focused on applying and integrating concepts rather than following one fixed format.

Industry-Oriented Project Work

Illinois Tech’s program includes projects, labs, and collaborations with global employers. This subject area reflects that practical approach, connecting classroom learning with the kind of project work students will encounter in real data science jobs.

How the Data Science Subjects Progress from India to the USA

Rather than repeating the subject list, it helps to see how subjects connect across the two phases. This is really the core of the program: three skill tracks that build up gradually across both countries.

From Python and Statistics to Advanced Machine Learning and AI

The technical track runs like this: Python builds the foundational programming base, statistics and probability build the analytical foundation, and Introduction to Machine Learning gives initial exposure to ML. In the USA phase, this becomes Advanced Machine Learning, which deepens the technical learning, and Artificial Intelligence Foundations, which introduces advanced computational applications.

From Data Management to Data Engineering and Big Data

The data-handling track starts with Data Management & SQL and Cloud & Data Infrastructure Fundamentals in India. In the USA phase, this grows into Big Data Analytics and Data Engineering & High-Performance Computing. Together, these four subjects build technical depth step by step, from handling structured data to working with large-scale, complex data systems.

From Analytics to Strategy and Governance

Data Visualisation & Business in India introduces the business-facing side of data science early on. Data Strategy & Governance in the USA phase takes that same perspective further, covering how data is managed and governed at an organisational level. This shows the curriculum covers both technical and business dimensions, not just one.

Why a Balanced Data Science Curriculum Matters

Data Science is not just machine learning. It needs a combination of programming, statistics, data handling, analytics, computing, and business understanding, and this program’s curriculum reflects exactly that combination across its two study phases.

Technical Skills

• Python
• SQL
• Machine Learning
• Artificial Intelligence
• Big Data
• Data Engineering
• High-Performance Computing

Analytical and Business Skills

• Statistics and Probability
• Data Visualisation
• Business
• Data Strategy
• Governance

Practical Application

• Applied Projects & Assessments
• Capstone / Master’s Thesis
• Industry-Oriented Project Work

This breadth is what connects the curriculum to career preparation. Employers are not just looking for someone who can build a model. They want people who can also understand the data, communicate findings, and apply their work to real business problems.

What Career Paths Can These Data Science Subjects Prepare You For?

The brochure lists specific roles this program is designed to lead toward: Data Scientist, Senior Data Analyst, Machine Learning Engineer, Data Engineer, Product & Growth Analyst, and Business Analytics Manager. Each connects back to specific subjects in the curriculum.

Data Scientist and Machine Learning Engineer

These roles connect directly to the machine learning, AI, and advanced analytics subjects covered in the USA Track. A Data Scientist builds predictive models and turns messy, real-world data into decisions, while a Machine Learning Engineer focuses on getting models into production, not just notebooks.

Data Analyst and Business Analytics Roles

Statistics, visualisation, business, and data strategy subjects connect directly to analytics-oriented roles like Senior Data Analyst and Business Analytics Manager, roles that own the metrics leadership looks at and bridge analytics teams with business decisions.

Data Engineer and Technical Data Roles

Data management, cloud infrastructure, big data, data engineering, and high-performance computing subjects connect to technical roles like Data Engineer, who designs the pipelines that everyone else’s analysis depends on.

India Track vs. USA Track: Data Science Subjects at a Glance

Here is a side-by-side look at how the subjects are split across the two academic stages of the Global Pathway.

Explore the MAS in Data Science Global Pathway

This curriculum is built to move students step by step from foundational Data Science knowledge to advanced technical and strategic subjects. Students begin with foundational and applied learning at Mahindra University, then progress to the STEM-designated MAS in Data Science at Illinois Tech in Chicago.

Across both phases, the program combines technical depth, analytical understanding, and applied project work, backed by an 85 to 90 percent visa acceptance rate at Illinois Tech and up to 36 months of U.S. work authorisation through OPT and STEM OPT.

If you want to see exactly how these subjects fit into the full program, from admissions to graduation, explore the edept x Illinois Tech x Mahindra University Global Pathway Program.