Data Science Syllabus 2026: Semester-Wise Subjects for BTech Data Science

The Data Science Syllabus for BTech Data Science lays the academic pathway for students who want to become experts in extracting insights, building models, and solving real-world problems using data. With the explosion of data in every industry, a structured syllabus that combines programming, statistics, machine learning, and domain applications has become essential.

The BTech Data Science programme at K.R. Mangalam University equips students with this blend of knowledge, preparing them for both industry roles and future technologies.

Year 1: Foundations of Data and Computing

Semester 1 – Introduction & Core Fundamentals

In the first semester, students focus on building basic computational and mathematical foundations:

• Engineering Mathematics I – Essential mathematical concepts for engineering

• Introduction to Programming – Basics of programming using languages like Python

• Digital Logic and Computer Architecture – Understanding how computers work

• Physics and Chemistry for Engineering – Core scientific principles

• Basic Lab Work – Practical exposure to programming and science fundamentals

Why this matters:
This semester ensures students are comfortable with logic, computation, and mathematical reasoning — the base upon which advanced data science concepts are built.

Semester 2 – Data Basics & Technical Skills

In the second semester, the focus shifts toward data and technical foundations:

• Engineering Mathematics II – Expands mathematical tools needed for data algorithms

• Data Structures and Algorithms – How data is represented and processed

• Object-Oriented Programming – Software design fundamentals

• Database Management Systems – Basics of storing and retrieving data

• Programming Lab – Hands-on coding practice

Why this matters:
Understanding how data is structured and manipulated is essential before moving into deeper analytics and machine learning.

Year 2: Core Data Science Concepts

Semester 3 – Statistics and Data Handling

At this stage, students start learning about data in context:

• Probability and Statistics for Data Science – Vital tools for data interpretation

• Data Mining Techniques – Intro to discovering patterns in data

• Operating Systems and Networks – Background technical skills

• Analytical Lab Sessions – Apply statistics and mining techniques

Why this matters:
Statistical understanding is the backbone of data science; it helps students analyse data accurately and make evidence-based decisions.

Semester 4 – Data Analysis and Visualisation

The focus in the fourth semester is on exploring and presenting data:

• Data Analytics and Visualisation – Techniques for making sense of large datasets

• Predictive Modelling – Intro to building models that forecast outcomes

• Design & Analysis of Algorithms – Efficiency focused learning

• Data Science Lab Work – Applying analysis on real datasets

Why this matters:
Being able to analyse data and present it meaningfully is a critical skill for any data professional.

Year 3: Advanced Techniques and Machine Learning

Semester 5 – Machine Learning Principles

At this vital stage, students dive into machine learning:

• Machine Learning Fundamentals – Building blocks of intelligent systems

• Artificial Intelligence Concepts – Intro to AI applications

• Big Data Technologies – Handling massive and complex datasets

• Hands-On ML Projects – Applying learning to real workflows

Why this matters:
Machine learning is the heart of data science — enabling systems that learn from data and make smart predictions.

Semester 6 – Advanced Data Engineering

This semester reinforces data engineering principles:

• Software Engineering for Data Science – Developing robust data apps

• Cloud Computing and Deployment – Using cloud platforms for scaling

• Advanced Analytics Projects – Real industry problem statements

• Capstone Lab Work – Toolkit application across domains

Why this matters:
Data science at scale requires strong software and engineering skills, not just analytics.

Year 4: Integration, Research, and Real World Application

Semester 7 – Domain Electives and Specialisations

In the final year, students choose electives and focus areas:

• Healthcare Data Science / Financial Data Analytics / IoT Analytics – Domain focus

• Deep Learning and Neural Networks – Advanced predictive systems

• Research Methodology – Tools for systematic investigation

• Industry Guided Lab Work

Why this matters:
Domain knowledge amplifies the power of data science, making students job-ready for specialised roles.

Semester 8 – Final Project & Internship

The final semester is dedicated to applying everything learned:

• Major Industry Project – Solving real business or research problems

• Internship with Industry Partner – Professional exposure

• Thesis / Report Submission – Demonstrating research and analytical outcomes

Why this matters:
The thesis and internship help bridge academic learning with real corporate or research applications, boosting confidence and employability.

Why Study BTech Data Science at K.R. Mangalam University

The BTech Data Science programme at K.R. Mangalam University takes this syllabus one step further by combining academic depth with real industry relevance:

Industry-Aligned Curriculum:
Each subject reflects current trends and tools used in data science practice — not just theory.

Practical Learning:
Labs, case studies, projects, and industry partnerships help students apply what they learn.

Placement Preparation:
Dedicated career support, mentoring, and interview coaching boost job readiness.

Location Advantage:
Situated in Gurugram, the programme taps into a vibrant tech and business ecosystem, offering networking and internship opportunities.

Conclusion

The Data Science Syllabus 2026 for BTech Data Science offers a well-rounded educational path from basic computation and statistics to advanced machine learning and domain specialisations. With its structured semesters and practical emphasis, students gain the skills needed to thrive in a rapidly evolving technology landscape.

By offering this programme with a curriculum grounded in real-world relevance and supported by campus facilities, industry insights, and career preparation, K.R. Mangalam University provides a compelling environment for aspiring data scientists in 2026 and beyond.

Frequently Asked Questions (FAQs)

What is the focus of the BTech Data Science syllabus?

It covers foundational mathematics and computing, statistics and analytics, machine learning, big data, and real-world applications through projects.

Does the syllabus include real industry exposure?

Yes — through practical labs, industry projects, and a final internship component.

Is machine learning part of the curriculum?

Definitely. Machine learning and predictive modelling are core parts of the syllabus.

How does the syllabus prepare students for jobs?

By combining theory, coding skills, analytics techniques, and project experience that align with employer expectations.