B.Sc. Computer Science with Data Analytics - Data Analytics Curriculum Overview

Sep 1, 2026

B.Sc. Computer Science with Data Analytics trains students in core computer science and data analytics to design, analyze, and implement solutions for real-world problems. The curriculum covers programming languages, algorithms, operating systems, databases, machine learning, data mining, business intelligence, big data, probability and statistics, and data optimization. It also includes modules across AI, machine learning, NLP, cloud computing, web development, and cyber security, supported by hands-on training, industrial projects, internships, and viva-voce to strengthen industry-academia alignment and decision-making skills.

LanguageEnglish

CategoryPaper Templates

FormatDOCX

Pages131

Reading Time46 min

AccessFree to download

Introduction

Highlights of the Revamped Curriculum

Student-centric learning and industrial components

Core subjects and peer learning

Competitive examinations and general studies

Industry-academia interface and job opportunities

Value additions in the Revamped Curriculum

Credit Distribution for UG Programmes

Choice Based Credit System (CBCS)

1. Introduction B.Sc. Computer Science with Data Analytics B.Sc. Computer Science with Data Analytics is a program that teaches students the fundamentals of computer science and data analytics. Students will learn various database tools, computational methods, and programming languages to design, analyze, and implement solutions to real-world problems. The coursework includes topics such as artificial intelligence, machine learning, natural language processing, cloud computing, web development, cyber security, and more. Students will have the opportunity to explore new technologies and advance their knowledge in the field of computer science and data analytics. The course is enabled to include several interdisciplinary areas like: programming languages, algorithms, operating systems, databases, machine learning, data mining, business intelligence, big data, probability and statistics, data optimization, statistical simulation and data analysis, management decision analysis, decision models and predictive analysis. The need for scientists who understand data in all its aspects will continue to grow strongly. Students graduating from the program will have significantly more depth and breadth in the broad area of Data Analytics and receive all the information they need to work with various kinds of data and statistical data. The program is designed so that students have in-depth knowledge of the HYPERLINK "https://www.simplilearn.com/building-a-data-driven-future-with-synthetic-data-article" \t "_blank" \o "many approaches, aptitudes, methodologies, and instruments" many approaches, aptitudes, methodologies, and instruments needed to deal with corporate data. Students receive instruction in the abilities needed to find the needed solutions and assist in making significant judgments. This is the primary reason the syllabus of Data Analytics courses includes concepts that touch base on cloud computing, big data, natural language processing, and data sentiment analysis. The future of Data analytics is estimated to bring opportunities in various areas of banking, finance, insurance, entertainment, telecommunication, automobile, etc. Data analytics has been around for centuries, but it wasn't until recently that the term 'data analytics' was coined. Throughout history, people have used data in various ways to obtain insight into their environment and decision-making. In the early 19th century, statisticians and scientists began looking at data in a more organized and mathematical way. With advances in technology, data analytics has become more sophisticated and widespread. Today, data analytics is used to make predictions and generate insights in virtually every industry. The present era is full of technological advances in almost all spectrum of life and we are flooded with enormous amount of data. There is an increasing demand of capturing, analyzing, and synthesizing this large amount of data sets in a number of application domains to better understand various phenomena and to convert the information available in the data into actionable strategies such as new scientific discoveries, business applications, policy making, and healthcare etc. Data Analytics is the process of analyzing data through different techniques and interpretation of the data to draw meaningful insights. It involves the gathering, reorganizing, and analysis of data which is then used to inform decisions or develop strategies. Data analytics can be used in a variety of different areas, including marketing, finance operations, customer service, and more. 3 – Strong, 2- Medium, 1- Low Highlights of the Revamped Curriculum: Student-centric, meeting the demands of industry & society, incorporating industrial components, hands-on training, skill enhancement modules, industrial project, project with viva-voce, exposure to entrepreneurial skills, training for competitive examinations, sustaining the quality of the core components and incorporating application or

FAQs

What skills does the B.Sc. program build for data analytics work?

The program develops abilities in database tools, computational methods, and programming languages, alongside data analysis techniques used to derive meaningful insights and support decision-making.

Which technical areas are included in the course content?

Course topics include artificial intelligence, machine learning, natural language processing, cloud computing, web development, cyber security, and additional areas such as big data and data mining.

How does the curriculum emphasize practical and industry experience?

It includes hands-on training, industrial projects with viva-voce, internships during the second year vacation, and project-based application of conceptual knowledge in the fifth semester.

Sep 1, 2026

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