B.Sc Data Science Syllabus and Subjects

Shristi T

BSc Data Science subjects cover crucial topics such as Data Analysis and Visualization, Machine Learning, Linear Algebra, Probability, Python/R Programming, Programming in C, Data Structures, and Analytics. Students can also choose elective B.Sc Data Science subjects tailored to specific interests such as Logistics Analytics, Deep Learning, Time Series Analysis, Social Network Analysis, and more.

B.Sc Data Science syllabus spans over three years and is strategically divided into six semesters. A BSc in Data Science prepares students with the knowledge and practical skills needed to extract valuable insights from data. The syllabus may vary slightly depending on the institution but the core topics and the focus on practical skills remain consistent throughout the BSc Data Science course.

BSc in Data Science course equips students with the skills to work in a variety of roles such as data analyst, data scientist, machine learning engineer, business analyst, or even pursue further education in data science and related fields.

Table of Contents

Subjects in BSc Data Science
Semester-Wise BSc in Data Science Syllabus

BSc Data Science 1st Semester Syllabus
Second-Year BSc Data Science Syllabus Semester Wise
Third-Year BSc Data Science Syllabus Semester Wise

Subjects in BSc Data Science

B.Sc Data Science subjects enhance students' skills in data collection, analysis, interpretation and making data-driven decisions. This degree focuses on statistical methods, machine learning, data visualization, and programming. The B.Sc Data Science subjects are divided into core and elective courses, which are delineated in the sections below.

Core BSc Data Science Subjects

The core subjects under BSc Data Science equip students with foundational knowledge and skills critical for a comprehensive understanding of Data Science. The core BSc Data Science subjects generally provided across most top colleges include:

Programming
Data Structures and Algorithm
Statistics for Data Science
Mathematics for Data Science
Database Management
Data Privacy and Security
Python, Java, C, R
Data Handling and Visualization
Big Data Analytics
Deep Learning

Elective BSc Data Science Subjects

Elective subjects enable students to tailor their education based on personal interests, the potential for advanced study, and diverse career opportunities in the field of Data Science. The list of elective subjects under BSc Data Science includes:

Time Series Analysis
Predictive Modelling and Analytics
Social Network Analytics
Information Retrieval and Processing
Conditional Monitoring Techniques for Data Science
Operating System
Tensor Flow

BSc Data Science Subjects in Detail

BSc Data Science covers a range of subjects that develop skills to work with data and drive insights such as computer science and programming, machine learning, data visualization, text mining, data mining, etc. The important subjects covered in B.Sc Data Science syllabus are explored in detail in the following table:

BSc Data Science Subjects	Topics Covered
Mathematics for Data Science	Matrices and Calculus, Sets and Foundations in Logic, Relations and Functions, Hypothesis Testing, Probability Theory,
Programming in C	Programming Language, Problem-solving Techniques, Fundamentals of C, Functions, Arrays and Strings, Pointers, Structures, and Union, Introduction to Embedded C.
Data Science Fundamentals	Data Analytics & EXCEL, Data Visualization, Data-driven Techniques, Advanced Data Analytics with Excel, Forecasting in Excel.
Statistics for Data Science	Statistical Methods, Probability and Distribution, Correlation and Regression, Sampling and Testing, Statistical Inference.
Python	Python Programming, File, Exception Handling and OOP, Numpy, Data Manipulation with Pandas, Data Cleaning Preparation and Visualization.
Data Security and Privacy	Data Privacy, Static Data Anonymization, Privacy Preserving Data Mining, Synthetic Data Generation, Dynamic Data Protection and Privacy Regulation.

Semester-Wise BSc in Data Science Syllabus

B.Sc Data Science subjects 1st year focuses on foundational principles in data science while 2nd year delves into practical applications, and the 3rd year covers advanced topics and a hands-on project for specialized skill development. The BSc Data Science syllabus semester-wise is detailed below.

BSc Data Science 1st Semester Syllabus

BSc Data Science 1st year program introduces students to the foundational principles of data science, covering topics such as statistics, programming, and data structure fundamentals. This first year BSc in Data Science syllabus is detailed in the table below:

Semester I	Semester II
English	Statistics for Data Science
Programming in C	Probability Models for Data
Mathematical Foundations for Data Science	Programming
Data Science Fundamentals	Data Structures and Algorithm
Foundations of Computer Science	Linear Algebra
Environmental Science	Operations Research
Elective	Elective

Practical Topics in First Year B.Sc Data Science Syllabus

The practical subjects taught in the B.Sc Data Science subjects 1st year are as follows

Database Management System Lab
Java Programming Lab

Second Year BSc Data Science Syllabus Semester Wise

BSc Data Science second year syllabus explores subjects like business analytics, machine learning, and data management, gaining proficiency in Python and R. The practical labs enhance their technical skills. The second year BSc Data Science syllabus semester wise is given below:

Semester III	Semester IV
Business Analytics	Machine Learning
Data Management	Data Security and Privacy
Python for Data Science	Data Wrangling with Python
Data Analytics using R	Advanced Python for Data
AI	Internet Technology
Electives	Electives

Practical Topics in Second Year B.Sc Data Science Syllabus

The second-year BSc in Data Science subjects for practicals are listed below:

Python
Machine Learning Lab
Technology Lab
Statistics Lab

Third Year BSc Data Science Syllabus Semester Wise

The third year is dedicated to advanced topics in data science, including big data analytics, deep learning, and natural language processing. Students also work on a significant project that allows them to apply their knowledge. The third year BSc Data Science syllabus is listed in the table below:

Semester V	Semester VI
Big Data Analytics	R for Analytics
Deep Learning	Techniques And Tools for Data Science
Data Handling and Visualization	Elective
Natural Language Processing	Internship
Elective	Project

Practical Topics in Third Year B.Sc Data Science Syllabus

The third-year BSc in Data Science subjects for practicals are listed below:

Relational Database Management System Lab
Big Data and Analytics Lab
Statistics Lab
Mathematics Lab

College-Wise BSc Data Science Syllabus

The subjects and syllabus for BSc Data Science can differ across colleges, reflecting their unique curriculum and educational objectives. To access and download the BSc Data Science syllabus pdf, students should visit the official website of their selected university. Here's a general outline of the syllabus of the top BSc Data Science colleges in India.

SVKM’s NMIMS BSc Data Science Syllabus

The BSc Data Science course at NMIMS delves into a comprehensive curriculum. It spans six semesters and equips students with a broad skill set to excel in the field of data science, culminating in a capstone project and research initiatives. The BSc Data Science syllabus semester-wise is provided in the tables below:

Semester I	Semester II
Descriptive Statistics - I	Descriptive Statistics - II
Introduction to Probability Theory	Probability Models for Discrete Data
Univariate Calculus	Probability Models for Continuous Data
Elementary Number Theory	Linear Algebra
Discrete Mathematics	Numerical Methods
Foundations of Computer Science	Introduction to Programming
Introduction to R	Effective Communication
Environmental Studies

Semester III	Semester IV
Statistical Inference for Data Science - I	Statistical Inference for Data Science - II
Sampling Distributions & Applications	Regression Analysis
Statistics Lab - I	Designs of Experiments
Multivariate Calculus	Statistics Lab - II
Mathematics Lab - I	Theory of Optimization & Graph Theory
Data Management	Mathematics Lab - II
Technology Lab - I	Machine Learning - I
Data Analysis using Python	Technology Lab - II
Research Writing	Data Wrangling with Python
Research Initiative in Data Science - I	Research Ethics
	Research Initiative in Data Science - II

Semester V	Semester VI
Multivariate Analysis	Markov Chains
Operations Research	Time Series & Forecasting
Statistics Lab - III	Statistical Process Control
Differential Equations	Statistics Lab - IV
Mathematics Lab - III	Deep Learning Techniques
Machine Learning - II	Technology Lab – IV
Technology Lab - III	Data Visualization and Modelling
Big Data Analytics	Entrepreneurship Skills
Professional Skills	Capstone Project
Research Initiative in Data Science - III

Hindustan University BSc Data Science Syllabus

Hindustan University's BSc Data Science course explores topics like machine learning, data security, and big data analytics. B.Sc Data Science syllabus also provides flexibility with electives and a focus on real-world application through internships and projects. The semester-wise BSc Data Science syllabus semester wise is detailed below.

Semester I	Semester II
English	Statistics for Data Science
Mathematical Foundations for Data Science	Data Structure and Algorithm
Programming in C	Operating Systems
Data Science Fundamentals	Database Management System
Computer Organization	Python for Data Science
C Programming Lab	Database Management Lab
Data Analysis with Excel Lab	Python Programming Lab

Semester III	Semester IV
Computer Networks	Machine Learning
Artificial Intelligence	Data Security and Privacy
Data Analytics using R	Professional Ethics and Life Skills
Business Analytics	Data Handling and Visualization
Elective: Time Series Analysis/Data Wrangling Techniques	Elective: Predictive Modelling and Analytics/Statistical Inference for Data Science
Data Science Programming using R	Machine Learning Lab
Business Analytics Lab	Data Handling and Visualization Lab

Semester V	Semester VI
Big Data and Analytics	Techniques and Tools for Data Science
Principles of Deep Learning	Elective: Conditional Monitoring Techniques for Data Science/IoT Cloud and Data Analytics
Elective: Social Network Analytics/Information Retrieval and Processing	Internship
Elective: Computer Vision Techniques/Digital Image Processing using MATLAB	Project Work
Big Data and Analytics Lab
Mini Project

BSc Data Science Project Topics

Projects are an integral tool of the BSc Data Science syllabus that bridges the gap between theory and practice. Listed below are some useful project topics for students to refer to:

Build a customer churn prediction system for a business to identify and retain at-risk customers.
Develop an anomaly detection system to enhance cybersecurity.
Analyze healthcare data to identify trends, patient outcomes, and potential improvements in healthcare delivery.
Build a recommendation system using collaborative filtering techniques
Develop a model for predicting energy consumption in a smart city.
Design a tool that helps organizations ensure data ethics and privacy compliance

BSc Data Science Course Structure

The course structure for a BSc Data Science typically comprises a combination of core subjects, elective courses, and practical components. While specific courses may vary by university and field of study, a BSc course structure often follows a pattern:

Three years
Six Semesters
Core and Elective courses
Seminars
Practical Lab
Guest lecturers
Projects

BSc Data Science Teaching Methodology and techniques

The teaching methods and techniques in higher education such as B.Sc Data Science have evolved to engage and educate students effectively. A combination of traditional and modern approaches is often employed by BSc Data Science colleges in India. The methods include:

Lectures
Practical Learning
Laboratory Work
Project Work
Case Studies
Guest Lectures
Coding Practice
Internships
Assessment
Research Projects

BSc Data Science Reference Books

Aspiring candidates can refer to the books mentioned in the table below for gaining overview of B.Sc Data subjects:

BSc Data Science Subjects	Reference Books	Authors
Mathematics for Data Science	Statistics with JMP: Graphs, Descriptive Statistics and Probability	Peter Goos and David Meintrup
Programming in C	Computer Programming	Ashok Kamthane
Data Science Fundamentals	Market Analytics Data Driven Technique with Microsoft Excel	Wayne L. Winston
Statistics for Data Science	Practical Statistics for Data Scientists	Peter Bruce, Andrew Bruce, and Peter Gedeck
Python	Core Python Programming	Wesley J. Chun
Data Security and Privacy	Data Privacy - Principles and Practice	Nataraj Venkataramanan and Ashwin Shriram

Top Bachelor of Science [B.Sc] (Data Science) Colleges

Amrita Vishwa Vidyapeetham (Amrita University)

Coimbatore,Tamil Nadu

IISER Bhopal

Bhopal,Madhya Pradesh

Sri Ramachandra University / Sri Ramachandra Medical College and Research Institute, Chennai

Chennai,Tamil Nadu

SNU

Chennai,Tamil Nadu

PSGCT

Coimbatore,Tamil Nadu

NMIMS Mumbai

Mumbai,Maharashtra

Anurag University

Hyderabad,Telangana

Guru Nanak Institute of Technology - GNIT

Kolkata,West Bengal

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Result Date: Mar 22, 2024

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B.Sc Data Science Fee Structure

FAQs on B.Sc Data Science Syllabus and Subjects

Q: Is BSc Data Science hard?

A: The level of difficulty varies from student to student, but BSc Data Science can be challenging due to its mathematical and technical components. However, it's manageable with dedication and the right study approach.

Q: Does BSc Data Science have maths?

A: Yes, mathematics is an integral part of BSc Data Science, including topics like statistics, linear algebra, probability, and calculus.

Q: Is there coding for BSc Data Science?

A: Certainly, coding is a significant component of BSc Data Science, with a focus on programming in languages like Python and R, along with data analysis and visualization.

Q: What are the programming languages taught in BSc Data Science syllabus?

A: The common programming languages taught in BSc Data Science syllabus are Java, Python, R, SQL, etc.

Q: Which Data Visualization tools do students learn in BSc Data Science curriculum?

A: Students learn Data Visualization tools like Matplotlib, Seaborn, and Tableau in BSc Data Science course.