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Ph.D. Admissions
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B.Sc. in Statistics, Computer Science & Mathematics

Eligibility Criteria

Pass in PUC/10+2 with Physics and Mathematics as mandatory subject and 45% marks (40% for reserved categories)

Career Opportunities

Graduates of the B.Sc. Mathematics, Statistics & Computer Science (MStCs) program can pursue rewarding careers in IT, software development, data science, artificial intelligence, scientific research, analytics, and technology-driven industries. The interdisciplinary curriculum also prepares students for higher studies (M.Sc., MCA, M.Tech., MBA), competitive examinations, and research careers. With strong analytical, programming, and problem-solving skills, graduates are well-equipped for roles in both industry and academia. The program also nurtures innovation, entrepreneurship, and start-up opportunities in emerging technologies.

Overview

The Bachelor of Science (B.Sc.) program in Mathematics, Statictics and Computer Science (MStCs) is a multidisciplinary undergraduate program designed in alignment with the National Education Policy (NEP) 2020. The program integrates fundamental concepts of physical sciences, mathematical techniques, and computational skills to equip students with a strong analytical, problem-solving, and research-oriented mindset. It is structured with flexibility, multiple exit options, and skill-based learning to cater to diverse academic and professional aspirations.

Course Curriculum

01Language II: Kannada I

02Language II: Hindi I

03Language II: Additional English I

04Communicative English I

05Advanced Differential Calculus and Computational Linear Algebra

06Foundations of Statistical Data Analysis

07Digital Logic and Problem-Solving using C

08R Programming

09Logic and 3D Analytics for Computing

10Advanced Differential Calculus and Computational Linear Algebra Lab

11Foundations of Statistical Data Analysis Lab

12Problem Solving Lab using C

01Language II: Kannada II

02Language II: Hindi II

03Language II: Additional English II

04Communicative English II

05Applied Calculus and Real Analysis

06Probability Models for Data Science

07Data Structures using C

08Advanced Calculus and Real Analysis Lab

09Probability Models for Data Science Lab

10Data Structures Lab

11Fundamentals of Artificial Intelligence

12Skill Enhancement Course – I

01Differential Equations and Foundations of Algebra

02Statistical Estimation and Simulation

03Operating System with Linux

04Differential Equations and Foundations of Algebra Lab

05Statistical Estimation and Simulation Lab

06Linux Programming

07Machine Learning

08Research Methodology and Intellectual Property Rights

09Constitution of India and Professional Ethics

10Health and Wellness / Yoga

11NCC Activities

12NSS Activities

01Mathematical Methods and Abstract Algebra

02Statistical Hypothesis Testing

03Database Management System

04Mathematical Methods and Abstract Algebra Lab

05Statistical Hypothesis Testing Lab

06Database Management System Lab

07Materials for Device Applications

08Environmental Studies

09Skill Enhancement Course - II

10Cyber Security

01Numerical Analysis, Linear Algebra and Laplace Transforms

02Design and Analysis of Experiments

03Machine Learning with Python

04Numerical Analysis, Linear Algebra and Laplace Transforms Lab

05Design and Analysis of Experiments Lab

06Machine Learning with Python Lab

07Complex Analysis

08Applied Statistics

09Minor Project / Internship

10Fourier Series and Fourier Transforms

11Entrepreneurship

12Soft Skill Training

01Computational Methods and Analysis

02Operations Research

03Full Stack Development using Python

04Computational Methods and Analysis Lab

05Operations Research Lab

06Full Stack Development using Python Lab

07Major Project/Internship

08MOOC/SWAYAM

01Real Analysis

02Algebra

03Ordinary and Partial Differential Equations

04Discrete Mathematics

05Numerical Methods

06Research Methodology and LaTeX Lab

07Statistical Analysis Using MATLAB

01Linear Algebra

02Advanced Complex Analysis

03Fluid Mechanics

04Fuzzy Set Theory

05Graph Theory

06Machine Learning Using Python

07Research Project / Internship

Programme Educational Objectives (PEOs)

After 3 years of graduation, the graduate will:

PEO-1

Professional Expertise & Application: Graduates will apply the principles of mathematics, statistics, and computer science to analyze data, develop mathematical and computational models, and solve complex problems in academic, research, and industrial domains.

PEO-2

Innovation & Lifelong Learning: Graduates will pursue advanced studies or engage in continuous professional development to innovate and adapt to emerging technologies and scientific challenges.

PEO-3

Social & Ethical Contribution: Graduates will demonstrate professional ethics, effective communication, and teamwork, contributing responsibly to scientific and societal progress.

Programme Outcomes (POs)

After successful completion of the programme, graduates will be able to:

PO 1Disciplinary Knowledge:
Apply the fundamental principles of mathematics, statistics, and computer science to understand theoretical concepts and computational systems.
PO 2 Problem Analysis:

Identify, formulate, and analyze complex mathematical, statistical, and computational problems using logical reasoning and appropriate analytical techniques.

PO 3 Design/development of solutions:

Design experiments and develop mathematical, statistical, and computational approaches to solve real-world problems.

PO 4 Investigation of complex Problems:

Conduct literature surveys, perform data analysis, and interpret results using suitable mathematical, statistical, and computational tools.

PO 5 Modern Tool Usage:

Create, Select and apply modern programming languages, statistical software’s, and computational tools for modeling, simulation, and data analysis.

PO 6 The Scientist and Society:

Understand the societal, health, safety, and ethical implications of scientific work and its applications.

PO 7 Environment and Sustainability:

Understand the impact of scientific and technological solutions on society and the environment to support the sustainable development.

PO 8 Ethics:

Apply ethical principles and professional integrity in data analysis, research, and computational practices.

PO 9 Individual and Teamwork:

Function effectively as an individual and as a member or leader in multidisciplinary teams.

PO 10 Communication:

Communicate mathematical, statistical, and computational ideas effectively through reports, presentations, and technical documentation.

PO 11 Project Management and Lifelong Learning:

Demonstrate knowledge of project management principles to execute scientific projects within realistic constraints and engage in lifelong learning to adapt to recent developments in mathematics, statistics, and comp

Programme Handbooks

Programme Specific Outcomes

After successful completion of the programme, graduates will be able to:

  • PSO-1 Mathematical and Statistical Knowledge : Demonstrate strong knowledge of mathematics, probability, and statistical methods to analyze scientific problems, develop models, and interpret results in scientific and real-world contexts.
  • PSO-2 Computational and Programming Competence : Apply programming, algorithms, and computational techniques to design efficient solutions for mathematical, statistical, and data-driven problems.
  • PSO-3 Data Analysis and Interdisciplinary Applications : Use statistical analysis, mathematical reasoning, and computational tools to analyze data, support decision-making, and address problems in areas such as data science, finance, research, and technology.
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