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B-SC in Computer Science Mathematics Statistics at CHRIST (Deemed to be University)
Bengaluru, Karnataka
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About the Specialization
What is Computer Science, Mathematics, Statistics at CHRIST (Deemed to be University) Bengaluru?
This Computer Science, Mathematics, Statistics (CMS) program at CHRIST (Deemed to be University) focuses on equipping students with a robust foundation in computational theories, mathematical principles, and statistical methodologies. The program is designed to meet the growing demand for interdisciplinary skills in India''''s technology and data-driven industries, fostering analytical thinking and problem-solving capabilities essential for complex real-world challenges.
Who Should Apply?
This program is ideal for fresh graduates seeking entry into data science, software development, or financial analysis roles, particularly those with a strong aptitude for logical reasoning and quantitative analysis. It also caters to individuals aiming for postgraduate studies in specialized fields like Artificial Intelligence, Actuarial Science, or Quantitative Finance, requiring a blend of technical and analytical expertise.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Data Scientists, Business Analysts, Software Developers, Statisticians, or Actuarial Analysts. Entry-level salaries typically range from INR 4-7 lakhs per annum, with experienced professionals potentially earning INR 10-25 lakhs or more. Growth trajectories are steep, aligning with the booming IT and analytics sectors, often leading to leadership roles in Indian MNCs and startups.
Student Success Practices
Foundation Stage
Master Core Programming & Math Fundamentals- (Semester 1-2)
Dedicate consistent effort to mastering C programming and fundamental calculus concepts. Utilize online platforms like HackerRank and NPTEL for coding practice and conceptual clarity, ensuring a strong base for advanced topics. Form study groups with peers to discuss challenging problems and clarify doubts promptly.
Tools & Resources
HackerRank, GeeksforGeeks, NPTEL videos, Khan Academy, Peer Study Groups
Career Connection
A solid foundation in these areas is crucial for all subsequent computer science and analytical roles, directly impacting performance in technical interviews for placements in IT and data firms.
Develop Strong English & Communication Skills- (Semester 1-2)
Actively participate in English language classes, focusing on academic writing, logical reasoning, and presentation skills. Engage in debates, public speaking events, and group discussions to enhance verbal communication and critical thinking. Read widely to improve vocabulary and comprehension.
Tools & Resources
Toastmasters International (student clubs), Online news portals, Grammarly, Group Discussions
Career Connection
Effective communication is a vital soft skill valued by Indian employers, essential for interviews, team collaboration, and client interactions across all industries.
Explore Interdisciplinary Applications Early- (Semester 1-2)
Look for mini-projects or assignments that combine elements of Computer Science, Mathematics, and Statistics, even at a basic level. Attend introductory workshops on data analysis or problem-solving using mathematical software to understand the interconnectedness of these disciplines.
Tools & Resources
Kaggle (introductory datasets), R/Python basics tutorials, University workshops
Career Connection
Early exposure to interdisciplinary problem-solving fosters a holistic understanding, making you a versatile candidate for diverse roles in data science and analytics in the Indian market.
Intermediate Stage
Engage in Practical Application & Projects- (Semester 3-5)
Actively seek opportunities for practical projects, whether academic assignments or self-initiated. Implement data structures, OOP concepts, and statistical models using tools like C++, Python, and R. Participate in coding competitions and hackathons to apply learned concepts in real-time scenarios.
Tools & Resources
GitHub, LeetCode, Kaggle Competitions, University Project Fairs
Career Connection
Hands-on experience with projects is highly valued by Indian recruiters. It demonstrates practical skills, problem-solving abilities, and a portfolio for showcasing capabilities during placements.
Undertake Industry Internships & Certifications- (Semester 4 (Internship) & Semester 3-5 (Certifications))
Actively search for internships during semester breaks in areas like software development, data analytics, or quantitative finance. Simultaneously pursue relevant online certifications in SQL, Python for Data Science, or specific statistical tools to add value to your profile.
Tools & Resources
Internshala, LinkedIn Jobs, Coursera/edX for certifications, Naukri.com
Career Connection
Internships provide crucial industry exposure and networking opportunities, often leading to pre-placement offers. Certifications validate specialized skills, improving your marketability for entry to mid-level roles in India.
Build a Strong Professional Network- (Semester 3-5)
Attend industry seminars, workshops, and guest lectures organized by the university or local professional bodies. Connect with faculty, alumni, and industry experts on platforms like LinkedIn. Participate in professional clubs or societies related to Computer Science, Mathematics, or Statistics.
Tools & Resources
LinkedIn, Professional Conferences, Alumni Connect Programs, Departmental Societies
Career Connection
Networking is essential for uncovering hidden job opportunities, gaining mentorship, and staying updated on industry trends, providing a significant edge in the competitive Indian job market.
Advanced Stage
Specialize and Build a Portfolio- (Semester 6-8)
Deep dive into your chosen specialization (e.g., Machine Learning, Actuarial Science, Optimization) through DSEs and research components. Develop a comprehensive portfolio of advanced projects, research papers, or significant internship contributions that showcase your expertise. Focus on real-world problem statements.
Tools & Resources
GitHub, Medium (for technical blogging), Personal website/blog, Research databases
Career Connection
A strong, specialized portfolio differentiates you from other candidates, demonstrating your in-depth knowledge and practical capabilities, crucial for securing roles in niche and high-paying domains in India.
Intensive Placement Preparation- (Semester 7-8)
Begin rigorous preparation for placements well in advance. Practice aptitude tests, logical reasoning, and data interpretation extensively. Hone your technical skills through coding challenges and mock interviews specific to Computer Science, Mathematics, and Statistics. Work on improving soft skills for HR rounds.
Tools & Resources
AmbitionBox, Glassdoor (for company-specific prep), PrepInsta, College Placement Cell
Career Connection
Thorough preparation directly translates into higher chances of securing desired job roles with competitive salaries in top Indian companies and MNCs operating in India.
Explore Higher Education & Research Avenues- (Semester 7-8)
If interested in academia or advanced research, actively engage in the Research Component subjects, aiming for publications or impactful project outcomes. Research postgraduate programs (M.Sc./MBA/MS) in India and abroad, and prepare for entrance exams like GATE, CAT, or GRE/GMAT if applicable.
Tools & Resources
UGC-NET/GATE exam portals, University career counseling, Research supervisors, Study Abroad consultants
Career Connection
This pathway prepares you for roles in R&D, specialized analytics, or academic positions, offering long-term career growth and intellectual satisfaction in India''''s evolving knowledge economy.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 level or equivalent with Mathematics as one of the subjects from any recognised Board in India or abroad.
Duration: 4 years / 8 semesters
Credits: 172 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS101 | Programming in C | Core - Computer Science | 4 | Programming Fundamentals, Data Types & Operators, Control Flow, Functions, Arrays, Pointers, Structures & Unions |
| MT101 | Calculus and Analytical Geometry | Core - Mathematics | 4 | Differential Calculus, Integral Calculus, Vectors, Three-dimensional Geometry, Multiple Integrals |
| ST101 | Descriptive Statistics | Core - Statistics | 4 | Introduction to Statistics, Data Organization, Measures of Central Tendency, Measures of Dispersion, Moments, Skewness, Kurtosis |
| CS181 | Programming in C - Lab | Core - Computer Science Lab | 2 | C Program Execution, Conditional Statements, Looping Constructs, Functions Implementation, Array Operations |
| MT181 | Mathematical Software - Lab (R) | Core - Mathematics Lab | 2 | Introduction to R, Data Structures in R, Data Manipulation, Statistical Graphics, Programming in R |
| EN101 | English I | Ability Enhancement Compulsory Course (AECC) | 3 | Grammar and Usage, Reading Comprehension, Writing Skills, Basic Communication Strategies, Presentation Skills |
| BA101 | Constitution of India & Human Rights | Ability Enhancement Compulsory Course (AECC) | 2 | Indian Constitution, Fundamental Rights, Directive Principles, Human Rights, Constitutional Amendments |
| VAC101 | Value Added Course | Value Added Course (VAC) | 1 | Generic skill development, Interpersonal communication, Problem-solving strategies, Ethical reasoning, Societal awareness |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS201 | Data Structures | Core - Computer Science | 4 | Introduction to Data Structures, Arrays, Stacks, Queues, Linked Lists, Trees, Graphs, Searching and Sorting |
| MT201 | Algebra | Core - Mathematics | 4 | Group Theory, Rings, Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors |
| ST201 | Probability and Distributions | Core - Statistics | 4 | Probability Theory, Random Variables, Probability Distributions, Expectation and Moments, Special Distributions (Binomial, Poisson, Normal) |
| CS281 | Data Structures - Lab | Core - Computer Science Lab | 2 | Stack and Queue Implementation, Linked List Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Techniques |
| MT281 | Discrete Mathematics - Lab | Core - Mathematics Lab | 2 | Logic Gates Simulation, Graph Theory Problems, Set Operations, Relations and Functions, Combinatorics |
| EN201 | English II | Ability Enhancement Compulsory Course (AECC) | 3 | Advanced Reading Strategies, Critical Thinking, Academic Writing, Report and Proposal Writing, Public Speaking |
| BA201 | Environmental Studies | Ability Enhancement Compulsory Course (AECC) | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Climate Change, Sustainable Development |
| VAC201 | Value Added Course | Value Added Course (VAC) | 1 | Generic skill development, Critical thinking, Leadership skills, Cultural awareness, Community engagement |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS301 | Object Oriented Programming using C++ | Core - Computer Science | 4 | OOP Concepts, Classes & Objects, Inheritance, Polymorphism, Virtual Functions, Exception Handling |
| MT301 | Real Analysis | Core - Mathematics | 4 | Real Number System, Sequences & Series, Limits & Continuity, Differentiation, Riemann Integration |
| ST301 | Sampling Techniques and Design of Experiments | Core - Statistics | 4 | Sampling Theory, Simple Random Sampling, Stratified and Systematic Sampling, Design of Experiments, ANOVA, CRD, RBD |
| CS381 | Object Oriented Programming using C++ - Lab | Core - Computer Science Lab | 2 | Class and Object Implementation, Constructor Overloading, Inheritance Examples, Polymorphism Practical, File I/O in C++ |
| SEC301 | Skill Enhancement Course I | Skill Enhancement Course (SEC) | 2 | Selected skill-based topics (e.g., Web Designing), Practical application of chosen skill, Problem-solving for specific domains, Tools and technologies training, Project-based learning |
| GE301 | Generic Elective I | Generic Elective (GE) | 3 | Interdisciplinary foundational concepts, Introduction to Humanities/Social Sciences, Basic principles of Economics/Psychology, Critical perspectives on contemporary issues, Communication and soft skills |
| RM301 | Research Methodology | Skill Enhancement Course (SEC) | 2 | Research Design, Data Collection Methods, Statistical Analysis Techniques, Report Writing, Ethics in Research |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS401 | Database Management Systems | Core - Computer Science | 4 | DBMS Concepts, ER Model, Relational Model, SQL Querying, Normalization, Transaction Management |
| MT401 | Complex Analysis | Core - Mathematics | 4 | Complex Numbers, Analytic Functions, Conformal Mapping, Complex Integration, Residue Theorem |
| ST401 | Statistical Inference | Core - Statistics | 4 | Point Estimation, Interval Estimation, Hypothesis Testing, Likelihood Ratio Tests, Non-parametric Tests |
| CS481 | Database Management Systems - Lab | Core - Computer Science Lab | 2 | SQL Queries, Database Creation, ER Diagram Tools, Stored Procedures, Data Manipulation Language |
| SEC401 | Skill Enhancement Course II | Skill Enhancement Course (SEC) | 2 | Selected skill-based topics (e.g., Python Programming), Advanced tool usage, Mini-project development, Industry standard practices, Problem diagnosis and resolution |
| GE401 | Generic Elective II | Generic Elective (GE) | 3 | Interdisciplinary foundational concepts, Introduction to Arts/Science topics, Global perspectives and current affairs, Ethical considerations in modern society, Cross-cultural communication |
| I401 | Internship | Internship | 2 | Practical industry exposure, Project implementation, Professional skill development, Report writing, Industry best practices |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS501 | Operating Systems | Core - Computer Science | 4 | OS Introduction, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| MT501 | Differential Equations | Core - Mathematics | 4 | First Order ODEs, Higher Order ODEs, Series Solutions, Partial Differential Equations, Laplace Transforms |
| ST501 | Regression Analysis and Econometrics | Core - Statistics | 4 | Simple Linear Regression, Multiple Regression, Regression Assumptions, Time Series Analysis, Index Numbers, Econometric Models |
| CS581 | Operating Systems - Lab | Core - Computer Science Lab | 2 | Linux Commands, Shell Scripting, Process Management, Memory Allocation Algorithms, File System Operations |
| DSE501 | Discipline Specific Elective I | Discipline Specific Elective (DSE) | 4 | Topics vary based on chosen specialization, Advanced concepts in Computer Science, Specialized areas in Mathematics, Applied Statistics methodologies, Research frontiers |
| DSE581 | Discipline Specific Elective I - Lab | Discipline Specific Elective (DSE) Lab | 2 | Practical implementation related to chosen DSE, Software tool utilization, Data analysis projects, Algorithm design and testing, Solution development |
| ST581 | Statistical Software - Lab | Core - Statistics Lab | 2 | Data analysis using R/Python, Hypothesis testing implementation, Regression analysis techniques, Data visualization, Statistical modeling |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS601 | Computer Networks | Core - Computer Science | 4 | Network Topologies, OSI Model, TCP/IP Suite, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| MT601 | Numerical Analysis | Core - Mathematics | 4 | Numerical Solutions of Equations, Interpolation, Numerical Differentiation, Numerical Integration, Numerical Solutions of ODEs |
| ST601 | Actuarial Statistics | Core - Statistics | 4 | Life Tables, Survival Models, Insurance Principles, Annuities, Premium Calculation, Risk Management |
| CS681 | Computer Networks - Lab | Core - Computer Science Lab | 2 | Network Configuration, Socket Programming, Network Packet Analysis, Client-Server Applications, Routing Protocols |
| DSE601 | Discipline Specific Elective II | Discipline Specific Elective (DSE) | 4 | Topics vary based on chosen specialization, Emerging technologies in Computer Science, Advanced mathematical modeling, Specialized statistical techniques, Interdisciplinary applications |
| DSE681 | Discipline Specific Elective II - Lab | Discipline Specific Elective (DSE) Lab | 2 | Practical implementation related to chosen DSE, Case studies and simulations, Data visualization and interpretation, System development and testing, Problem-solving for specific domains |
| RC601 | Research Component I | Research Component (RC) | 2 | Literature Review, Research Problem Identification, Methodology Design, Data Collection Planning, Research Proposal Writing |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS701 | Machine Learning | Core - Computer Science | 4 | Introduction to ML, Supervised Learning, Unsupervised Learning, Deep Learning basics, Model Evaluation, Reinforcement Learning |
| MT701 | Linear Programming | Core - Mathematics | 4 | Formulation of LPP, Graphical Method, Simplex Method, Duality Theory, Transportation Problem, Assignment Problem |
| ST701 | Time Series Analysis | Core - Statistics | 4 | Components of Time Series, Stationarity, ARIMA Models, Forecasting Methods, Spectral Analysis, Financial Time Series |
| CS781 | Machine Learning - Lab | Core - Computer Science Lab | 2 | Implementing ML Algorithms (Python/R), Data Preprocessing, Model Training and Tuning, Performance Evaluation Metrics, Machine Learning Libraries (Scikit-learn) |
| DSE701 | Discipline Specific Elective III | Discipline Specific Elective (DSE) | 4 | Topics vary based on chosen specialization, Advanced computational techniques, Pure and applied mathematics topics, Big Data Analytics and Statistical Learning, Research-oriented subjects |
| DSE781 | Discipline Specific Elective III - Lab | Discipline Specific Elective (DSE) Lab | 2 | Practical applications of advanced electives, Software development for specialized areas, Data science project implementation, Advanced numerical simulations, Research experimentation |
| RC701 | Research Component II | Research Component (RC) | 2 | Data Analysis and Interpretation, Scientific Report Writing, Presentation Skills, Research Paper Structuring, Ethical Considerations in Research |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| P801 | Project (Major) | Project | 12 | Problem Definition, Literature Survey, System Design and Architecture, Implementation and Development, Testing, Evaluation & Documentation, Project Presentation & Viva |
| DSE801 | Discipline Specific Elective IV | Discipline Specific Elective (DSE) | 4 | Topics vary based on chosen specialization, Industry-focused applications, Cutting-edge research areas, Advanced topics in Data Science, Emerging technologies and their impact |
| DSE881 | Discipline Specific Elective IV - Lab | Discipline Specific Elective (DSE) Lab | 2 | Practical skills for final year specialization, Advanced software tool usage, Complex problem-solving, Developing industry-ready solutions, Capstone project integration |
| RC801 | Research Component III | Research Component (RC) | 2 | Advanced Research Methods, Grant Proposal Writing, Publication Strategies, Intellectual Property Rights, Scientific Communication and Dissemination |
