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BSC in Mathematics Statistics Computer Science Msc at Sri K. Puttaswamy First Grade College
Mysuru, Karnataka
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About the Specialization
What is Mathematics, Statistics, Computer Science (MSC) at Sri K. Puttaswamy First Grade College Mysuru?
This Mathematics, Statistics, Computer Science (MSC) program at Sri K. Puttaswamy First Grade College, Mysuru, focuses on providing a robust foundation in three critical quantitative and computational disciplines. Tailored to meet the growing demand in the Indian technology and analytics sectors, the program integrates theoretical knowledge with practical applications, equipping students for diverse roles. Its multidisciplinary approach offers a unique blend of analytical rigor, statistical insight, and computational prowess.
Who Should Apply?
This program is ideal for fresh graduates seeking entry into data analysis, software development, or research roles. It also suits individuals with a strong aptitude for problem-solving and logical reasoning who aim to pursue higher education in specialized areas like Data Science, Actuarial Science, or Quantitative Finance. Career changers looking to transition into analytical or tech-oriented industries will also find this curriculum beneficial, provided they meet the science stream prerequisites.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths in IT, finance, research, and analytics. Roles include Data Analyst (entry salary: ₹3-6 LPA), Software Developer (₹3-7 LPA), Business Analyst (₹4-8 LPA), Statistician (₹3-6 LPA), or Quantitative Analyst. Growth trajectories in Indian companies often lead to senior analyst, project lead, or data scientist positions. The comprehensive foundation also prepares students for competitive exams and higher studies like MCA, MBA, or M.Sc. in related fields.
Student Success Practices
Foundation Stage
Master Core Programming & Math Logic- (Semester 1-2)
Dedicate significant time to fundamental programming (C language) and mathematical concepts (Algebra, Calculus). Actively solve problems from textbooks and online platforms. Understand the logical flow and problem-solving techniques inherent in both disciplines.
Tools & Resources
GeeksforGeeks for C programming, NPTEL/Coursera for fundamental math courses, Khan Academy for concept clarity, College''''s computer labs
Career Connection
A strong grasp of programming and mathematical logic is foundational for all advanced technical roles, enabling efficient coding, algorithm development, and analytical problem-solving required in software and data industries.
Build Data Analysis Basics with Practical Tools- (Semester 1-2)
Focus on understanding descriptive statistics and probability theory. Simultaneously gain hands-on experience with statistical software like R or Excel for data manipulation, visualization, and basic statistical inference. Participate in college workshops on data tools.
Tools & Resources
R-Studio for statistical computing, Microsoft Excel for data management, Online tutorials from Coursera/edX for R basics, NSS/NCC for soft skill development
Career Connection
Early proficiency in data analysis tools like R and Excel directly translates to entry-level Data Analyst, Business Intelligence, and Research Assistant roles, which are prevalent in the Indian job market.
Engage in Peer Learning & Problem Solving Groups- (Semester 1-2)
Form study groups with peers to discuss complex mathematical problems, debug code together, and clarify statistical concepts. Collaborative learning enhances understanding, exposes diverse problem-solving approaches, and builds teamwork skills.
Tools & Resources
College library study rooms, Online collaboration tools like Google Docs/Meet, Departmental faculty for guidance
Career Connection
Teamwork and collaborative problem-solving are highly valued in modern workplaces, especially in IT and R&D teams, preparing students for effective contribution in project-based environments.
Intermediate Stage
Deepen Data Structures & OOP Skills- (Semester 3-4)
Master advanced data structures (trees, graphs) and Object-Oriented Programming (C++). Implement complex algorithms and OOP principles from scratch. Participate in coding competitions and hackathons to apply learned concepts.
Tools & Resources
HackerRank, LeetCode for coding practice, GitHub for version control and project showcase, University/College coding clubs
Career Connection
Proficiency in Data Structures and Algorithms (DSA) and OOP is crucial for cracking technical interviews at top Indian tech companies and for developing efficient, scalable software solutions.
Explore Statistical Modeling & Inferential Techniques- (Semester 3-4)
Delve into statistical inference, hypothesis testing, and sampling techniques. Work on mini-projects involving real-world datasets to apply these methods, focusing on drawing conclusions and making informed decisions. Utilize R for complex statistical computations.
Tools & Resources
Kaggle for datasets, R-Studio for advanced statistical analysis, NPTEL courses on Statistical Inference, Academic journals for case studies
Career Connection
Strong inferential statistics skills are highly sought after in market research, actuarial science, quality control, and data science roles within Indian industries, enabling data-driven insights.
Seek Internships & Industry Exposure- (Semester 3-4)
Actively look for short-term internships during summer breaks in local startups, IT firms, or research institutions. This provides practical industry experience, helps in understanding corporate culture, and builds professional networks.
Tools & Resources
LinkedIn, Internshala, Indeed for internship search, College placement cell, Networking events
Career Connection
Internships are vital for gaining practical experience, making industry connections, and often lead to pre-placement offers (PPOs) in Indian companies, significantly boosting employability.
Advanced Stage
Specialize through Advanced Electives & Project Work- (Semester 5-6)
Choose advanced electives aligning with career aspirations (e.g., AI, Cloud Computing, Operations Research). Undertake a capstone project/dissertation using the combined knowledge of Mathematics, Statistics, and Computer Science to solve a significant problem.
Tools & Resources
Relevant IDEs for development (e.g., VS Code, Eclipse), Cloud platforms (AWS, Azure, GCP free tiers), Project management tools (Jira, Trello)
Career Connection
Specialized knowledge and a substantial project demonstrate expertise to potential employers, positioning graduates for roles in emerging fields like Data Scientist, AI Engineer, or Cloud Specialist with higher earning potential.
Intensive Placement Preparation & Mock Interviews- (Semester 5-6)
Engage in rigorous preparation for campus placements, including aptitude tests, technical rounds, and HR interviews. Participate in mock interviews, group discussions, and resume-building workshops organized by the college''''s placement cell.
Tools & Resources
Placement training companies/modules, Online aptitude platforms (IndiaBix), Mock interview sessions with faculty/alumni
Career Connection
Comprehensive placement preparation is crucial for securing desirable job offers from top recruiters during campus placements, which is a primary career entry point for Indian graduates.
Network with Alumni & Industry Professionals- (Semester 5-6)
Attend industry seminars, guest lectures, and alumni meets. Leverage these opportunities to build a professional network, seek mentorship, and gain insights into career trends and opportunities in the Indian market.
Tools & Resources
LinkedIn for professional networking, College alumni portal, Industry events and conferences (e.g., Data Science Summits, Tech Fests)
Career Connection
Networking opens doors to hidden job opportunities, mentorship, and keeps students informed about industry dynamics, providing a competitive edge in their career trajectory in India.
Program Structure and Curriculum
Eligibility:
- Pass in 10+2 (PUC or equivalent) with Science subjects (Physics, Chemistry, Mathematics/Computer Science/Statistics) from a recognized board.
Duration: 6 Semesters (3 years for Basic B.Sc.)
Credits: ~166 (Based on detailed per-semester credit breakdown in UoM NEP documents; note a discrepancy with the stated 100 credits for Basic B.Sc. in overview tables) Credits
Assessment: Internal: 40% (for Theory courses), 50% (for Practical courses), External: 60% (for Theory courses - End Semester Exam), 50% (for Practical courses - End Semester Exam)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21KAA1 | Kannada | AECC (Ability Enhancement Compulsory Course) | 2 | Kannada Language and Literature, Grammar, Prose and Poetry, Cultural Aspects, Communication Skills |
| 21EAA1 | English | AECC (Ability Enhancement Compulsory Course) | 2 | Communication Skills, Grammar and Usage, Reading Comprehension, Literary Texts, Writing Skills |
| 21VAC1 | Indian Constitution | VAC (Value Added Course) | 2 | Constitutional Framework, Fundamental Rights and Duties, Directive Principles, Union and State Governments, Judiciary |
| 21VAC2 | Environmental Studies | VAC (Value Added Course) | 2 | Ecology and Ecosystems, Biodiversity, Environmental Pollution, Natural Resources, Conservation |
| 21MDCM1 | Algebra | Major Discipline Core Course (Mathematics) | 4 | Set Theory, Group Theory, Ring Theory, Vector Spaces, Matrices and Determinants |
| 21MDCP1 | Problem Solving in Algebra (Practical) | Major Discipline Core Practical (Mathematics) | 2 | Matrix Operations, Solving Linear Equations, Group Properties Verification, Boolean Algebra Applications |
| 21MDCS1 | Descriptive Statistics | Major Discipline Core Course (Statistics) | 4 | Data Collection and Presentation, Measures of Central Tendency, Measures of Dispersion, Moments, Skewness and Kurtosis, Correlation and Regression |
| 21MDSP1 | Data Analysis using R/Excel (Practical) | Major Discipline Core Practical (Statistics) | 2 | Data Entry and Cleaning, Calculation of Descriptive Measures, Graphical Representation, Correlation & Regression Analysis in Software |
| 21MDCC1 | Computer Fundamentals and Digital Fluency | Major Discipline Core Course (Computer Science) | 4 | Introduction to Computers, Number Systems and Codes, Boolean Algebra and Logic Gates, Memory and Storage Devices, Operating System Concepts |
| 21MDCPCS1 | Digital Fluency Lab (Practical) | Major Discipline Core Practical (Computer Science) | 2 | Word Processing Tools, Spreadsheet Applications, Presentation Software, Internet Browsing and Email, Basic Cyber Security Practices |
| 21SEC1 | Skill Enhancement Course - 1 | Skill Enhancement Course | 2 | Communication Skills, Professional Ethics, Teamwork, Problem-solving, Time Management |
| 21OE1 | Open Elective - 1 | Open Elective | 3 | Interdisciplinary subject chosen by student |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21KAA2 | Kannada | AECC (Ability Enhancement Compulsory Course) | 2 | Advanced Kannada Grammar, Modern Kannada Literature, Functional Kannada, Writing and Translation, Karnataka Culture |
| 21EAA2 | English | AECC (Ability Enhancement Compulsory Course) | 2 | Advanced Communication Strategies, Public Speaking, Creative Writing, Critical Reading, Research Skills |
| 21VAC3 | Human Rights and Ethics | VAC (Value Added Course) | 2 | Concepts of Human Rights, Universal Declaration of Human Rights, Ethical Theories, Professional Ethics, Social Justice |
| 21VAC4 | Introduction to Artificial Intelligence/Data Science | VAC (Value Added Course) | 2 | Basics of AI, Machine Learning Concepts, Data Analysis Fundamentals, Big Data, Applications of AI |
| 21MDCM2 | Calculus | Major Discipline Core Course (Mathematics) | 4 | Differential Calculus, Integral Calculus, Partial Differentiation, Multiple Integrals, Applications of Calculus |
| 21MDCP2 | Problem Solving in Calculus (Practical) | Major Discipline Core Practical (Mathematics) | 2 | Limits and Continuity Problems, Derivatives and Integrals, Area and Volume Calculation, Maxima and Minima, Vector Calculus basics |
| 21MDCS2 | Probability Theory and Distributions | Major Discipline Core Course (Statistics) | 4 | Basic Probability Concepts, Random Variables, Mathematical Expectation, Discrete Probability Distributions, Continuous Probability Distributions |
| 21MDSP2 | Probability and Distributions Lab using R/Excel (Practical) | Major Discipline Core Practical (Statistics) | 2 | Probability Calculations, Random Variable Simulation, Fitting Probability Distributions, Hypothesis Testing Basics |
| 21MDCC2 | Programming in C | Major Discipline Core Course (Computer Science) | 4 | C Language Fundamentals, Data Types and Operators, Control Structures, Functions and Arrays, Pointers and Structures, File I/O in C |
| 21MDCPCS2 | C Programming Lab (Practical) | Major Discipline Core Practical (Computer Science) | 2 | Implementing basic C programs, Using loops and conditional statements, Working with arrays and functions, Pointer manipulation, File handling in C |
| 21SEC2 | Skill Enhancement Course - 2 | Skill Enhancement Course | 2 | Critical Thinking, Data Interpretation, Decision Making, Digital Literacy, Entrepreneurial Skills |
| 21OE2 | Open Elective - 2 | Open Elective | 3 | Interdisciplinary subject chosen by student |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MDCM3 | Real Analysis | Major Discipline Core Course (Mathematics) | 4 | Real Number System, Sequences and Series, Limits and Continuity, Differentiability, Riemann Integration |
| 22MDCP3 | Problem Solving in Real Analysis (Practical) | Major Discipline Core Practical (Mathematics) | 2 | Convergence of Sequences and Series, Properties of Continuous Functions, Applications of Derivatives, Integral Calculations |
| 22MDCS3 | Statistical Inference | Major Discipline Core Course (Statistics) | 4 | Sampling Distributions, Point Estimation, Interval Estimation, Hypothesis Testing, Parametric and Non-Parametric Tests |
| 22MDSP3 | Statistical Inference Lab using R/Excel (Practical) | Major Discipline Core Practical (Statistics) | 2 | Confidence Intervals in Software, Hypothesis Testing Procedures, Chi-square Tests, ANOVA using R/Excel |
| 22MDCC3 | Data Structures | Major Discipline Core Course (Computer Science) | 4 | Arrays and Pointers, Stacks and Queues, Linked Lists, Trees and Graphs, Searching and Sorting Algorithms |
| 22MDCPCS3 | Data Structures Lab (Practical) | Major Discipline Core Practical (Computer Science) | 2 | Implementation of Stacks and Queues, Linked List Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Programs |
| 22SEC3 | Skill Enhancement Course - 3 | Skill Enhancement Course | 2 | Professional Communication, Interview Skills, Resume Building, Presentation Skills, Conflict Resolution |
| 22OE3 | Open Elective - 3 | Open Elective | 3 | Interdisciplinary subject chosen by student |
Semester 4
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MDCC5.1 | Database Management Systems | Major Discipline Core Course (Computer Science) | 4 | Database Concepts and Architecture, ER Modeling, Relational Algebra and Calculus, SQL Queries and Constraints, Normalization and Transaction Management |
| 23MDCC5.2 | Java Programming | Major Discipline Core Course (Computer Science) | 4 | Java Fundamentals, Classes, Objects, Inheritance, Interfaces and Packages, Exception Handling, Multithreading and Collections |
| 23MDCC5.3 | Operating Systems | Major Discipline Core Course (Computer Science) | 4 | OS Concepts and Functions, Process Management, CPU Scheduling, Memory Management, File Systems and I/O Management |
| 23MDCC5.4 | Computer Networks | Major Discipline Core Course (Computer Science) | 4 | Network Topologies and Models (OSI, TCP/IP), Data Link Layer, Network Layer (IP Addressing, Routing), Transport Layer (TCP, UDP), Application Layer Protocols |
| 23MDEC5.1 | Web Programming | Major Discipline Elective Course (Computer Science) | 3 | HTML5 and CSS3, JavaScript Fundamentals, DOM Manipulation, Responsive Design, Web Development Frameworks (basics) |
| 23MDEC5.2 | Python for Data Science | Major Discipline Elective Course (Computer Science) | 3 | Python Basics for Data Analysis, NumPy for numerical operations, Pandas for data manipulation, Matplotlib for data visualization, Introduction to Machine Learning Libraries |
| 23MDPW5 | Major Project Work / Dissertation | Major Discipline Project Work (Computer Science) | 4 | Problem Identification, Literature Review, System Design, Implementation and Testing, Report Writing and Presentation |
| 23SEC5 | Skill Enhancement Course - 5 | Skill Enhancement Course | 2 | Advanced Software Tools, Cloud Computing Basics, Cybersecurity Awareness, Ethical Hacking, Data Privacy Regulations |
| 23OE5 | Open Elective - 5 (Optional relevant for MSC students) | Open Elective | 3 | Mathematical Modeling, Applied Statistics, Machine Learning Principles, Financial Mathematics |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MDCC6.1 | Web Technologies | Major Discipline Core Course (Computer Science) | 4 | Advanced HTML/CSS, Client-Side Scripting (JavaScript frameworks), Server-Side Scripting (Node.js/PHP basics), Database Integration (MySQL/MongoDB), Web Security |
| 23MDCC6.2 | Software Engineering | Major Discipline Core Course (Computer Science) | 4 | Software Development Life Cycle (SDLC), Requirements Engineering, Software Design Principles, Software Testing, Project Management |
| 23MDCC6.3 | Artificial Intelligence | Major Discipline Core Course (Computer Science) | 4 | Introduction to AI, Search Algorithms, Knowledge Representation, Machine Learning Paradigms, Natural Language Processing basics |
| 23MDCC6.4 | Cloud Computing | Major Discipline Core Course (Computer Science) | 4 | Cloud Computing Concepts, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security and Management |
| 23MDEC6.1 | Mobile Application Development | Major Discipline Elective Course (Computer Science) | 3 | Android/iOS Architecture, UI/UX Design for Mobile, Kotlin/Java for Android, Swift/Objective-C for iOS (basics), API Integration |
| 23MDEC6.2 | Internet of Things (IoT) | Major Discipline Elective Course (Computer Science) | 3 | IoT Architecture and Components, Sensors and Actuators, IoT Communication Protocols, Data Analytics in IoT, IoT Security and Privacy |
| 23SEC6 | Skill Enhancement Course - 6 | Skill Enhancement Course | 2 | Project Management Tools, Agile Methodologies, Data Visualization, Intellectual Property Rights, Entrepreneurship Basics |
| 23OE6 | Open Elective - 6 (Optional relevant for MSC students) | Open Elective | 3 | Operations Research, Statistical Quality Control, Big Data Analytics, Bioinformatics |
