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B-SC in Mathematics Statistics Computer Science at NIE First Grade College
Mysuru, Karnataka
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About the Specialization
What is Mathematics, Statistics, Computer Science at NIE First Grade College Mysuru?
This Mathematics, Statistics, Computer Science program at NIE First Grade College, Mysuru, focuses on building a strong foundation across three synergistic disciplines. It integrates rigorous mathematical reasoning with statistical analysis and computational problem-solving, equipping students for diverse analytical roles. The curriculum is designed to meet the growing demand in India for professionals who can leverage data, algorithms, and logical structures to address complex challenges across various industries.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude in science and mathematics, seeking a multi-faceted degree. It attracts fresh graduates aiming for entry-level roles in data analytics, software development, or research. Professionals looking to upskill in quantitative methods or transition into technical roles in finance, IT, or scientific research within the Indian market will also find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including Data Scientist, Business Analyst, Software Developer, Actuarial Analyst, or Quantitative Researcher. Entry-level salaries typically range from INR 3-6 LPA, growing significantly with experience. The interdisciplinary nature fosters strong analytical and problem-solving skills, highly valued in Indian companies, and provides a solid base for advanced studies or professional certifications in AI, Machine Learning, or Big Data.
Student Success Practices
Foundation Stage
Master Core Concepts and Problem Solving- (Semester 1-2)
Dedicate consistent time to understand fundamental concepts in algebra, calculus, C programming, and descriptive statistics. Focus on solving a wide variety of problems from textbooks and online platforms to build a strong analytical base. This includes attending tutorials regularly and clarifying doubts immediately.
Tools & Resources
NCERT/Reference Textbooks, Khan Academy, GeeksforGeeks for C programming, Peer study groups
Career Connection
A strong foundation is crucial for excelling in advanced subjects and forms the bedrock for technical interviews and analytical roles in India''''s competitive job market.
Develop Programming Proficiency Early- (Semester 1-2)
Beyond classroom assignments, actively participate in coding challenges and practice platforms for C programming. Understand data structures and algorithms conceptually and implement them hands-on. Early exposure to practical coding enhances problem-solving logic.
Tools & Resources
HackerRank, CodeChef, LeetCode (for beginners), Online C compilers
Career Connection
Proficiency in programming is a direct pathway to software development, data analytics, and computational roles prevalent in India''''s booming tech sector.
Engage in Interdisciplinary Study Groups- (Semester 1-2)
Form study groups with peers from different subject interests (e.g., a math enthusiast, a stats buff, and a CS coder). This fosters diverse perspectives, helps in understanding how concepts from one discipline apply to another, and prepares for real-world collaborative projects.
Tools & Resources
College library, Google Meet/Zoom for virtual discussions, Whiteboards for collaborative problem-solving
Career Connection
Collaboration and interdisciplinary thinking are highly valued in modern Indian workplaces, especially in data science and tech teams, improving teamwork and communication skills.
Intermediate Stage
Apply Concepts to Real-world Data- (Semester 3-5)
Actively seek opportunities to apply statistical methods and programming skills to real datasets. Work on small projects involving data collection, cleaning, analysis, and visualization. Use tools like Excel, R, or Python for practical implementation of concepts learned in statistics and computer science.
Tools & Resources
Kaggle for datasets, R/Python for statistical computing, Jupyter Notebooks, Google Sheets/Excel
Career Connection
Practical data handling skills are highly sought after by analytics firms and IT companies in India, leading to roles like Data Analyst or Junior Statistician.
Explore Open-Source Contributions and Mini-Projects- (Semester 3-5)
Beyond syllabus, explore basic concepts of web development or simple application building. Contribute to open-source projects or undertake personal mini-projects. This showcases initiative and practical skill application, differentiating students in the competitive Indian job market.
Tools & Resources
GitHub, FreeCodeCamp, Stack Overflow, Coursera/Udemy for specific skill courses
Career Connection
Developing a portfolio of projects is essential for placements in software development, showcasing practical skills and enhancing resume appeal for Indian tech companies.
Network and Attend Workshops/Seminars- (Semester 3-5)
Attend college-organized workshops, webinars, and seminars focused on emerging technologies, data science, or mathematical applications. Network with guest speakers, alumni, and industry professionals to gain insights into industry trends and potential career paths in India.
Tools & Resources
LinkedIn, College career cell events, Industry association events (e.g., NASSCOM, CSI)
Career Connection
Networking opens doors to internships, mentorships, and early career opportunities, crucial for navigating the Indian professional landscape and understanding industry expectations.
Advanced Stage
Undertake a Comprehensive Project or Research- (Semester 6)
Work on a significant project, ideally interdisciplinary, that integrates mathematics, statistics, and computer science. This could be a data analysis project, a software development project with a mathematical model, or a statistical study. Focus on problem definition, methodology, implementation, and detailed reporting.
Tools & Resources
Git/Version Control, Advanced IDEs (VS Code, PyCharm), Jupyter notebooks for presentation, Access to faculty guidance
Career Connection
A robust project is a powerful resume booster, demonstrating problem-solving capabilities and practical application of knowledge, highly valued by Indian employers for direct placements.
Intensive Placement Preparation and Skill Specialization- (Semester 6)
Engage in rigorous placement preparation, focusing on aptitude tests, logical reasoning, and technical interview skills. Identify a niche area (e.g., Machine Learning, Actuarial Science, Financial Modeling) and pursue advanced online courses or certifications to specialize and stand out for specific roles in India.
Tools & Resources
Online aptitude platforms (e.g., IndiaBix), Mock interview platforms, NPTEL courses, Udemy/Coursera certifications
Career Connection
Targeted preparation and specialization significantly increase chances of securing competitive placements with higher salary packages in Indian IT, analytics, and finance sectors.
Build a Professional Portfolio and Online Presence- (Semester 6)
Create a professional online portfolio showcasing projects, coding skills, and analytical work. Develop a strong LinkedIn profile highlighting academic achievements, skills, and project experiences. Actively participate in hackathons or coding competitions to demonstrate competitive skills.
Tools & Resources
GitHub portfolio, LinkedIn profile, Personal website/blog (optional), Participation in college/inter-college competitions
Career Connection
An impressive online presence and portfolio are critical for attracting recruiters from Indian companies, facilitating direct hires and enhancing visibility in the professional community.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 or equivalent examination with Science subjects (Mathematics, Physics, Chemistry, Computer Science etc.) from a recognized board.
Duration: 6 semesters / 3 years
Credits: 140 Credits
Assessment: Internal: 20%, External: 80%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BM1 | Algebra and Calculus - I | Core Theory | 4 | Set Theory and Functions, Limits and Continuity, Differentiation Techniques, Integration Methods, Sequences and Series |
| BML1 | Mathematics Practical - I | Core Practical | 2 | Graphing Functions, Numerical Differentiation, Numerical Integration, Matrix Operations, Solving Equations |
| ST1 | Descriptive Statistics and Probability - I | Core Theory | 4 | Data Collection and Presentation, Measures of Central Tendency, Measures of Dispersion, Skewness and Kurtosis, Basic Probability Concepts |
| STL1 | Statistics Practical - I | Core Practical | 2 | Data Tabulation, Diagrams and Graphs, Calculating Central Tendency, Computing Dispersion Measures, Probability Experiments |
| CS1 | Foundations of Computer Science | Core Theory | 4 | Introduction to Computers, Number Systems, Boolean Algebra and Logic Gates, Basic Operating System Concepts, Software Categories and Applications |
| CSL1 | Computer Science Practical - I | Core Practical | 2 | Basic DOS/Linux Commands, File Operations, MS Office Applications, Basic Logic Gate Simulation, Simple Flowcharting |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BM2 | Algebra and Calculus - II | Core Theory | 4 | Matrices and Determinants, Systems of Linear Equations, Vector Algebra, Partial Differentiation, Multiple Integrals |
| BML2 | Mathematics Practical - II | Core Practical | 2 | Matrix Inverse Calculation, Vector Operations, Solving Systems of Equations, Multivariable Function Analysis, Plotting 3D Surfaces |
| ST2 | Descriptive Statistics and Probability - II | Core Theory | 4 | Random Variables, Probability Distributions (Binomial, Poisson, Normal), Mathematical Expectation, Correlation Analysis, Regression Analysis |
| STL2 | Statistics Practical - II | Core Practical | 2 | Fitting Probability Distributions, Calculating Moments, Correlation Coefficient, Regression Line Fitting, Data Analysis using Software |
| CS2 | Programming in C | Core Theory | 4 | C Language Fundamentals, Control Structures, Arrays and Strings, Functions and Pointers, Structures, Unions and File Handling |
| CSL2 | Computer Science Practical - II | Core Practical | 2 | C Program Development, Implementing Control Statements, Array and String Operations, Pointer-based Programs, File Input/Output in C |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BM3 | Real Analysis - I & Calculus - III | Core Theory | 4 | Sequences and Series Convergence, Continuity and Differentiability, Riemann Integration, Improper Integrals, Gamma and Beta Functions |
| BML3 | Mathematics Practical - III | Core Practical | 2 | Testing Series Convergence, Graphing Functions for Continuity, Numerical Riemann Integration, Improper Integral Evaluation, Properties of Gamma and Beta Functions |
| ST3 | Statistical Methods - I | Core Theory | 4 | Sampling Distributions, Point and Interval Estimation, Principles of Hypothesis Testing, t-tests, Chi-square tests, F-tests and ANOVA |
| STL3 | Statistics Practical - III | Core Practical | 2 | Constructing Confidence Intervals, Performing Hypothesis Tests, ANOVA Table Construction, Chi-square Test for Independence, Using Statistical Software for Inference |
| CS3 | Data Structures | Core Theory | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Traversal, Searching and Sorting Algorithms |
| CSL3 | Computer Science Practical - III | Core Practical | 2 | Implementing Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Representation and Traversal, Coding Search and Sort Algorithms |
| AECC1 | Environmental Studies | Ability Enhancement Compulsory Course | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Environmental Ethics, Sustainable Development |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BM4 | Real Analysis - II & Calculus - IV | Core Theory | 4 | Metric Spaces, Uniform Convergence, Functions of Several Variables, Maxima and Minima of Functions, First Order Differential Equations |
| BML4 | Mathematics Practical - IV | Core Practical | 2 | Properties of Metric Spaces, Analyzing Multivariable Functions, Solving First Order ODEs, Numerical Solutions of PDEs, Vector Field Computations |
| ST4 | Statistical Methods - II | Core Theory | 4 | Non-parametric Tests, Factorial Experiments, Multiple Regression Analysis, Time Series Analysis, Index Numbers |
| STL4 | Statistics Practical - IV | Core Practical | 2 | Applying Non-parametric Tests, Conducting Factorial ANOVA, Building Multiple Regression Models, Time Series Forecasting, Calculating Index Numbers |
| CS4 | OOP using C++ | Core Theory | 4 | Classes and Objects, Inheritance and Polymorphism, Constructors and Destructors, Virtual Functions and Friend Functions, Templates and Exception Handling |
| CSL4 | Computer Science Practical - IV | Core Practical | 2 | C++ Class Implementation, Inheritance Hierarchy Design, Polymorphism Demonstration, Template Programming, Exception Handling in C++ |
| AECC2 | Constitution of India | Ability Enhancement Compulsory Course | 2 | Preamble and Basic Structure, Fundamental Rights and Duties, Directive Principles of State Policy, Union and State Governments, Indian Judiciary System |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BM5 | Abstract Algebra - I & Differential Equations - I | Core Theory | 4 | Groups and Subgroups, Cyclic Groups and Cosets, Normal Subgroups and Homomorphisms, Solution Methods for First Order ODEs, Linear Differential Equations |
| BM6 | Numerical Methods & Vector Calculus | Core Theory | 4 | Numerical Solution of Equations, Interpolation Techniques, Numerical Differentiation and Integration, Vector Differentiation (Gradient, Divergence, Curl), Line and Surface Integrals |
| BML5 | Mathematics Practical - V | Core Practical | 2 | Group Properties Verification, Solving ODEs numerically, Interpolation using Software, Vector Calculus Computations, Implementing Numerical Integration |
| ST5 | Statistical Inference - I | Core Theory | 4 | Estimation Theory, Properties of Estimators, Methods of Estimation (MLE, MOM), Hypothesis Testing Concepts, Neyman-Pearson Lemma |
| ST6 | Sampling Techniques and Design of Experiments | Core Theory | 4 | Simple Random Sampling, Stratified and Systematic Sampling, Cluster and Two-stage Sampling, Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD) |
| STL5 | Statistics Practical - V | Core Practical | 2 | Constructing Confidence Intervals, Estimating Population Parameters, Designing CRD and RBD Experiments, Analyzing Sample Survey Data, Hypothesis Testing using R/Python |
| CS5 | Database Management System | Core Theory | 4 | DBMS Architecture and Data Models, Entity-Relationship (ER) Model, Relational Algebra and Calculus, Structured Query Language (SQL), Normalization and Transaction Management |
| CS6 | Operating System | Core Theory | 4 | Operating System Functions, Process Management and CPU Scheduling, Memory Management Techniques, Virtual Memory Concepts, File Systems and I/O Management |
| CSL5 | Computer Science Practical - V | Core Practical | 2 | SQL Query Implementation, Database Schema Design, Process Scheduling Algorithms, Memory Allocation Simulation, File System Operations |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BM7 | Abstract Algebra - II & Differential Equations - II | Core Theory | 4 | Rings, Fields, Integral Domains, Ideals and Quotient Rings, Vector Spaces and Subspaces, Partial Differential Equations, Classification of PDEs |
| BM8 | Linear Algebra & Complex Analysis | Core Theory | 4 | Linear Transformations, Eigenvalues and Eigenvectors, Diagonalization, Complex Numbers and Functions, Analytic Functions and Cauchy-Riemann Equations |
| BML6 | Mathematics Practical - VI | Core Practical | 2 | Ring and Field Properties, Solving PDEs Analytically, Eigenvalue/Eigenvector Calculations, Complex Function Plotting, Applications of Linear Algebra |
| ST7 | Statistical Inference - II | Core Theory | 4 | Large Sample Theory, Sequential Probability Ratio Test, Bayesian Inference, Decision Theory, Goodness of Fit Tests |
| ST8 | Operations Research and Quality Control | Core Theory | 4 | Linear Programming Problems (LPP), Transportation and Assignment Problems, Queuing Theory, Statistical Quality Control (Control Charts), Acceptance Sampling |
| STL6 | Statistics Practical - VI | Core Practical | 2 | Solving LPP using Simplex Method, Constructing Control Charts, Queuing Model Simulations, Applying Acceptance Sampling Plans, Decision Making under Uncertainty |
| CS7 | Data Communication and Computer Networks | Core Theory | 4 | OSI and TCP/IP Models, Network Topologies and Devices, Data Transmission Media, IP Addressing and Subnetting, Routing Protocols and Network Security |
| CS8 | Web Programming | Core Theory | 4 | HTML5 and CSS3 for Web Design, JavaScript for Client-side Scripting, DOM Manipulation and Events, Introduction to PHP/ASP.NET, Web Servers and Database Connectivity |
| CSL6 | Computer Science Practical - VI | Core Practical | 2 | Network Configuration Exercises, HTML/CSS Website Development, JavaScript Interactive Pages, Basic PHP Web Applications, Client-Server Communication |
| CSP | Project Work | Project | 4 | Project Planning and Requirements Analysis, System Design and Architecture, Implementation and Coding, Testing and Debugging, Documentation and Presentation |
