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B-SC in Computer Science Mathematics Statistics at The Oxford College of Arts
Bengaluru, Karnataka
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About the Specialization
What is Computer Science, Mathematics, Statistics at The Oxford College of Arts Bengaluru?
This B.Sc. (Computer Science, Mathematics, Statistics) program at The Oxford College of Arts focuses on building a robust foundation in logical reasoning, computational thinking, and data analysis. It is designed to meet the growing demands of India''''s technology and data-driven industries, preparing students with interdisciplinary skills essential for complex problem-solving. The unique combination equips graduates with both theoretical depth and practical application expertise, making them versatile assets in the Indian job market.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude in science, especially mathematics, who are keen to explore the intersection of computing, abstract reasoning, and data interpretation. It caters to those aspiring for analytical, research-oriented, or software development roles, as well as working professionals looking to transition into data science or quantitative analysis. Specific prerequisite backgrounds include a 10+2 science stream with Mathematics.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths in IT, finance, research, and data analytics. Roles such as Data Analyst, Software Developer, Quantitative Analyst, Statistician, or Research Assistant are common. Entry-level salaries can range from INR 3-5 LPA, growing significantly with experience to INR 8-15+ LPA. The program aligns with industry demands for professionals skilled in algorithms, statistical modeling, and computational tools, leading to excellent growth trajectories in Indian companies and startups.
Student Success Practices
Foundation Stage
Strengthen Core Fundamentals- (Semester 1-2)
Dedicate consistent time to understanding basic programming logic, mathematical theorems, and statistical concepts. Utilize online platforms like NPTEL and Khan Academy for supplementary learning and problem-solving to reinforce classroom teachings. Form small study groups for peer-to-peer learning and doubt clearing sessions.
Tools & Resources
NPTEL (for Computer Science, Mathematics, Statistics), Khan Academy, GeeksforGeeks, Local study groups
Career Connection
A strong grasp of fundamentals is crucial for excelling in advanced subjects and forms the bedrock for technical interviews and coding challenges during placements.
Develop Practical Coding Skills Early- (Semester 1-2)
Regularly practice coding problems in C/C++ alongside theoretical classes. Focus on implementing basic data structures and algorithms. Participate in coding competitions on platforms like HackerRank or CodeChef to sharpen logical thinking and problem-solving abilities from the outset.
Tools & Resources
HackerRank, CodeChef, LeetCode (Beginner problems), IDEs like VS Code, Dev C++
Career Connection
Early coding proficiency is vital for securing internships and entry-level developer roles, as it demonstrates practical application of theoretical knowledge.
Master Data Interpretation and Visualization- (Semester 1-2)
Alongside descriptive statistics, practice interpreting real-world datasets and visualize them using basic tools. Familiarize yourself with Excel''''s data analysis features and start exploring R or Python for statistical graphing. Understand how to present data insights clearly.
Tools & Resources
Microsoft Excel, R/Python (basic libraries like ggplot2/Matplotlib), Online datasets (Kaggle), Data visualization tutorials
Career Connection
This skill is fundamental for data analyst roles and helps in understanding research papers and industry reports, improving analytical communication.
Intermediate Stage
Build Project Portfolio and Apply Concepts- (Semester 3-5)
Work on mini-projects that integrate Computer Science, Mathematics, and Statistics concepts. For example, build a small database application with statistical reporting or implement a mathematical algorithm in code. Actively seek out faculty guidance for project ideas and execution.
Tools & Resources
GitHub (for version control), SQL databases (MySQL, PostgreSQL), Integrated Development Environments, Project management tools (Trello, Asana)
Career Connection
A robust project portfolio showcases practical skills to potential employers and is a key differentiator in internships and job applications, especially for full-stack or data roles.
Gain Industry Exposure through Internships/Workshops- (Semester 3-5)
Actively search for internships during summer breaks in areas like software development, data analysis, or quantitative research. Attend industry workshops, seminars, and guest lectures organized by the college or local tech communities to understand current trends and network with professionals.
Tools & Resources
LinkedIn, Internshala, College career cell, Local tech meetups
Career Connection
Internships provide invaluable real-world experience, making students job-ready and often leading to pre-placement offers. Networking opens doors to future opportunities.
Specialized Skill Development and Certifications- (Semester 3-5)
Identify areas of interest (e.g., Machine Learning, Operations Research, Advanced Statistics) and pursue online certifications or advanced courses. Platforms like Coursera, edX, or Google Certifications can provide industry-recognized credentials that complement the university curriculum.
Tools & Resources
Coursera, edX, Udemy, Google IT Support Professional Certificate, SAS/SPSS certifications
Career Connection
Specialized skills and certifications enhance your resume, making you more competitive for specific roles in growing sectors like AI/ML, FinTech, or Biostatistics.
Advanced Stage
Intensive Placement Preparation- (Semester 6)
Focus intensely on aptitude, logical reasoning, verbal ability, and technical interview preparation. Practice coding questions, revise core CS, Math, and Stats concepts, and participate in mock interviews. Utilize college placement cell resources and alumni networks for guidance.
Tools & Resources
IndiaBix (Aptitude), GeeksforGeeks (Interview Prep), Mock interview platforms, College placement cell
Career Connection
Thorough preparation directly translates into higher success rates in campus placements and off-campus recruitment drives, securing desired job roles.
Advanced Project and Research Work- (Semester 6)
Undertake a significant final year project that showcases your integrated skills. Consider a research project under faculty mentorship, aiming for publications or presentations. This demonstrates advanced problem-solving capabilities and dedication to your field.
Tools & Resources
Research papers (arXiv, IEEE Xplore), Academic journals, Collaboration tools, Advanced statistical software
Career Connection
High-impact projects or research work enhance your profile for postgraduate studies, research positions, or specialized roles in R&D departments.
Career Planning and Professional Branding- (Semester 6)
Define clear career goals, whether it''''s higher studies, entrepreneurship, or corporate roles. Optimize your LinkedIn profile, create a professional resume, and network proactively. Attend career fairs and industry events to explore opportunities and build connections.
Tools & Resources
LinkedIn (Professional Networking), Resume builders (Canva, Zety), Career counselling services, College alumni network
Career Connection
Effective career planning and professional branding are critical for navigating the job market, attracting recruiters, and making informed decisions about your future career trajectory.
Program Structure and Curriculum
Eligibility:
- Pass in PUC / 10+2 / H.S.C / P.D.C in Science Stream with Mathematics as one of the subjects or equivalent examination recognised by Bangalore University.
Duration: 3 Years (6 Semesters) for Basic Degree, option for 4th Year (8 Semesters) for Honours Degree
Credits: Approximately 120-124 credits for 3-year Basic Degree Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-C1 | Fundamentals of Computers & Programming in C | Core | 4 | Introduction to Computers, C Language Fundamentals, Control Structures & Functions, Arrays and Pointers, Input/Output Operations |
| CS-CP1 | Computer Lab - I (Programming in C) | Practical | 2 | C Program Development, Debugging Techniques, Implementation of Control Structures, Function Calls, Array and Pointer Exercises |
| MA-MT-C1 | Differential Calculus and Integral Calculus | Core | 4 | Limits and Continuity, Differentiation Techniques, Applications of Derivatives, Methods of Integration, Definite Integrals and Applications |
| MA-MT-P1 | Mathematics Lab - I | Practical | 2 | Solving Problems using Software (e.g., Geogebra), Graphing Functions, Numerical Differentiation, Numerical Integration |
| ST-C1 | Descriptive Statistics | Core | 4 | Data Organization and Presentation, Measures of Central Tendency, Measures of Dispersion, Skewness and Kurtosis, Correlation and Regression Analysis |
| ST-P1 | Statistics Lab - I | Practical | 2 | Data Entry and Cleaning, Calculation of Descriptive Statistics, Graphical Representation of Data, Correlation and Regression Computation, Using Statistical Software (e.g., R, Excel) |
| AECC1.1 | Indian Constitution, Human Rights & Environmental Studies | Ability Enhancement Compulsory Course | 2 | Indian Constitution, Fundamental Rights and Duties, Environmental Pollution, Natural Resources, Sustainable Development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-C2 | Data Structures using C++ | Core | 4 | Introduction to Data Structures, Arrays, Stacks, Queues, Linked Lists, Trees and Binary Search Trees, Graphs and Graph Traversal Algorithms |
| CS-CP2 | Computer Lab - II (Data Structures) | Practical | 2 | Implementation of Stacks and Queues, Linked List Operations, Tree Traversal Algorithms, Graph Representation and Traversal, Sorting and Searching Algorithms |
| MA-MT-C2 | Differential Equations and Group Theory | Core | 4 | First Order Differential Equations, Second Order Linear Differential Equations, Partial Differential Equations, Groups and Subgroups, Permutation Groups and Cyclic Groups |
| MA-MT-P2 | Mathematics Lab - II | Practical | 2 | Solving ODEs and PDEs, Visualizing Group Structures, Applications of Differential Equations, Using Mathematical Software for Problems |
| ST-C2 | Probability and Probability Distributions | Core | 4 | Basic Probability Theory, Random Variables and Expectation, Discrete Probability Distributions (Binomial, Poisson), Continuous Probability Distributions (Normal, Exponential), Central Limit Theorem |
| ST-P2 | Statistics Lab - II | Practical | 2 | Calculating Probabilities, Fitting Probability Distributions to Data, Simulating Random Variables, Hypothesis Testing for Proportions, Using Statistical Software (e.g., R, Python for Stats) |
| AECC2.1 | English | Ability Enhancement Compulsory Course | 2 | Communication Skills, Grammar and Vocabulary, Reading Comprehension, Creative Writing, Soft Skills |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-C3 | Database Management Systems | Core | 4 | DBMS Architecture, Entity-Relationship (ER) Model, Relational Model and Algebra, Structured Query Language (SQL), Normalization and Transaction Management |
| CS-CP3 | Computer Lab - III (DBMS) | Practical | 2 | SQL Queries (DDL, DML), Database Design and ER Diagrams, Data Manipulation and Joins, Views and Stored Procedures, Relational Database Implementation |
| MA-MT-C3 | Real Analysis and Linear Algebra | Core | 4 | Real Number System, Sequences and Series, Continuity and Uniform Continuity, Vector Spaces and Subspaces, Linear Transformations and Matrices, Eigenvalues and Eigenvectors |
| MA-MT-P3 | Mathematics Lab - III | Practical | 2 | Numerical Methods for Linear Algebra, Matrix Operations, Solving Systems of Linear Equations, Computational Real Analysis Problems |
| ST-C3 | Statistical Inference | Core | 4 | Estimation Theory (Point and Interval), Properties of Estimators, Hypothesis Testing Principles, Parametric Tests (t-test, Chi-square, F-test), Non-parametric Tests |
| ST-P3 | Statistics Lab - III | Practical | 2 | Constructing Confidence Intervals, Performing Hypothesis Tests, Analyzing Data with ANOVA, Implementing Non-parametric Tests, Using Statistical Software for Inference |
| SEC3.1 | Python Programming (Example) | Skill Enhancement Course | 2 | Python Basics, Data Structures in Python, Functions and Modules, File Handling, Object-Oriented Programming with Python |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-C4 | Java Programming | Core | 4 | Object-Oriented Programming (OOP) Concepts, Classes, Objects and Methods, Inheritance, Polymorphism, Abstraction, Exception Handling and Multithreading, GUI Programming (AWT/Swing) |
| CS-CP4 | Computer Lab - IV (Java Programming) | Practical | 2 | Implementing OOP Principles, Developing Java Applications, Handling Exceptions, Creating Multithreaded Programs, Building GUI interfaces |
| MA-MT-C4 | Complex Analysis and Numerical Methods | Core | 4 | Complex Numbers and Functions, Analytic Functions and Cauchy-Riemann Equations, Complex Integration and Residue Theorem, Numerical Solution of Algebraic Equations, Interpolation and Curve Fitting, Numerical Differentiation and Integration |
| MA-MT-P4 | Mathematics Lab - IV | Practical | 2 | Solving Complex Equations, Implementing Numerical Methods, Approximation Techniques, Using Software for Complex Analysis |
| ST-C4 | Sampling Theory and Design of Experiments | Core | 4 | Sampling Techniques (SRS, Stratified, Systematic), Estimation of Population Parameters, Analysis of Variance (ANOVA), Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD) |
| ST-P4 | Statistics Lab - IV | Practical | 2 | Implementing Sampling Procedures, ANOVA Table Construction, Designing and Analyzing Experiments, Using Statistical Software for Experimental Data |
| SEC4.1 | Web Designing (Example) | Skill Enhancement Course | 2 | HTML Fundamentals, CSS Styling, JavaScript Basics, Responsive Design, Web Page Layout |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-C5.1 | Operating Systems | Core | 4 | Operating System Concepts, Process Management and Scheduling, Memory Management Techniques, Virtual Memory and Paging, File Systems and I/O Management |
| CS-C5.2 | Computer Networks | Core | 4 | Network Topologies and Devices, OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer (IP Addressing, Routing), Transport Layer (TCP, UDP), Application Layer Protocols |
| CS-CP5 | Computer Lab - V (OS & Networking) | Practical | 2 | Shell Scripting, Process and Thread Management, Socket Programming, Network Configuration Commands, Client-Server Applications |
| MA-MT-C5 | Partial Differential Equations and Applications | Core | 4 | Formation of PDEs, First Order Linear and Non-Linear PDEs, Second Order Linear PDEs, Method of Separation of Variables, Wave, Heat, and Laplace Equations |
| MA-MT-E1 | Elective: Operations Research (Example) | Elective | 3 | Linear Programming Problems, Simplex Method, Transportation Problem, Assignment Problem, Game Theory |
| MA-MT-P5 | Mathematics Lab - V | Practical | 2 | Solving PDEs Numerically, Applications of PDEs in Physics, Solving OR Problems using Software, Mathematical Modeling with PDEs |
| ST-C5 | Econometrics and Time Series Analysis | Core | 4 | Simple and Multiple Linear Regression, Assumptions of Classical Linear Regression, Autocorrelation and Heteroscedasticity, Components of Time Series, ARIMA Models and Forecasting |
| ST-E1 | Elective: Data Mining for Statistics (Example) | Elective | 3 | Introduction to Data Mining, Classification Techniques, Clustering Algorithms, Association Rule Mining, Decision Trees and Support Vector Machines |
| ST-P5 | Statistics Lab - V | Practical | 2 | Regression Analysis in R/Python, Time Series Plotting and Decomposition, Forecasting using ARIMA Models, Implementing Data Mining Algorithms, Econometric Model Estimation |
| OEC5.1 | Open Elective - I | Open Elective | 3 | Interdisciplinary subject chosen from various faculties offered by the college |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-C6.1 | Web Programming | Core | 4 | HTML5 and CSS3, JavaScript and DOM Manipulation, Server-side Scripting (PHP/Node.js), Database Connectivity for Web Applications, Web Frameworks (Basic concepts) |
| CS-C6.2 | Software Engineering | Core | 4 | Software Development Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing Strategies, Software Project Management Concepts |
| CS-CP6 | Computer Lab - VI & Project Work | Practical & Project | 6 | Developing Web Applications, Implementing Software Engineering Principles, System Design and Development, Testing and Deployment, Individual/Group Project on a relevant topic |
| MA-MT-C6 | Mathematical Modeling and Applications | Core | 4 | Introduction to Mathematical Modeling, Modeling with Ordinary Differential Equations, Modeling with Partial Differential Equations, Population Dynamics Models, Economic and Environmental Models |
| MA-MT-E2 | Elective: Graph Theory (Example) | Elective | 3 | Graphs and Graph Isomorphism, Paths, Cycles, and Trees, Planar Graphs, Graph Coloring, Network Flows and Connectivity |
| MA-MT-P6 | Mathematics Lab - VI | Practical | 2 | Developing Mathematical Models, Simulating Model Behaviors, Using Software for Graph Theory Problems, Analysis of Complex Systems |
| ST-C6 | Operations Research and Quality Control | Core | 4 | Linear Programming and Duality, Network Analysis (CPM, PERT), Inventory Control Models, Queuing Theory, Statistical Process Control (Control Charts), Acceptance Sampling |
| ST-E2 | Elective: Bio-Statistics (Example) | Elective | 3 | Measures of Health and Disease, Survival Analysis, Clinical Trials Design, Epidemiological Methods, Statistical Genetics |
| ST-P6 | Statistics Lab - VI & Project Work | Practical & Project | 6 | Solving OR Problems, Implementing Quality Control Charts, Data Analysis in Bio-Statistics, Statistical Report Writing, Independent Project on a Statistical Problem |
| OEC6.1 | Open Elective - II | Open Elective | 3 | Interdisciplinary subject chosen from various faculties offered by the college |
