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B-SC in Computer Science Mathematics Statistics at Devanga Sangha First Grade College
Bengaluru, Karnataka
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About the Specialization
What is Computer Science, Mathematics, Statistics at Devanga Sangha First Grade College Bengaluru?
This Computer Science, Mathematics, Statistics (CMS) program at Devanga Sangha First Grade College focuses on building a robust foundation in logical reasoning, computational thinking, and data analysis. It integrates core principles from three vital disciplines, preparing students for an increasingly data-driven world. The program is designed to meet the growing demand for professionals skilled in software development, quantitative analysis, and statistical modeling across various Indian industries.
Who Should Apply?
This program is ideal for fresh 10+2 science graduates with a keen interest in problem-solving, analytical thinking, and a desire to understand both the theoretical underpinnings and practical applications of computing and data. It caters to students aspiring for careers in data science, software development, actuarial science, or research, providing a versatile skill set for diverse roles in the Indian job market.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths as Data Analysts, Software Developers, Statisticians, Actuarial Trainees, or Research Assistants. Entry-level salaries typically range from INR 3-6 lakhs per annum, with experienced professionals earning significantly more. The strong quantitative and computational background offers excellent growth trajectories in IT, finance, research, and analytics sectors within major Indian cities like Bengaluru, Mumbai, and Hyderabad.
Student Success Practices
Foundation Stage
Master Core Programming and Math Fundamentals- (Semester 1-2)
Dedicate time to thoroughly understand C/C++ programming and foundational mathematical concepts like calculus and algebra. Regularly solve problems from textbooks and online platforms to solidify understanding. Form study groups to discuss complex topics and practice coding together.
Tools & Resources
GeeksforGeeks, HackerRank, Khan Academy (for Math), NPTEL videos for C/C++
Career Connection
A strong grasp of fundamentals is crucial for passing initial screening tests for IT companies and for building robust problem-solving skills, essential for any technical role.
Build Basic Data Analysis Skills- (Semester 1-2)
While learning descriptive statistics, practice data collection, organization, and basic visualization using spreadsheets. Understand how to interpret statistical measures and present data clearly. Start exploring simple datasets available online to gain practical experience.
Tools & Resources
Microsoft Excel, Google Sheets, Kaggle (for basic datasets), YouTube tutorials for data visualization
Career Connection
Early exposure to data handling makes you more competitive for internships in analytics and helps build a portfolio for future data-centric roles.
Engage in Academic and Departmental Activities- (Semester 1-2)
Actively participate in college-level programming competitions, math quizzes, and statistics workshops organized by the respective departments. This helps in networking with peers and faculty, and in developing confidence and presentation skills beyond the classroom.
Tools & Resources
College clubs and societies, Departmental events
Career Connection
Participation showcases initiative and teamwork, qualities highly valued by employers, and can lead to mentorship opportunities.
Intermediate Stage
Develop Practical Programming and Database Skills- (Semester 3-4)
Focus on implementing real-world scenarios using Java and SQL. Work on small projects that combine object-oriented programming with database management. Seek opportunities to build mini-applications, like a student management system or inventory tracker.
Tools & Resources
IntelliJ IDEA/Eclipse (for Java), MySQL/PostgreSQL, LeetCode (for advanced problem-solving)
Career Connection
Hands-on experience with Java and databases is a fundamental requirement for most software development and data-related positions in India.
Explore Statistical Software and Analytical Tools- (Semester 3-4)
Beyond theoretical statistics, learn practical application using software like R or Python (with libraries like Pandas, NumPy, Matplotlib). Work on case studies involving sampling, experimental design, and time series analysis to see data in action.
Tools & Resources
RStudio, Anaconda (for Python), Coursera/edX courses on R/Python for Data Science
Career Connection
Proficiency in statistical software is indispensable for roles in data analytics, market research, and quantitative finance, offering a significant edge in placements.
Seek Early Internship and Project Opportunities- (Semester 3-4)
Start looking for summer internships or part-time projects in local startups, NGOs, or even within the college departments. Focus on roles that allow application of CS, Math, or Stats skills. Collaborate with faculty on minor research projects.
Tools & Resources
Internshala, LinkedIn, College Placement Cell
Career Connection
Early internships provide invaluable industry exposure, help build a professional network, and often lead to pre-placement offers or strong referrals.
Advanced Stage
Specialize and Build a Strong Portfolio- (Semester 5-6)
Choose electives strategically (e.g., Machine Learning, Cloud Computing) and delve deep into those areas. Develop a substantial final year project that showcases your specialized skills, ideally solving a real-world problem or contributing to open source. Document all projects on GitHub.
Tools & Resources
GitHub, Kaggle Competitions, Advanced courses on platforms like Udacity/Pluralsight
Career Connection
A well-curated project portfolio is critical for demonstrating practical expertise and securing specialized roles in competitive fields like AI/ML or cloud engineering.
Prepare for Placements and Higher Studies- (Semester 5-6)
Engage in rigorous aptitude test preparation, mock interviews, and group discussions. Polish your resume and LinkedIn profile. If aspiring for higher education (M.Sc./MBA), start preparing for entrance exams like GATE, CAT, or GRE/GMAT and identify suitable universities.
Tools & Resources
Online aptitude test platforms, InterviewBit, Glassdoor (for company-specific interview prep)
Career Connection
Thorough preparation for recruitment processes significantly increases chances of securing desirable job offers or admission to prestigious postgraduate programs.
Network and Attend Industry Events- (Semester 5-6)
Attend industry conferences, seminars, and workshops in Bengaluru to stay updated on emerging technologies and trends. Network with professionals, alumni, and potential employers. Join professional bodies related to Computer Science, Mathematics, or Statistics.
Tools & Resources
Meetup groups, Industry events (e.g., Data Science Congress, Tech Summits), LinkedIn professional groups
Career Connection
Networking opens doors to hidden job opportunities, mentorship, and insights into industry demands, accelerating career growth post-graduation.
Program Structure and Curriculum
Eligibility:
- Pass in PUC (10+2) or equivalent examination with Science subjects from a recognized Board/Council
Duration: 3 Years (6 Semesters) for Bachelor''''s Degree
Credits: 136 Credits
Assessment: Internal: 40% (for Theory), 50% (for Practicals), External: 60% (for Theory), 50% (for Practicals)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCU CS DSC 1 | Fundamentals of Digital Computers and C Programming | Discipline Specific Core (DSC) | 4 | Computer Fundamentals, Number Systems, C Programming Basics, Control Structures, Functions and Arrays |
| BCU CS DSC 1P | C Programming Lab | Discipline Specific Core (Practical) | 2 | C Program Implementation, Conditional Statements, Looping Constructs, Functions and Pointers, Array Operations |
| BCU MT DSC 1 | Algebra-I and Calculus-I | Discipline Specific Core (DSC) | 4 | Matrices and Determinants, Group Theory Basics, Differential Calculus, Successive Differentiation, Partial Differentiation |
| BCU MT DSC 1P | Mathematics Practical-I | Discipline Specific Core (Practical) | 2 | Matrix Operations, Limits and Continuity, Applications of Derivatives, Numerical Methods basics, Solving Equations |
| BCU ST DSC 1 | Descriptive Statistics and Probability Theory | Discipline Specific Core (DSC) | 4 | Measures of Central Tendency, Measures of Dispersion, Correlation and Regression, Probability Concepts, Random Variables |
| BCU ST DSC 1P | Statistics Practical-I | Discipline Specific Core (Practical) | 2 | Data Tabulation, Graphical Representation, Calculation of Measures, Correlation Coefficient, Regression Line Fitting |
| BCU AECC 1 | Indian Language (Kannada/Sanskrit/Hindi) | Ability Enhancement Compulsory Course (AECC) | 2 | Basic Grammar, Reading Comprehension, Writing Skills, Cultural Aspects, Communication |
| BCU AECC 2 | Environmental Studies | Ability Enhancement Compulsory Course (AECC) | 2 | Ecosystems, Biodiversity, Environmental Pollution, Natural Resources, Sustainable Development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCU CS DSC 2 | Data Structures and C++ | Discipline Specific Core (DSC) | 4 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Object-Oriented Programming with C++ |
| BCU CS DSC 2P | Data Structures and C++ Lab | Discipline Specific Core (Practical) | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Representation, C++ Object-Oriented Features |
| BCU MT DSC 2 | Algebra-II and Calculus-II | Discipline Specific Core (DSC) | 4 | Vector Spaces, Linear Transformations, Integral Calculus, Multiple Integrals, Vector Calculus |
| BCU MT DSC 2P | Mathematics Practical-II | Discipline Specific Core (Practical) | 2 | Vector Space Problems, Linear Transformation Problems, Definite Integrals, Double and Triple Integrals, Green''''s and Stokes'''' Theorem |
| BCU ST DSC 2 | Probability Distributions and Statistical Inference-I | Discipline Specific Core (DSC) | 4 | Discrete Probability Distributions, Continuous Probability Distributions, Sampling Distributions, Estimation Theory, Hypothesis Testing Basics |
| BCU ST DSC 2P | Statistics Practical-II | Discipline Specific Core (Practical) | 2 | Fitting Probability Distributions, Confidence Intervals, Parametric Tests, Non-parametric Tests, ANOVA tables |
| BCU AECC 3 | English Language | Ability Enhancement Compulsory Course (AECC) | 2 | Grammar and Composition, Reading Skills, Effective Communication, Soft Skills, Public Speaking |
| BCU AECC 4 | Indian Constitution and Human Rights | Ability Enhancement Compulsory Course (AECC) | 2 | Preamble and Basic Structure, Fundamental Rights, Directive Principles, Federalism, Human Rights Conventions |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCU CS DSC 3 | Object Oriented Programming with Java and DBMS | Discipline Specific Core (DSC) | 4 | Java Programming Fundamentals, Classes and Objects, Inheritance and Polymorphism, Exception Handling, Database Concepts and SQL |
| BCU CS DSC 3P | Java and DBMS Lab | Discipline Specific Core (Practical) | 2 | Java Program Implementation, SQL Queries, Database Design, JDBC Connectivity, GUI Applications with Java |
| BCU MT DSC 3 | Real Analysis-I and Differential Equations-I | Discipline Specific Core (DSC) | 4 | Sequences and Series, Continuity and Differentiability, Riemann Integration, Ordinary Differential Equations, Laplace Transforms |
| BCU MT DSC 3P | Mathematics Practical-III | Discipline Specific Core (Practical) | 2 | Convergence of Sequences, Properties of Continuous Functions, Solving ODEs, Applications of Differential Equations, Inverse Laplace Transforms |
| BCU ST DSC 3 | Sampling Techniques and Design of Experiments | Discipline Specific Core (DSC) | 4 | Sampling Methods, Sample Size Determination, Basic Designs of Experiments, ANOVA for CRD and RBD, Factorial Experiments |
| BCU ST DSC 3P | Statistics Practical-III | Discipline Specific Core (Practical) | 2 | Stratified Sampling, Systematic Sampling, ANOVA Calculations, Design of experiments applications, Response Surface Methodology |
| BCU SEC 1 | Python Programming | Skill Enhancement Course (SEC) | 2 | Python Basics, Data Structures in Python, File Handling, Libraries for Data Analysis, Object-Oriented Python |
| BCU OE 1 | Open Elective - I | Open Elective (OE) | 3 | Interdisciplinary subject chosen by student, Basic concepts of chosen field, Practical applications, Societal relevance, Current trends |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCU CS DSC 4 | Operating System and Computer Networks | Discipline Specific Core (DSC) | 4 | Operating System Concepts, Process Management, Memory Management, Networking Models (OSI/TCP-IP), Network Devices and Protocols |
| BCU CS DSC 4P | Operating System and Computer Networks Lab | Discipline Specific Core (Practical) | 2 | Linux Commands, Shell Scripting, Socket Programming, Network Configuration, Process Synchronization |
| BCU MT DSC 4 | Real Analysis-II and Differential Equations-II | Discipline Specific Core (DSC) | 4 | Metric Spaces, Topology Basics, Partial Differential Equations, Fourier Series, Integral Transforms |
| BCU MT DSC 4P | Mathematics Practical-IV | Discipline Specific Core (Practical) | 2 | Solving PDEs, Fourier Series Expansion, Applications of Fourier Transforms, Numerical methods for PDEs, Wave and Heat Equations |
| BCU ST DSC 4 | Applied Statistics and Econometrics | Discipline Specific Core (DSC) | 4 | Time Series Analysis, Index Numbers, Statistical Quality Control, Regression Models, Forecasting Techniques |
| BCU ST DSC 4P | Statistics Practical-IV | Discipline Specific Core (Practical) | 2 | Time Series Decomposition, Control Charts, Multiple Regression Analysis, Forecasting models implementation, Survey Data Analysis |
| BCU SEC 2 | Web Designing Basics | Skill Enhancement Course (SEC) | 2 | HTML Fundamentals, CSS Styling, JavaScript Basics, Responsive Design, Web Development Tools |
| BCU OE 2 | Open Elective - II | Open Elective (OE) | 3 | Interdisciplinary subject chosen by student, Fundamental concepts, Societal impact, Ethical considerations, Emerging trends |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCU CS DSE 1A | Computer Graphics | Discipline Specific Elective (DSE) | 4 | Graphics Primitives, 2D/3D Transformations, Viewing and Clipping, Color Models, Projections |
| BCU CS DSE 1AP | Computer Graphics Lab | Discipline Specific Elective (Practical) | 2 | Line Drawing Algorithms, Circle Drawing Algorithms, Polygon Filling, 2D Transformation Implementation, Simple Animation |
| BCU CS DSE 2A | Data Analytics | Discipline Specific Elective (DSE) | 4 | Introduction to Data Analytics, Data Cleaning and Preprocessing, Exploratory Data Analysis, Statistical Methods for Data Analysis, Data Visualization |
| BCU CS DSE 2AP | Data Analytics Lab | Discipline Specific Elective (Practical) | 2 | R/Python for Data Analysis, Data Import and Export, Data Manipulation, Generating Visualizations, Basic Predictive Modeling |
| BCU MT DSE 1A | Complex Analysis | Discipline Specific Elective (DSE) | 4 | Complex Numbers, Analytic Functions, Complex Integration, Series Expansions, Residue Theory |
| BCU MT DSE 2A | Numerical Methods | Discipline Specific Elective (DSE) | 4 | Solution of Algebraic Equations, Interpolation, Numerical Differentiation, Numerical Integration, Numerical Solution of ODEs |
| BCU ST DSE 1A | Actuarial Statistics | Discipline Specific Elective (DSE) | 4 | Risk Theory, Life Contingencies, Life Annuities, Premiums and Reserves, Survival Models |
| BCU ST DSE 2A | Demography | Discipline Specific Elective (DSE) | 4 | Sources of Demographic Data, Measures of Fertility, Measures of Mortality, Life Tables, Population Projection |
| BCU PROJ RM | Research Methodology and Project Preparation | Project Related | 4 | Research Design, Data Collection Methods, Statistical Analysis for Research, Report Writing, Ethical Considerations |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCU CS DSE 3A | Cloud Computing | Discipline Specific Elective (DSE) | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security, Cloud Platforms |
| BCU CS DSE 3AP | Cloud Computing Lab | Discipline Specific Elective (Practical) | 2 | Working with AWS/Azure/GCP, Virtual Machine Deployment, Storage Services, Containerization (Docker), Cloud-based Application Deployment |
| BCU CS DSE 4A | Machine Learning | Discipline Specific Elective (DSE) | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation |
| BCU CS DSE 4AP | Machine Learning Lab | Discipline Specific Elective (Practical) | 2 | Implementing ML Algorithms in Python, Data Preprocessing for ML, Model Training and Testing, Cross-Validation Techniques, Using Scikit-learn |
| BCU MT DSE 3A | Abstract Algebra | Discipline Specific Elective (DSE) | 4 | Groups and Subgroups, Rings and Fields, Homomorphisms, Isomorphisms, Polynomial Rings |
| BCU MT DSE 4A | Operations Research | Discipline Specific Elective (DSE) | 4 | Linear Programming, Simplex Method, Transportation Problem, Assignment Problem, Network Analysis |
| BCU ST DSE 3A | R Programming for Statistics | Discipline Specific Elective (DSE) | 4 | R Basics and Data Types, Data Structures in R, Statistical Graphics, Importing/Exporting Data, Statistical Modeling with R |
| BCU ST DSE 4A | Reliability Theory | Discipline Specific Elective (DSE) | 4 | Reliability Concepts, Failure Rate, Lifetime Distributions, System Reliability, Maintenance Modeling |
| BCU PROJ FIN | Major Project Work / Dissertation | Project Work | 6 | Problem Identification, Literature Survey, Methodology Development, Implementation and Analysis, Project Report and Presentation |
