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B-SC in Computer Science Mathematics Statistics Cms at ISBC College of Arts, Science and Commerce
Bengaluru, Karnataka
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About the Specialization
What is Computer Science, Mathematics, Statistics (CMS) at ISBC College of Arts, Science and Commerce Bengaluru?
This Computer Science, Mathematics, Statistics (CMS) program at ISBC College focuses on providing a robust foundation in three critical quantitative and analytical disciplines. It combines the logical rigor of Computer Science, the abstract problem-solving of Mathematics, and the data interpretation power of Statistics, preparing students for diverse roles in India''''s technology and data-driven industries. This interdisciplinary approach is highly relevant for emerging fields like Artificial Intelligence, Data Science, and Quantitative Finance.
Who Should Apply?
This program is ideal for fresh graduates from the 10+2 science stream with a strong aptitude for analytical thinking and problem-solving. It suits students aspiring to careers in IT, data analytics, research, or higher studies. Working professionals looking to acquire a foundational understanding of these interconnected fields for upskilling or career transitions into quantitative roles will also find this program beneficial. Prerequisites include a solid background in Mathematics at the pre-university level.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths as Data Analysts, Software Developers, Statisticians, Business Intelligence Analysts, or Junior Researchers. Entry-level salaries typically range from INR 3-6 lakhs per annum, with experienced professionals earning upwards of INR 10-15 lakhs. Growth trajectories are strong in Indian IT and analytics companies. The program also lays a strong groundwork for competitive exams and certifications in data science, actuarial science, and software development.
Student Success Practices
Foundation Stage
Master Core Programming & Math Fundamentals- (Semester 1-2)
Dedicate time daily to practice C programming and mathematical problem-solving. Utilize online platforms like HackerRank for coding challenges and engage in solving textbook problems for Calculus and Algebra to build a strong theoretical base. Consistent practice is key to logical thinking.
Tools & Resources
HackerRank, GeeksforGeeks, Khan Academy (for Math concepts), NPTEL introductory courses
Career Connection
A strong foundation in programming and mathematics is indispensable for advanced CS/Stats subjects and directly impacts performance in technical interviews for software development and analytical roles.
Engage in Peer Learning and Collaborative Study- (Semester 1-2)
Form study groups with classmates to discuss complex topics in Computer Science, Mathematics, and Statistics. Collaborative problem-solving clarifies doubts, provides different perspectives, and strengthens understanding of core concepts. Teach each other to solidify your own knowledge.
Tools & Resources
WhatsApp/Telegram groups, College library study spaces, Online collaboration tools
Career Connection
Develops teamwork and communication skills, which are highly valued in corporate environments for project work and cross-functional teams.
Build a Portfolio of Mini-Projects- (Semester 1-2)
Beyond lab exercises, undertake small personal projects using C/C++ or basic statistical tools. For instance, create a simple calculator, a basic data analysis script, or a small game. This practical application reinforces learning and sparks creativity.
Tools & Resources
CodeBlocks/VS Code (IDE), GitHub (for version control), Online C/C++ tutorials
Career Connection
Showcases practical skills and initiative to potential employers, even at an early stage, making resumes stand out during internships or entry-level job applications.
Intermediate Stage
Explore Interdisciplinary Applications & Electives- (Semester 3-5)
Actively research and choose open electives and skill enhancement courses that broaden your CMS knowledge, such as Python for data science, web development, or advanced statistical software. Look for ways to integrate CS, Math, and Stats in projects.
Tools & Resources
NPTEL courses on Data Science/ML, Coursera/edX for specialized topics, College''''s elective list
Career Connection
Develops a versatile skill set highly sought after in India''''s interdisciplinary job market, especially in fintech, analytics, and AI/ML domains.
Participate in Coding Competitions & Hackathons- (Semester 3-5)
Engage in intra-college or external coding competitions and hackathons. These events provide real-world problem-solving experience, improve algorithmic thinking, and expose you to working under pressure. Seek out competitions on platforms popular in India.
Tools & Resources
CodeChef, HackerEarth, College tech clubs
Career Connection
Enhances problem-solving abilities, builds a competitive portfolio, and provides networking opportunities with industry professionals and peers, which can lead to internship leads.
Seek Mentorship and Industry Exposure- (Semester 3-5)
Connect with faculty, seniors, and industry professionals. Attend workshops, seminars, and guest lectures to understand current industry trends in tech and analytics. Look for opportunities to shadow professionals or participate in short-term industry projects.
Tools & Resources
LinkedIn, Professional networking events (if available), Alumni network
Career Connection
Gain insights into career paths, learn about in-demand skills, and potentially secure recommendations or leads for internships and placements in leading Indian companies.
Advanced Stage
Undertake an Industry-Relevant Project/Internship- (Semester 6)
Secure an internship (minimum 2-3 months) or undertake a substantial project that applies your CMS knowledge to solve a real-world problem. Focus on areas like data analysis, machine learning model development, or software solution architecture. This is crucial for Indian placements.
Tools & Resources
LinkedIn Jobs, Internshala, College placement cell, Professors for research projects
Career Connection
Provides practical experience, builds a strong resume, and often converts into pre-placement offers (PPOs) or provides valuable industry references for future job applications.
Intensive Placement Preparation & Mock Interviews- (Semester 6)
Begin rigorous preparation for campus placements, focusing on aptitude tests, technical rounds (coding, DBMS, OS, Networks), and HR interviews. Participate in mock interviews conducted by the college or external agencies to refine your communication and problem-solving under pressure.
Tools & Resources
Company-specific interview guides, Placement training workshops, Online aptitude tests
Career Connection
Directly prepares you for the recruitment process of Indian companies, significantly increasing your chances of securing a desirable job offer upon graduation.
Specialize and Certify in Niche Areas- (Semester 6)
Identify a niche area within CMS (e.g., AI, Cloud Computing, Actuarial Science) and pursue advanced certifications. This demonstrates specialized expertise beyond the degree and makes you a more attractive candidate for specific roles in the Indian market.
Tools & Resources
AWS/Azure certifications, Google Data Analytics/ML certificates, Coursera/Udemy advanced courses
Career Connection
Differentiates you in a competitive job market, aligns your skills with high-demand industry requirements, and often leads to higher starting salaries and faster career growth.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 (PUC II) or equivalent with Science subjects (Physics, Chemistry, Mathematics, Biology / Computer Science / Statistics).
Duration: 3 years / 6 semesters
Credits: 120-132 Credits
Assessment: Internal: 40-50%, External: 50-60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC-CS-DSC1T | Fundamentals of Computers and C Programming (Theory) | Core (Computer Science) | 4 | Introduction to Computers, Operating System Concepts, C Programming Basics, Control Structures, Arrays and Functions, Pointers and Structures |
| BSC-CS-DSC1P | C Programming Lab | Core (Computer Science) - Practical | 2 | Basic C Programs, Control Statement Implementation, Array and String Operations, Functions and Pointers Practice, Structures and Unions |
| BSC-M-DSC1T | Differential Calculus and Integral Calculus (Theory) | Core (Mathematics) | 4 | Successive Differentiation, Partial Differentiation, Reduction Formulae, Multiple Integrals, Beta and Gamma Functions |
| BSC-M-DSC1P | Calculus Lab using Maxima/Python | Core (Mathematics) - Practical | 2 | Maxima/Python Basics, Plotting Functions, Differentiation & Integration, Solving Equations, Vector Operations |
| BSC-S-DSC1T | Descriptive Statistics and Probability (Theory) | Core (Statistics) | 4 | Measures of Central Tendency, Measures of Dispersion, Skewness and Kurtosis, Correlation and Regression, Basic Probability Concepts, Random Variables |
| BSC-S-DSC1P | Statistics Lab using R/Excel | Core (Statistics) - Practical | 2 | Data Entry and Cleaning, Calculating Descriptive Statistics, Correlation and Regression Analysis, Data Visualization, Basic Probability Simulations |
| AECC-1 | Environmental Studies | Ability Enhancement Compulsory Course | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Environmental Ethics, Sustainable Development |
| SEC-CS1 | Office Automation Tools | Skill Enhancement Course (CS option) | 2 | MS Word for Document Creation, MS Excel for Data Analysis, MS PowerPoint for Presentations, Internet and Email Basics |
| SEC-M1 | Mathematical Software | Skill Enhancement Course (Math option) | 2 | Introduction to LaTeX, Basics of Maxima/Python for Math, Symbolic Computations, Numerical Computations, Graphing Functions |
| SEC-S1 | Data Entry and SPSS | Skill Enhancement Course (Stats option) | 2 | Data Entry Principles, SPSS Interface and Data View, Variable Definition, Descriptive Statistics in SPSS, Basic Inferential Analysis |
| OE-1 | Open Elective - I | Open Elective | 3 | Varies based on chosen elective from other disciplines |
| LAN101 | Language - I (MIL) | Language | 3 | Selected Indian Language (Kannada/Hindi/Sanskrit etc.) Literature, Grammar, Prose and Poetry, Cultural Context |
| ENG101 | Language - II (English) | Language | 3 | Communication Skills, Grammar and Usage, Reading Comprehension, Writing Skills, Literary Appreciation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC-CS-DSC2T | Data Structures using C (Theory) | Core (Computer Science) | 4 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms |
| BSC-CS-DSC2P | Data Structures Lab using C | Core (Computer Science) - Practical | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Representations, Sorting and Searching Algorithms |
| BSC-M-DSC2T | Algebra I and Vector Calculus (Theory) | Core (Mathematics) | 4 | Group Theory Basics, Rings and Fields, Vector Differentiation, Vector Integration, Green''''s, Gauss''''s, Stokes''''s Theorems |
| BSC-M-DSC2P | Algebra and Vector Calculus Lab | Core (Mathematics) - Practical | 2 | Maxima/Python for Group Theory, Vector Algebra Operations, Gradient, Divergence, Curl Computations, Line and Surface Integrals |
| BSC-S-DSC2T | Probability Distributions and Statistical Inference (Theory) | Core (Statistics) | 4 | Discrete Probability Distributions, Continuous Probability Distributions, Central Limit Theorem, Point and Interval Estimation, Hypothesis Testing Basics, Chi-Square, t, F Distributions |
| BSC-S-DSC2P | Probability Distributions and Statistical Inference Lab | Core (Statistics) - Practical | 2 | Fitting Probability Distributions, Parameter Estimation, One Sample Hypothesis Tests, Two Sample Hypothesis Tests, Non-parametric Tests |
| AECC-2 | Indian Constitution | Ability Enhancement Compulsory Course | 2 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union and State Governments, Judiciary and Elections, Constitutional Amendments |
| SEC-CS2 | Web Designing | Skill Enhancement Course (CS option) | 2 | HTML Fundamentals, CSS for Styling, JavaScript Basics, Responsive Design Principles, Introduction to Web Hosting |
| SEC-M2 | Graph Theory with Geogebra | Skill Enhancement Course (Math option) | 2 | Basic Graph Concepts, Graph Traversals, Trees and Spanning Trees, Planar Graphs, Network Flows, Geogebra for Graph Visualization |
| SEC-S2 | Sampling Techniques and R | Skill Enhancement Course (Stats option) | 2 | Types of Sampling, Simple Random Sampling, Stratified and Systematic Sampling, Introduction to R Programming, Data Manipulation in R, Sampling in R |
| OE-2 | Open Elective - II | Open Elective | 3 | Varies based on chosen elective from other disciplines |
| LAN201 | Language - I (MIL) | Language | 3 | Selected Indian Language (Kannada/Hindi/Sanskrit etc.) Advanced Literature, Applied Grammar, Creative Writing, Critical Analysis |
| ENG201 | Language - II (English) | Language | 3 | Advanced Communication, Professional English, Report Writing, Presentation Skills, Literary Criticism |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC-CS-DSC3T | Object Oriented Programming with C++ (Theory) | Core (Computer Science) | 4 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Operator Overloading, Virtual Functions, File I/O and Exception Handling |
| BSC-CS-DSC3P | Object Oriented Programming Lab with C++ | Core (Computer Science) - Practical | 2 | Class and Object Implementation, Constructor and Destructor Practice, Inheritance Examples, Polymorphism and Virtual Functions, Template Programming |
| BSC-M-DSC3T | Real Analysis I and Complex Analysis I (Theory) | Core (Mathematics) | 4 | Real Number System, Sequences and Series, Continuity and Differentiability, Riemann Integration, Complex Numbers and Functions, Analytic Functions |
| BSC-M-DSC3P | Real and Complex Analysis Lab | Core (Mathematics) - Practical | 2 | Numerical Convergence of Sequences, Plotting Complex Functions, Roots of Complex Equations, Series Summation, Properties of Continuous Functions |
| BSC-S-DSC3T | Sampling Techniques and Design of Experiments (Theory) | Core (Statistics) | 4 | Sampling Methods, Estimation of Parameters, Analysis of Variance (ANOVA), Completely Randomized Design, Randomized Block Design, Factorial Experiments |
| BSC-S-DSC3P | Sampling Techniques and Design of Experiments Lab | Core (Statistics) - Practical | 2 | Implementing Sampling Schemes, ANOVA Table Calculation, Design of Experiments Analysis, SAS/R for Experimental Data, Comparison of Treatments |
| SEC-CS3 | Python Programming | Skill Enhancement Course (CS option) | 2 | Python Fundamentals, Data Types and Structures, Control Flow, Functions and Modules, File Handling, Object-Oriented Python |
| SEC-M3 | Statistical Software (R/SAS) | Skill Enhancement Course (Math option) | 2 | Introduction to R/SAS, Data Import and Export, Statistical Graphics, Descriptive Statistics, Hypothesis Testing |
| SEC-S3 | Statistical Quality Control and Reliability | Skill Enhancement Course (Stats option) | 2 | Control Charts for Variables, Control Charts for Attributes, Acceptance Sampling, Reliability Concepts, Life Testing |
| OE-3 | Open Elective - III | Open Elective | 3 | Varies based on chosen elective from other disciplines |
| LAN301 | Language - I (MIL) | Language | 3 | Selected Indian Language (Kannada/Hindi/Sanskrit etc.) Regional Literature, Cultural Studies, Translation Practice |
| ENG301 | Language - II (English) | Language | 3 | Academic Writing, Research Methodology, Critical Thinking, Public Speaking |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC-CS-DSC4T | Database Management Systems (Theory) | Core (Computer Science) | 4 | Database Concepts, ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| BSC-CS-DSC4P | DBMS Lab | Core (Computer Science) - Practical | 2 | SQL DDL and DML, Advanced SQL Queries, Stored Procedures and Triggers, Database Design, Report Generation |
| BSC-M-DSC4T | Numerical Analysis and Linear Algebra (Theory) | Core (Mathematics) | 4 | Numerical Solutions of Equations, Interpolation, Numerical Integration, Matrices and Determinants, Vector Spaces, Linear Transformations |
| BSC-M-DSC4P | Numerical Analysis and Linear Algebra Lab | Core (Mathematics) - Practical | 2 | Solving Equations Numerically (e.g., Bisection, Newton-Raphson), Numerical Integration Methods, Matrix Operations, Eigenvalues and Eigenvectors, Linear Regression |
| BSC-S-DSC4T | Time Series and Index Numbers (Theory) | Core (Statistics) | 4 | Components of Time Series, Measurement of Trend, Seasonal and Cyclical Variations, Forecasting Methods, Index Numbers Construction, Tests of Adequacy |
| BSC-S-DSC4P | Time Series and Index Numbers Lab | Core (Statistics) - Practical | 2 | Time Series Data Handling in R/Excel, Trend Fitting, Seasonal Adjustment, Index Number Calculation, Forecasting using Moving Averages |
| SEC-CS4 | Linux Shell Programming | Skill Enhancement Course (CS option) | 2 | Linux Commands, Shell Scripting Basics, Variables and Operators, Control Flow in Shell, File System Navigation, Process Management |
| SEC-M4 | Differential Equations with MATLAB | Skill Enhancement Course (Math option) | 2 | First Order Differential Equations, Second Order Differential Equations, Systems of ODEs, MATLAB for ODEs, Numerical Methods for ODEs |
| SEC-S4 | Official Statistics | Skill Enhancement Course (Stats option) | 2 | Indian Statistical System, National Sample Survey Office (NSSO), Census of India, Agriculture Statistics, Industrial Statistics, Price Statistics |
| OE-4 | Open Elective - IV | Open Elective | 3 | Varies based on chosen elective from other disciplines |
| LAN401 | Language - I (MIL) | Language | 3 | Selected Indian Language (Kannada/Hindi/Sanskrit etc.) Communication Practice, Report Writing, Debate and Discussion |
| ENG401 | Language - II (English) | Language | 3 | Advanced English for Professional Purposes, Documentation, Interview Skills, Group Discussion |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC-CS-DSC5T | Operating Systems (Theory) | Core (Computer Science) | 4 | OS Introduction, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| BSC-CS-DSC5P | Operating Systems Lab | Core (Computer Science) - Practical | 2 | Linux Commands for OS Management, Process Creation and Management, CPU Scheduling Algorithms Implementation, Memory Allocation Algorithms, File System Operations |
| BSC-CS-DSC6T | Computer Networks (Theory) | Core (Computer Science) | 4 | Network Topologies, OSI and TCP/IP Models, Network Devices, Data Link Layer, Network Layer (IP, Routing), Transport Layer (TCP, UDP) |
| BSC-CS-DSC6P | Computer Networks Lab | Core (Computer Science) - Practical | 2 | Network Configuration, Socket Programming Basics, Packet Tracing (Wireshark), Subnetting Practice, Network Protocol Analysis |
| BSC-M-DSC5T | Abstract Algebra and Linear Algebra II (Theory) | Core (Mathematics) | 4 | Groups, Subgroups, Normal Subgroups, Homomorphisms and Isomorphisms, Vector Spaces, Inner Product Spaces, Eigenvalues and Eigenvectors, Quadratic Forms |
| BSC-M-DSC5P | Abstract Algebra and Linear Algebra II Lab | Core (Mathematics) - Practical | 2 | Symmetry Groups, Field Extensions, Gram-Schmidt Process, Singular Value Decomposition, Solving Linear Systems |
| BSC-S-DSC5T | Econometrics and Demography (Theory) | Core (Statistics) | 4 | Classical Linear Regression Model, Assumptions of CLRM, Problem of Multicollinearity, Heteroscedasticity, Population Growth Models, Fertility and Mortality Measures |
| BSC-S-DSC5P | Econometrics and Demography Lab | Core (Statistics) - Practical | 2 | Regression Analysis using R/Excel, Testing for Assumptions, Population Projection, Life Table Construction, Fertility Rate Calculation |
| BSC-CS-DSE1T | Discipline Specific Elective - I (CS) | Elective (Computer Science) | 3 | Choice from options like Python Programming, Data Mining, Android Programming, etc. (Varies) |
| BSC-CS-DSE1P | Discipline Specific Elective - I (CS) Lab | Elective (Computer Science) - Practical | 1 | Practical implementation related to chosen DSE (CS) |
| BSC-M-DSE1T | Discipline Specific Elective - I (Mathematics) | Elective (Mathematics) | 3 | Choice from options like Discrete Mathematics, Graph Theory, etc. (Varies) |
| BSC-M-DSE1P | Discipline Specific Elective - I (Mathematics) Lab | Elective (Mathematics) - Practical | 1 | Practical implementation related to chosen DSE (Mathematics) |
| BSC-S-DSE1T | Discipline Specific Elective - I (Statistics) | Elective (Statistics) | 3 | Choice from options like Actuarial Statistics, Operations Research, etc. (Varies) |
| BSC-S-DSE1P | Discipline Specific Elective - I (Statistics) Lab | Elective (Statistics) - Practical | 1 | Practical implementation related to chosen DSE (Statistics) |
| SEC-5 | Skill Enhancement Course - 5 | Skill Enhancement Course | 2 | Varies based on choice (e.g., Cyber Security, LaTeX, Data Analytics with R) |
| OE-5 | Open Elective - V | Open Elective | 3 | Varies based on chosen elective from other disciplines |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC-CS-DSC7T | Web Programming (Theory) | Core (Computer Science) | 4 | HTML5 and CSS3, JavaScript and DOM, Server-side Scripting (PHP/Node.js), Web Frameworks (Basic), Database Connectivity (MySQL), Web Security Fundamentals |
| BSC-CS-DSC7P | Web Programming Lab | Core (Computer Science) - Practical | 2 | Static Web Page Design, Dynamic Content with JavaScript, Server-side Application Development, Database Integration, Full-stack Application Development |
| BSC-CS-DSC8T | Software Engineering (Theory) | Core (Computer Science) | 4 | Software Development Life Cycle, Requirements Engineering, Software Design, Software Testing, Project Management, Agile Methodologies |
| BSC-CS-DSC8P | Software Engineering Lab | Core (Computer Science) - Practical | 2 | UML Diagramming, Requirement Specification Document, Test Case Generation, Project Planning Tools, Version Control Systems (Git) |
| BSC-M-DSC6T | Topology and Differential Equations (Theory) | Core (Mathematics) | 4 | Topological Spaces, Continuous Functions, Connectedness and Compactness, First Order Differential Equations, Higher Order Linear ODEs, Partial Differential Equations |
| BSC-M-DSC6P | Topology and Differential Equations Lab | Core (Mathematics) - Practical | 2 | Visualizing Topological Concepts, Solving ODEs numerically, Phase Plane Analysis, Fourier Series Solutions for PDEs |
| BSC-S-DSC6T | Applied Statistics and Data Analytics (Theory) | Core (Statistics) | 4 | Non-parametric Tests, Categorical Data Analysis, Multivariate Analysis Basics, Introduction to Data Analytics, Machine Learning Concepts, Big Data Fundamentals |
| BSC-S-DSC6P | Applied Statistics and Data Analytics Lab | Core (Statistics) - Practical | 2 | Implementing Non-parametric Tests in R/Python, Logistic Regression, Principal Component Analysis, Clustering Algorithms, Introduction to Machine Learning Models |
| BSC-CS-DSE2T | Discipline Specific Elective - II (CS) | Elective (Computer Science) | 3 | Choice from options like Data Science, Machine Learning, Cloud Computing, IoT etc. (Varies) |
| BSC-CS-DSE2P | Discipline Specific Elective - II (CS) Lab | Elective (Computer Science) - Practical | 1 | Practical implementation related to chosen DSE (CS) |
| BSC-M-DSE2T | Discipline Specific Elective - II (Mathematics) | Elective (Mathematics) | 3 | Choice from options like Financial Mathematics, Cryptography, etc. (Varies) |
| BSC-M-DSE2P | Discipline Specific Elective - II (Mathematics) Lab | Elective (Mathematics) - Practical | 1 | Practical implementation related to chosen DSE (Mathematics) |
| BSC-S-DSE2T | Discipline Specific Elective - II (Statistics) | Elective (Statistics) | 3 | Choice from options like Biostatistics, Quality Management, Data Mining for Statistics etc. (Varies) |
| BSC-S-DSE2P | Discipline Specific Elective - II (Statistics) Lab | Elective (Statistics) - Practical | 1 | Practical implementation related to chosen DSE (Statistics) |
| SEC-6 | Skill Enhancement Course - 6 | Skill Enhancement Course | 2 | Varies based on choice (e.g., Ethical Hacking, Quantitative Finance, Survey Methodology) |
| OE-6 | Open Elective - VI | Open Elective | 3 | Varies based on chosen elective from other disciplines |
| PROJECT-6 | Project Work / Internship | Project | 6 | Problem Identification, Literature Review, Methodology Development, Implementation and Testing, Report Writing, Presentation and Viva-Voce |
