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BCA-HONOURS in Data Science at Centre for Computer Science and Information Technology, Mundur
Palakkad, Kerala
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About the Specialization
What is Data Science at Centre for Computer Science and Information Technology, Mundur Palakkad?
This Data Science program at Centre for Computer Science and Information Technology, Palakkad, focuses on equipping students with expertise in data analytics, machine learning, and big data technologies. In the burgeoning Indian market, characterized by massive data generation, this program cultivates skilled professionals capable of extracting actionable insights. Its curriculum is designed to bridge theoretical knowledge with practical applications, meeting the high industry demand for data-savvy talent.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics and analytical thinking, seeking entry into the booming data analytics and AI sectors. It also caters to aspiring data professionals looking to build a robust foundational and advanced skill set. Individuals passionate about solving complex problems using data, and those aiming for research-oriented roles or advanced studies in data science, will find this specialization particularly rewarding.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Analyst, Machine Learning Engineer, Business Intelligence Developer, and Data Scientist in both startups and large corporations. Entry-level salaries typically range from INR 4-7 lakhs per annum, with experienced professionals earning significantly more. The program aligns with certifications like Microsoft Certified: Azure Data Scientist Associate or Google''''s Professional Data Engineer, fostering strong growth trajectories in leading Indian companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (Python & Java)- (Semester 1-2)
Dedicate consistent time to practice coding problems in Python and Java. Focus on understanding data structures and algorithms deeply, as these are foundational for all advanced topics. Actively participate in lab sessions and seek additional online practice.
Tools & Resources
HackerRank, GeeksforGeeks, CodeChef, LeetCode (easy problems)
Career Connection
Strong programming skills are non-negotiable for any tech role, especially in data science. Proficiency here directly impacts interview performance and opens doors to various technical roles.
Build a Strong Mathematical & Statistical Base- (Semester 1-2)
Beyond classroom learning, invest in understanding the ''''why'''' behind mathematical and statistical concepts crucial for data science. This includes linear algebra, calculus, and probability. Use online courses and textbooks for deeper dives.
Tools & Resources
Khan Academy, NPTEL courses on Mathematics for Data Science, NCERT Mathematics (Class 11-12)
Career Connection
A solid quantitative foundation is essential for understanding machine learning algorithms, model building, and interpreting results, crucial for a data scientist''''s analytical prowess.
Engage in Peer Learning & Collaborative Projects- (Semester 1-2)
Form study groups to discuss complex topics, solve problems together, and collaborate on small projects. Teaching peers reinforces your understanding and develops teamwork skills, vital in industry.
Tools & Resources
WhatsApp/Telegram groups, GitHub for version control on shared projects, Google Docs for collaborative notes
Career Connection
Collaboration and communication skills are highly valued by employers. Group projects simulate real-world team environments, enhancing your readiness for industry roles.
Intermediate Stage
Undertake Data Analysis Mini-Projects- (Semester 3-5)
Apply concepts learned in Data Analytics with R and Introduction to Machine Learning by working on small, real-world datasets. Focus on data cleaning, exploratory data analysis, and basic model building.
Tools & Resources
Kaggle (datasets and tutorials), Google Colab, Jupyter Notebook, RStudio
Career Connection
Practical project experience is critical for showcasing your skills. These projects form the core of your portfolio, demonstrating your ability to apply theoretical knowledge to solve problems, directly aiding placements.
Develop a Personal Technical Blog/Portfolio- (Semester 3-5)
Start documenting your learning journey, project work, and insights gained. A blog helps solidify your understanding and provides a public portfolio to showcase your capabilities to potential employers. Write clear explanations and code snippets.
Tools & Resources
GitHub Pages, Medium, LinkedIn Articles, Notion for organizing thoughts
Career Connection
A well-maintained technical blog or portfolio acts as a digital resume, distinguishing you in the job market and demonstrating initiative, communication skills, and continuous learning.
Participate in Coding Challenges & Hackathons- (Semester 3-5)
Regularly engage in coding challenges on platforms like HackerRank or LeetCode to sharpen problem-solving skills. Participate in college-level or regional hackathons to build innovative solutions and network with peers and industry experts.
Tools & Resources
HackerRank, LeetCode, Major League Hacking (MLH) events, College tech clubs
Career Connection
Success in these competitions boosts your resume, hones your ability to work under pressure, and can lead to networking opportunities or even direct recruitment by companies looking for talent.
Advanced Stage
Pursue Specialised Internships & Live Projects- (Semester 6-8)
Seek internships that align with your Data Science specialization, focusing on machine learning, deep learning, or big data. Actively contribute to live projects, understanding the full development lifecycle and business impact. Leverage your college''''s placement cell.
Tools & Resources
LinkedIn Jobs, Internshala, College placement cell, Direct company applications
Career Connection
Internships are often a direct pipeline to full-time employment. Real-world experience is invaluable, giving you an edge in the competitive Indian job market and providing practical skills that employers seek.
Focus on Domain-Specific Data Science Applications- (Semester 6-8)
As you delve into advanced topics like NLP, Data Warehousing, and Reinforcement Learning, consider applying these to a specific industry domain (e.g., Healthcare, Finance, E-commerce). This makes your profile more specialized and attractive to specific companies.
Tools & Resources
Industry reports, Domain-specific datasets (e.g., from WHO, RBI), IEEE/ACM journals for research papers
Career Connection
Demonstrating expertise in a specific domain through projects or research differentiates you from generalists, making you a more desirable candidate for roles within those industries, leading to better placements.
Prepare for Data Science Specific Interview Rounds- (Semester 6-8)
Beyond technical skills, prepare for case studies, behavioral questions, and explain core data science concepts clearly. Practice mock interviews with peers or mentors. Understand common data science tools and their applications.
Tools & Resources
Glassdoor (interview questions), Interviews.ai, Data Science Central, Mentors/Alumni Network
Career Connection
Targeted interview preparation significantly increases your chances of securing placements. It ensures you can articulate your skills and experience effectively, impressing hiring managers.
Program Structure and Curriculum
Eligibility:
- Pass in Higher Secondary Examination (10+2) or equivalent with Mathematics/Computer Science/Informatics Practices/Statistics as one of the subjects, as per University of Calicut norms.
Duration: 4 years / 8 semesters
Credits: Minimum 160 credits (up to 190 for Honours with Research) Credits
Assessment: Internal: 20%, External: 80%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AEC01 | Academic Writing and Presentation Skills | Ability Enhancement Course | 2 | Academic Writing Essentials, Presentation Techniques, Report Writing, Research Skills, Effective Communication |
| VEC01 | Digital Marketing | Vocational Skill Enhancement Course | 2 | Digital Marketing Fundamentals, SEO and SEM, Social Media Marketing, Content Marketing, Email Marketing |
| DSCC01 | Digital Logic | Core | 4 | Number Systems and Codes, Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Registers and Counters |
| DSCC02 | Introduction to Programming (using Python) | Core | 4 | Python Basics, Data Types and Operators, Control Flow, Functions and Modules, Object-Oriented Programming Concepts, File Handling |
| DSCC03 | Computational Thinking and Problem Solving | Core | 4 | Problem Solving Strategies, Algorithmic Thinking, Decomposition and Abstraction, Pattern Recognition, Flowcharts and Pseudocode |
| DSCC04 | Lab I - Python Programming Lab | Lab | 2 | Python Program Development, Conditional Statements, Loops and Functions, Data Structures in Python, File Operations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AEC02 | Professional Communication Skills | Ability Enhancement Course | 2 | Verbal Communication, Non-Verbal Communication, Listening Skills, Group Discussions, Interviews |
| VEC02 | Web Designing using HTML and CSS | Vocational Skill Enhancement Course | 2 | HTML Structure, CSS Styling, Layouts and Responsiveness, Forms and Media, Web Standards |
| DSCC05 | Data Communication and Networking | Core | 4 | Network Topologies, OSI Model, TCP/IP Protocol Suite, Network Devices, Data Transmission Media |
| DSCC06 | Object Oriented Programming (using Java) | Core | 4 | Java Fundamentals, Classes and Objects, Inheritance and Polymorphism, Exception Handling, Multithreading |
| DSCC07 | Database Management System | Core | 4 | Database Concepts, Relational Model, SQL Queries, Normalization, Transaction Management |
| DSCC08 | Lab II - Java Programming Lab | Lab | 2 | Java Program Implementation, OOPs Concepts in Java, Applet Programming, GUI Development, File I/O |
| DSCC09 | Lab III - DBMS Lab | Lab | 2 | SQL Commands (DDL, DML, DCL), Database Creation, Querying Data, Stored Procedures, Triggers |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AEC03 | Introduction to Cyber Security | Ability Enhancement Course | 2 | Cybersecurity Basics, Threats and Vulnerabilities, Network Security, Data Security, Cyber Laws |
| VEC03 | Mobile Application Development (Android) | Vocational Skill Enhancement Course | 2 | Android Studio Basics, UI Design (XML), Activity Lifecycle, Data Storage, App Deployment |
| DSCC10 | Data Structures | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Hashing Techniques, Sorting and Searching Algorithms |
| DSCC11 | Operating Systems | Core | 4 | OS Introduction, Process Management, Memory Management, File Systems, I/O Management, Deadlocks |
| DSCC12 | Computer Organization and Architecture | Core | 4 | Basic Computer Structure, CPU Organization, Memory Hierarchy, Input-Output Organization, Instruction Set Architecture |
| DSCC13 | Lab IV - Data Structures Lab | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversals, Graph Algorithms, Sorting and Searching Practical |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AEC04 | Open Source Software | Ability Enhancement Course | 2 | Open Source Principles, Linux Operating System, Open Source Tools, Version Control (Git), Community Contributions |
| VEC04 | UI/UX Designing | Vocational Skill Enhancement Course | 2 | UI/UX Fundamentals, User Research, Wireframing and Prototyping, Usability Testing, Design Tools (Figma/Adobe XD) |
| DSCC14 | Algorithm Analysis and Design | Core | 4 | Algorithm Complexity, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Backtracking, Graph Algorithms |
| DSCC15 | Web Programming using PHP | Core | 4 | PHP Fundamentals, Form Handling, Database Connectivity (MySQL), Session Management, Server-side Scripting |
| DSCC16 | Software Engineering | Core | 4 | Software Development Life Cycle, Requirements Engineering, Software Design, Testing and Maintenance, Agile Methodologies |
| DSCC17 | Lab V - Web Programming Lab | Lab | 2 | PHP Scripting, Database Integration with PHP, Client-side Scripting (JavaScript), Full-stack Web Application, Deployment Basics |
| DSCC18 | Project I | Project | 4 | Project Planning, System Design, Implementation Phase, Testing and Debugging, Report Writing and Presentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCC19 | Artificial Intelligence | Core | 4 | AI Introduction, Problem Solving Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics |
| DSCC20 | Computer Graphics | Core | 4 | Graphics Primitives, 2D and 3D Transformations, Clipping and Viewing, Color Models, Animation Techniques |
| DSE01 | Data Analytics with R | Elective (Data Science Specialization) | 4 | R Programming Fundamentals, Data Import and Export, Data Manipulation with R, Statistical Graphics, Regression Analysis, Supervised Learning |
| DSE02 | Introduction to Machine Learning | Elective (Data Science Specialization) | 4 | ML Basics, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation, Feature Engineering |
| OE01 | E-Commerce | Open Elective | 3 | E-Commerce Models, Online Payment Systems, Digital Marketing in E-Commerce, Security Issues, M-Commerce |
| DSCC21 | Lab VI - AI and Computer Graphics Lab | Lab | 2 | AI Problem Implementation, Search Algorithms, 2D/3D Graphics Programming, Geometric Transformations, Animation Basics |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCC22 | Introduction to Cloud Computing | Core | 4 | Cloud Computing Concepts, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security |
| DSE03 | Big Data Technologies | Elective (Data Science Specialization) | 4 | Big Data Concepts, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases, Data Ingestion |
| DSE04 | Deep Learning | Elective (Data Science Specialization) | 4 | Neural Network Fundamentals, Feedforward Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transfer Learning, Deep Learning Frameworks |
| OE02 | Ethical Hacking | Open Elective | 3 | Introduction to Ethical Hacking, Footprinting and Reconnaissance, Scanning Networks, System Hacking, Web Application Hacking |
| DSCC23 | Lab VII - Big Data and Machine Learning Lab | Lab | 2 | Hadoop Cluster Setup, MapReduce Programming, Spark Data Processing, Machine Learning Model Implementation, Deep Learning Frameworks (TensorFlow/PyTorch) |
| DSCC24 | Project II | Project | 4 | Advanced Project Planning, Module Integration, System Testing, Deployment Strategies, Documentation and Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCC25 | Advanced Database Management Systems | Core (Honours) | 5 | Distributed Databases, Object-Oriented Databases, NoSQL Concepts, Database Security, Query Optimization |
| DSCC26 | Natural Language Processing | Core (Honours) | 5 | NLP Fundamentals, Text Preprocessing, Syntactic and Semantic Analysis, Language Models, Machine Translation, Sentiment Analysis |
| DSE05 | Data Warehousing and Mining | Elective (Honours Data Science Specialization) | 5 | Data Warehouse Architecture, OLAP Operations, Data Mining Concepts, Association Rule Mining, Classification and Prediction, Clustering Techniques |
| DSE06 | Time Series Analysis | Elective (Honours Data Science Specialization) | 5 | Time Series Components, ARIMA Models, Forecasting Techniques, Spectral Analysis, Stationarity and Seasonality |
| DSCC27 | Lab VIII - Advanced DBMS & NLP Lab | Lab (Honours) | 3 | NoSQL Database Implementation, Text Processing with NLTK, NLP Model Training, Information Extraction, Semantic Analysis Tools |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCC28 | Data Visualization | Core (Honours) | 5 | Visualization Principles, Data Storytelling, Interactive Visualizations, Visualization Tools (Tableau, PowerBI), Dashboard Design |
| DSCC29 | Ethics in Data Science | Core (Honours) | 5 | Ethical Principles in AI/ML, Data Privacy and Security, Bias and Fairness, Responsible AI Development, Legal and Regulatory Frameworks |
| DSE07 | Reinforcement Learning | Elective (Honours Data Science Specialization) | 5 | RL Fundamentals, Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Q-Learning, Deep Reinforcement Learning |
| DSCC30 | Major Project / Dissertation | Project (Honours) | 6 | Research Problem Identification, Literature Review, System Design and Development, Experimentation and Evaluation, Dissertation Writing and Defense |
| DSCC31 | Internship | Internship (Honours) | 3 | Industry Exposure, Real-world Project Experience, Professional Skill Development, Report Submission, Presentation of Work |
