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BCA in Data Science at SRM Institute of Science and Technology (Deemed to be University)
Chengalpattu, Tamil Nadu
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About the Specialization
What is Data Science at SRM Institute of Science and Technology (Deemed to be University) Chengalpattu?
This BCA in Data Science program at SRM Institute of Science and Technology focuses on equipping students with a strong foundation in computer applications combined with specialized knowledge in data analysis, machine learning, and big data technologies. It is designed to meet the burgeoning demand for skilled data professionals in India''''s rapidly expanding digital economy, providing a unique blend of theoretical understanding and practical application crucial for this field.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics and computing, seeking entry into the dynamic field of data science. It also caters to students who aspire to build analytical and problem-solving skills to manage and interpret large datasets, making them suitable for roles in various sectors from finance to healthcare, and is a stepping stone for further specialization.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Data Analyst, Business Intelligence Developer, Junior Data Scientist, or Machine Learning Engineer in Indian companies and MNCs. Entry-level salaries typically range from INR 3-6 lakhs per annum, with significant growth potential up to INR 10-15 lakhs or more with experience. The program aligns with industry-recognized skills, preparing students for certifications in areas like Python for Data Science or AWS/Azure Data Analytics.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (C/C++, Python)- (Semester 1-2)
Dedicate significant time to thoroughly understand programming concepts in C, C++, and Python. Practice daily coding problems on platforms like HackerRank or LeetCode (easy level) to solidify logic and syntax. Form study groups to debug and discuss solutions regularly.
Tools & Resources
HackerRank, GeeksforGeeks, CodeChef, NPTEL courses on Programming
Career Connection
Strong programming skills are the bedrock for any data science role, crucial for data manipulation, algorithm implementation, and successfully clearing technical coding rounds in placement interviews.
Build a Strong Mathematical and Statistical Base- (Semester 1-2)
Pay close attention to Discrete Mathematics, Probability, and Statistics courses. Practice problems from textbooks regularly and utilize online resources for conceptual clarity. Focus on understanding the underlying logic rather than just memorizing formulas for better application.
Tools & Resources
Khan Academy, MIT OpenCourseWare for Mathematics, Standard Statistics textbooks
Career Connection
These subjects are fundamental for understanding machine learning algorithms, hypothesis testing, and effective data interpretation, which are all essential skills for a professional data scientist.
Cultivate Effective Communication Skills- (Semester 1-2)
Actively participate in English and Communication Skills labs. Practice presenting ideas clearly, engage in group discussions, and refine technical writing skills. Seek feedback from instructors and peers to continuously improve verbal and written communication abilities.
Tools & Resources
Toastmasters (if available), English learning apps, Presentation tools like Canva
Career Connection
Data scientists often need to present complex findings to non-technical stakeholders; strong communication is vital for effectively conveying insights and influencing decisions, aiding career progression.
Intermediate Stage
Develop Hands-on Data Science Projects- (Semester 3-5)
Apply learned concepts from Python, DBMS, Data Science Fundamentals, and Machine Learning to build small, end-to-end projects. Start with readily available datasets on Kaggle. Focus on data cleaning, exploration, model building, and basic visualization to create a robust portfolio.
Tools & Resources
Kaggle, Jupyter Notebook, Google Colab, GitHub, Python libraries (Pandas, NumPy, Scikit-learn, Matplotlib)
Career Connection
A strong portfolio of projects is crucial for demonstrating practical skills to recruiters and is often the first filter in the Indian job market, showcasing your ability to apply theoretical knowledge.
Engage in Internships and Workshops- (Semester 4-5)
Actively seek out internships during summer breaks or semester breaks, even if unpaid initially, to gain invaluable industry exposure. Participate in workshops, bootcamps, and hackathons focused on data science and analytics to learn new tools and network with professionals.
Tools & Resources
LinkedIn, Internshala, College placement cell, Industry events and seminars
Career Connection
Internships provide real-world experience, build industry contacts, and often lead to pre-placement offers, significantly boosting career prospects and understanding industry demands.
Specialize in a Niche Area through Electives- (Semester 5)
Utilize elective courses (like Data Mining, Deep Learning, NLP, Reinforcement Learning) to delve deeper into an area of interest within data science. Supplement classroom learning with online advanced courses and build specialized projects to demonstrate expertise.
Tools & Resources
Coursera, Udemy, edX, Specialized books and research papers
Career Connection
Specialization helps in targeting specific roles (e.g., NLP Engineer, Deep Learning Researcher) and makes a candidate more attractive for niche industry requirements, enhancing employability.
Advanced Stage
Focus on Comprehensive Placement Preparation- (Semester 6)
Start preparing for placements early. This includes revising all core data science concepts, practicing aptitude and logical reasoning, undergoing mock interviews (technical and HR), and diligently working on your final year project to be interview-ready.
Tools & Resources
Placement preparation books, Online test platforms (e.g., PrepInsta), College training and placement cell
Career Connection
Thorough preparation is critical for navigating the competitive campus placement process and securing a desirable entry-level position in top Indian IT and analytics firms.
Develop a Capstone Project with Industry Relevance- (Semester 6)
For the final year project, choose a problem statement that addresses a real-world business challenge or utilizes cutting-edge data science techniques. Collaborate with faculty or industry mentors. Document the project meticulously with a strong focus on practical implementation and outcomes.
Tools & Resources
Industry case studies, Research papers, Advanced ML/DL frameworks, Project management tools
Career Connection
A high-quality capstone project is a powerful demonstration of applied skills and can be a significant talking point in job interviews, showcasing problem-solving and implementation abilities to potential employers.
Network and Brand Building- (Semester 5-6)
Attend virtual and in-person industry conferences, seminars, and meetups. Connect with professionals on LinkedIn. Maintain an active GitHub profile showcasing your projects and potentially contribute to open-source initiatives to build your professional brand.
Tools & Resources
LinkedIn, GitHub, Industry events, Professional associations (e.g., Data Science Society)
Career Connection
Networking helps in discovering hidden job opportunities, getting referrals, and staying updated with industry trends, which are crucial for long-term career growth in India''''s dynamic tech ecosystem.
Program Structure and Curriculum
Eligibility:
- Minimum 50% in 10th and 12th in HSC / CBSE / ISCE (Min 45% for vocational students)
Duration: 3 years / 6 semesters
Credits: 140 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADSC101J | FOUNDATIONAL ENGLISH | Core | 3 | Reading Comprehension, Grammar and Vocabulary, Basic Writing Skills, Introduction to Communication, Listening and Speaking |
| 21ADSC102J | COMPUTER ORGANIZATION AND ARCHITECTURE | Core | 4 | Digital Logic Circuits, Data Representation, Basic Computer Organization, Memory System, I/O Organization |
| 21ADSC103J | PROGRAMMING IN C | Core | 4 | C Language Fundamentals, Control Structures, Arrays and Strings, Functions and Pointers, Structures and Unions |
| 21ADSC104J | DISCRETE MATHEMATICS | Core | 4 | Mathematical Logic, Set Theory and Relations, Functions and Recurrence Relations, Graph Theory, Boolean Algebra |
| 21ADSL105J | COMPUTER ORGANIZATION AND ARCHITECTURE LAB | Lab | 2 | Logic Gates Simulation, Combinational Circuits, Sequential Circuits, Assembly Language Programming Basics |
| 21ADSL106J | PROGRAMMING IN C LAB | Lab | 2 | C Program Implementation, Control Flow Structures, Array and String Manipulation, Function and Pointer Usage, File Handling in C |
| 21ADSL107J | COMMUNICATION SKILLS LAB | Lab | 1 | Phonetics and Pronunciation, Presentation Skills, Group Discussion Techniques, Interview Skills, Professional Etiquette |
| 21ADSE108L | BASIC APTITUDE AND REASONING | Skill Enhancement | 1 | Number Systems, Percentages and Ratios, Basic Logical Reasoning, Data Interpretation Fundamentals, Time and Work |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADSC201J | ADVANCED ENGLISH | Core | 3 | Advanced Grammar Usage, Technical Writing, Report and Proposal Writing, Public Speaking, Business Communication |
| 21ADSC202J | DATA STRUCTURES AND ALGORITHMS | Core | 4 | Abstract Data Types, Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms |
| 21ADSC203J | OBJECT ORIENTED PROGRAMMING WITH C++ | Core | 4 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Exception Handling, Templates and STL |
| 21ADSC204J | PROBABILITY AND STATISTICS | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Hypothesis Testing, Correlation and Regression |
| 21ADSL205J | DATA STRUCTURES AND ALGORITHMS LAB | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting Algorithm Analysis |
| 21ADSL206J | OBJECT ORIENTED PROGRAMMING WITH C++ LAB | Lab | 2 | C++ Program Development, Class and Object Implementation, Inheritance and Virtual Functions, Operator Overloading, File Operations in C++ |
| 21ADSE207L | ENVIRONMENTAL SCIENCE | Skill Enhancement | 1 | Ecosystems and Biodiversity, Environmental Pollution, Renewable Energy Sources, Climate Change, Environmental Ethics and Management |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADSC301J | DATABASE MANAGEMENT SYSTEMS | Core | 4 | Database Concepts, ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| 21ADSC302J | OPERATING SYSTEMS | Core | 4 | OS Introduction, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| 21ADSC303J | PYTHON PROGRAMMING | Core | 4 | Python Basics, Data Structures in Python, Functions and Modules, Object-Oriented Python, File Handling and Exceptions |
| 21ADSC304J | DATA SCIENCE FUNDAMENTALS | Core | 4 | Introduction to Data Science, Data Collection and Preprocessing, Exploratory Data Analysis, Data Visualization Basics, Introduction to Machine Learning |
| 21ADSL305J | DATABASE MANAGEMENT SYSTEMS LAB | Lab | 2 | SQL Commands and Queries, Database Design Implementation, Stored Procedures and Triggers, Views and Joins, Report Generation from Database |
| 21ADSL306J | PYTHON PROGRAMMING LAB | Lab | 2 | Python Scripting, Using Python Libraries (NumPy, Pandas), Data Manipulation, Basic Plotting, Web Scraping Basics |
| 21ADSE307L | CRITICAL THINKING AND REASONING | Skill Enhancement | 1 | Logical Reasoning, Problem Solving Strategies, Argument Analysis, Decision Making, Creative Thinking |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADSC401J | COMPUTER NETWORKS | Core | 4 | Network Models (OSI/TCP-IP), Physical Layer, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| 21ADSC402J | WEB DEVELOPMENT | Core | 4 | HTML5 and CSS3, JavaScript Fundamentals, DOM Manipulation, Front-end Frameworks (basics), Server-Side Scripting (basics), Database Integration |
| 21ADSC403J | MACHINE LEARNING | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Clustering Techniques, Model Evaluation |
| 21ADSC404J | BIG DATA ANALYTICS | Core | 4 | Big Data Concepts, Hadoop Ecosystem, MapReduce Framework, Spark Framework, NoSQL Databases, Data Warehousing Concepts |
| 21ADSL405J | WEB DEVELOPMENT LAB | Lab | 2 | Static Web Page Design, Interactive Web Elements, Client-Side Scripting, Server-Side Scripting, Database Connectivity for Web Apps |
| 21ADSL406J | MACHINE LEARNING LAB | Lab | 2 | Scikit-learn Implementation, Data Preprocessing for ML, Linear Regression Projects, Classification Model Building, Clustering Algorithm Application |
| 21ADSE407L | ENTREPRENEURSHIP | Skill Enhancement | 1 | Entrepreneurial Mindset, Business Idea Generation, Market Analysis, Business Plan Development, Funding and Pitching |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADSC501J | DATA MINING AND WAREHOUSING | Core | 4 | Data Warehouse Architecture, OLAP Operations, Data Preprocessing for Mining, Association Rule Mining, Classification and Prediction, Clustering Techniques |
| 21ADSC502J | DEEP LEARNING | Core | 4 | Neural Network Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Learning Frameworks (TensorFlow/Keras) |
| 21ADSC503J | CLOUD COMPUTING | Core | 4 | Cloud Computing Paradigms, Virtualization Technology, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Cloud Security, Introduction to AWS/Azure/GCP |
| 21ADSE504L | ADVANCED APTITUDE AND REASONING | Skill Enhancement | 1 | Advanced Quantitative Aptitude, Complex Logical Puzzles, Verbal Reasoning, Critical Reasoning, Data Interpretation Advanced |
| 21ADSL505J | DATA MINING AND WAREHOUSING LAB | Lab | 2 | ETL Process Implementation, Data Warehouse Design, Data Mining Tool Usage (e.g., Weka), Classification Algorithm Application, Clustering Analysis |
| 21ADSL506J | DEEP LEARNING LAB | Lab | 2 | TensorFlow/Keras Implementation, CNN for Image Classification, RNN for Sequence Data, Pre-trained Model Fine-tuning, Deep Learning Project Development |
| 21ADSP507J | MINI PROJECT | Project | 2 | Project Planning and Scoping, Literature Review, System Design, Implementation and Testing, Technical Report Writing |
| 21ADSE01L | NoSQL DATABASES (Professional Elective I) | Elective | 3 | Introduction to NoSQL, Key-Value Stores (Redis), Document Databases (MongoDB), Column-Family Stores (Cassandra), Graph Databases (Neo4j) |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21ADSC601J | INTERNET OF THINGS (IOT) | Core | 4 | IoT Architecture, Sensors and Actuators, IoT Communication Protocols, Cloud Platforms for IoT, IoT Security and Privacy |
| 21ADSC602J | BIG DATA VISUALIZATION | Core | 4 | Principles of Data Visualization, Visual Analytics Tools (Tableau/Power BI), Python Visualization Libraries (Matplotlib, Seaborn), Interactive Dashboards, Storytelling with Data |
| 21ADSL603J | INTERNET OF THINGS LAB | Lab | 2 | Sensor Interfacing with Microcontrollers, Data Acquisition from Sensors, IoT Platform Integration (e.g., MQTT), Basic IoT Application Development, Cloud Connectivity for IoT Devices |
| 21ADSP604J | PROJECT WORK | Project | 6 | Advanced Project Planning, System Design and Architecture, Complex Implementation, Comprehensive Testing and Evaluation, Final Project Presentation and Report |
| 21ADSE05L | EDGE COMPUTING (Professional Elective II) | Elective | 3 | Introduction to Edge Computing, Fog Computing Concepts, Edge Devices and Architectures, Edge Analytics and AI, Security and Privacy at the Edge |
