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B-SC in Information Technology Data Analytics at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Information Technology (Data Analytics) at SRM Institute of Science and Technology Chengalpattu?
This Information Technology program at SRM Institute of Science and Technology, Chengalpattu, allows students to specialize in Data Analytics through a carefully chosen set of program electives. It focuses on equipping graduates with the skills to collect, process, analyze, and interpret large datasets, addressing the critical demand for data professionals in the burgeoning Indian industry. The program emphasizes both foundational IT knowledge and advanced analytical techniques.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and computing, seeking entry into the data science and analytics domain. It also caters to working professionals in IT who aim to upskill in data-driven methodologies, and career changers transitioning into data roles. Candidates should possess basic programming knowledge and a keen interest in problem-solving with data.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths such as Data Analyst, Business Intelligence Developer, Junior Data Scientist, and Machine Learning Engineer in various sectors like finance, e-commerce, and healthcare. Entry-level salaries typically range from INR 4-7 LPA, with experienced professionals earning significantly more. The program aligns with professional certifications from platforms like NASSCOM and provides a strong base for continuous career growth.
Student Success Practices
Foundation Stage
Master Programming & Mathematical Foundations- (Semester 1-2)
Dedicate time in Semesters 1 and 2 to build a strong base in C, C++, and Python programming. Simultaneously, focus on discrete mathematics, probability, and statistics, as these form the bedrock for advanced data analytics. Use online platforms to practice coding challenges regularly.
Tools & Resources
GeeksforGeeks, HackerRank, NPTEL courses for Discrete Math and Probability
Career Connection
A solid foundation in programming and mathematics is essential for understanding and implementing complex algorithms, which is crucial for data analyst and data scientist roles.
Develop Strong Problem-Solving Acumen- (Semester 1-2)
Actively participate in problem-solving labs and coding competitions. Focus on breaking down complex problems into smaller, manageable parts and developing efficient algorithms. Collaborate with peers to discuss different approaches and learn from diverse perspectives.
Tools & Resources
CodeChef, LeetCode, Project Euler
Career Connection
Employers highly value problem-solving skills. Excelling in this area enhances your logical reasoning, which is critical for analytical roles and technical interviews in top Indian companies.
Engage in Academic & Peer Learning- (Semester 1-2)
Form study groups to discuss core concepts in Digital Computer Fundamentals, Data Structures, and Operating Systems. Utilize faculty office hours for clarifications and deeper understanding. Present concepts to peers to solidify your knowledge and improve communication skills.
Tools & Resources
Textbooks and reference materials, Internal college mentorship programs
Career Connection
Strong academic performance and collaborative learning build a robust knowledge base and communication skills, which are vital for team-based projects and professional success.
Intermediate Stage
Specialize in Data-Centric Technologies- (Semester 3-5)
From Semester 3, deeply engage with subjects like Database Management Systems and Python Programming, which are directly relevant to data analytics. Start exploring data manipulation libraries (Pandas, NumPy) in Python. Begin building a portfolio of small data projects.
Tools & Resources
Kaggle (for datasets), DataCamp/Coursera for Python data science courses, SQL Practice websites
Career Connection
Proficiency in SQL and Python is non-negotiable for Data Analytics roles. Early specialization provides a competitive edge for internships and entry-level positions in Indian tech companies.
Seek Early Industry Exposure & Internships- (Semester 3-5)
Actively look for mini-projects or short internships in areas related to data analysis during semester breaks. Leverage college career services or personal networks to find opportunities. This hands-on experience translates theoretical knowledge into practical skills.
Tools & Resources
College Placement Cell, LinkedIn, Internshala
Career Connection
Practical experience through internships is highly valued by Indian recruiters. It demonstrates application skills, industry awareness, and can often lead to pre-placement offers.
Build a Portfolio of Data Projects- (Semester 3-5)
Start working on personal projects using real-world datasets. Focus on different stages of data analytics: data cleaning, exploration, visualization, and basic modeling. Document your projects on platforms like GitHub to showcase your skills effectively.
Tools & Resources
GitHub, Tableau Public (for visualizations), Jupyter Notebooks
Career Connection
A strong project portfolio is a testament to your capabilities and helps distinguish you in the job market, especially for roles requiring practical implementation and analytical thinking.
Advanced Stage
Deep Dive into Specialization Electives- (Semester 5-6)
Carefully choose program electives like Data Warehousing, Data Mining, Machine Learning, Big Data Analytics, Deep Learning, Business Intelligence, and Data Visualization. Focus on understanding their theoretical underpinnings and practical implementation through labs and dedicated projects.
Tools & Resources
TensorFlow/Keras/PyTorch, Apache Hadoop/Spark, Tableau/Power BI, NPTEL advanced courses
Career Connection
Mastering these advanced topics prepares you for specialized roles like Data Scientist, ML Engineer, or BI Consultant, which are high-demand positions in India''''s booming data industry.
Undertake a Capstone Project or Research- (Semester 6)
For your final semester project, choose a complex data analytics problem. Work independently or in a small team to apply all learned techniques, from data collection to model deployment and result interpretation. Aim for a novel solution or a significant improvement to an existing one.
Tools & Resources
University research labs, Industry mentors, Relevant research papers
Career Connection
A strong capstone project showcases your ability to handle end-to-end data science workflows, providing a compelling talking point in interviews for advanced data roles.
Prepare for Placements and Professional Growth- (Semester 6)
Focus on enhancing soft skills, interview preparation, and resume building in the final semesters. Attend workshops on communication and aptitude. Network with alumni and industry professionals. Stay updated on latest trends in data analytics through online forums and industry reports.
Tools & Resources
College Placement Cell services, Mock interview platforms, LinkedIn Learning
Career Connection
Holistic preparation ensures successful placements in leading companies and sets the stage for continuous learning and professional advancement in a rapidly evolving field.
Program Structure and Curriculum
Eligibility:
- A pass in H.Sc. (10+2) or its equivalent with a minimum aggregate of 50%.
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 |
|---|---|---|---|---|
| 21LLN101T | Communicative English | Core | 2 | Language skills for communication, Grammar and vocabulary, Speaking and presentation skills, Reading comprehension, Writing clear reports |
| 21LHS101T | Principles of Management | Core | 3 | Introduction to management concepts, Planning and decision-making, Organizing and staffing, Directing and controlling functions, Organizational behavior |
| 21LIT101T | Problem Solving Techniques | Core | 3 | Problem solving methodologies, Algorithms and flowcharts, Pseudocode and logic development, Introduction to data structures, Recursive techniques |
| 21LIT102T | Digital Computer Fundamentals | Core | 3 | Number systems and codes, Boolean algebra and logic gates, Combinational logic circuits, Sequential logic circuits, Memory organization |
| 21LIT103T | Introduction to C Programming | Core | 3 | C language basics and data types, Operators and expressions, Control flow statements, Functions, arrays, and strings, Pointers and structures |
| 21LIT101L | Problem Solving Techniques Lab | Lab | 2 | Implementing algorithms, Debugging techniques, Developing logic for problems, Hands-on with data structure basics, Testing problem solutions |
| 21LIT103L | C Programming Lab | Lab | 2 | Practical C program development, Using control statements, Implementing functions and arrays, Pointer applications, File handling in C |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEL201T | Environmental Science | Core | 2 | Ecosystems and their components, Natural resources and management, Biodiversity and conservation, Environmental pollution, Climate change and sustainable development |
| 21LMA201T | Discrete Mathematics | Core | 4 | Mathematical logic and proofs, Set theory and relations, Functions and combinatorics, Graph theory fundamentals, Algebraic structures |
| 21LIT201T | Data Structures | Core | 3 | Arrays, linked lists, stacks, queues, Trees and binary search trees, Graph representations and traversals, Hashing techniques, Sorting and searching algorithms |
| 21LIT202T | Object-Oriented Programming using C++ | Core | 3 | OOP concepts: classes, objects, Inheritance and polymorphism, Abstraction and encapsulation, Constructors and destructors, Templates and exception handling |
| 21LIT203T | Computer Organization and Architecture | Core | 3 | Basic computer structure, CPU organization and functions, Memory hierarchy and cache, Input/Output organization, Instruction sets and addressing modes |
| 21LIT201L | Data Structures Lab | Lab | 2 | Implementing linear data structures, Tree and graph implementations, Sorting and searching algorithms, Performance analysis of data structures, Debugging data structure programs |
| 21LIT202L | Object-Oriented Programming using C++ Lab | Lab | 2 | Developing C++ classes and objects, Inheritance and virtual functions, Polymorphism implementation, Operator overloading, File handling in C++ |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LHS301T | Professional Ethics | Core | 2 | Ethical theories and principles, Professional codes of conduct, Cyber ethics and privacy, Intellectual property rights, Social responsibility of IT professionals |
| 21LMA301T | Probability and Statistics | Core | 4 | Probability theory and axioms, Random variables and distributions, Sampling and estimation, Hypothesis testing, Correlation and regression analysis |
| 21LIT301T | Operating Systems | Core | 3 | Operating system functions, Process management and scheduling, Memory management techniques, Virtual memory and paging, File systems and I/O management |
| 21LIT302T | Database Management Systems | Core | 3 | Database concepts and architecture, Entity-Relationship model, Relational model and algebra, Structured Query Language (SQL), Normalization and transaction management |
| 21LIT303T | Introduction to Python Programming | Core | 3 | Python syntax and data types, Control flow and functions, Modules and packages, File I/O operations, Object-Oriented Programming in Python |
| 21LIT302L | Database Management Systems Lab | Lab | 2 | Executing DDL and DML commands, Writing complex SQL queries, Implementing joins and subqueries, Basic PL/SQL programming, Database design and schema creation |
| 21LIT303L | Python Programming Lab | Lab | 2 | Developing Python scripts, Working with data structures in Python, File handling applications, Basic data analysis with Python libraries, Error handling and debugging Python code |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LLN401T | Quantitative Aptitude and Logical Reasoning | Core | 2 | Numerical ability and data interpretation, Problem-solving in arithmetic and algebra, Logical reasoning puzzles, Analytical and critical thinking, Series and patterns recognition |
| 21LIT401T | Computer Networks | Core | 3 | Network models (OSI, TCP/IP), Physical and Data Link Layer concepts, Network Layer: IP addressing, routing, Transport Layer: TCP, UDP, Application Layer protocols (HTTP, DNS) |
| 21LIT402T | Introduction to Java Programming | Core | 3 | Java language fundamentals, OOP principles in Java, Inheritance, interfaces, packages, Exception handling and multithreading, Applets and GUI programming basics |
| 21LIT403T | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements engineering, Software design principles, Software testing methodologies, Project management and quality assurance |
| 21LIT404T | Web Technology | Core | 3 | HTML, CSS, and JavaScript basics, Client-server architecture, XML and JSON data formats, Web servers and deployment, Responsive web design principles |
| 21LIT402L | Java Programming Lab | Lab | 2 | Developing Java applications, Implementing OOP concepts in Java, Exception handling in practical scenarios, Multithreading applications, Database connectivity using JDBC |
| 21LIT404L | Web Technology Lab | Lab | 2 | Creating dynamic HTML pages with CSS, JavaScript for interactive web elements, AJAX for asynchronous communication, Implementing responsive layouts, Developing simple web applications |
| 21LIT405P | Mini Project I | Project | 1 | Problem identification and analysis, System design and planning, Implementation and coding, Testing and debugging, Documentation and presentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LHS501T | Human Values and Indian Ethos | Core | 2 | Values in daily life, Ethical thinking and decision-making, Indian philosophical thoughts, Holistic personality development, Stress management and well-being |
| 21LIT501T | Data Warehousing and Data Mining | Program Elective (Data Analytics Track) | 3 | Data warehousing architecture, ETL process and data cubes, OLAP operations, Data mining techniques and tasks, Association rule mining and classification |
| 21LIT504T | Machine Learning | Program Elective (Data Analytics Track) | 3 | Supervised and unsupervised learning, Regression and classification algorithms, Clustering techniques, Introduction to neural networks, Model evaluation and validation |
| 21LIT506L | Data Warehousing and Data Mining Lab | Lab (Data Analytics Track) | 2 | Using ETL tools for data integration, Performing OLAP queries, Hands-on with data mining software (e.g., Weka), Implementing classification algorithms, Clustering data sets |
| 21LIT508L | Machine Learning Lab | Lab (Data Analytics Track) | 2 | Working with Python ML libraries (Scikit-learn), Data preprocessing and feature engineering, Implementing various ML models, Evaluating model performance, Mini projects on ML applications |
| 21LOE5XXT | Open Elective | Open Elective | 3 | Topics determined by student choice, Interdisciplinary subjects, Skill enhancement areas, General knowledge and humanities, Emerging technologies |
| 21LIT501P | Internship | Internship | 1 | Industry exposure and experience, Applying theoretical knowledge, Professional skill development, Project work in a real-world setting, Networking and career exploration |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LIT601T | Big Data Analytics | Program Elective (Data Analytics Track) | 3 | Introduction to Big Data ecosystem, Hadoop and MapReduce framework, HDFS and Spark for processing, NoSQL databases, Data streaming and real-time analytics |
| 21LIT602T | Deep Learning | Program Elective (Data Analytics Track) | 3 | Neural network architectures, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders and GANs, Deep learning frameworks (TensorFlow, Keras) |
| 21LIT603T | Business Intelligence | Program Elective (Data Analytics Track) | 3 | BI architecture and components, Data visualization and dashboards, Reporting and data storytelling, Decision support systems, Predictive analytics for business |
| 21LIT605T | Data Visualization | Program Elective (Data Analytics Track) | 3 | Principles of effective data visualization, Types of charts and graphs, Interactive dashboards design, Data visualization tools (Tableau, Power BI), Storytelling with data |
| 21LIT606L | Big Data Analytics Lab | Lab (Data Analytics Track) | 2 | Hadoop ecosystem setup and usage, MapReduce programming exercises, Spark for data processing, NoSQL database operations (e.g., MongoDB), Processing large datasets |
| 21LIT607L | Deep Learning Lab | Lab (Data Analytics Track) | 2 | Implementing neural networks with TensorFlow/Keras, Building CNNs for image classification, Developing RNNs for sequence data, Experimenting with deep learning models, Tuning hyperparameters for performance |
| 21LIT608L | Business Intelligence Lab | Lab (Data Analytics Track) | 2 | Designing interactive dashboards in Tableau/Power BI, Creating various types of reports, Data analysis for business insights, Working with different data sources, Presenting data effectively |
| 21LIT610L | Data Visualization Lab | Lab (Data Analytics Track) | 2 | Hands-on with Tableau/Power BI, Using Python libraries (Matplotlib, Seaborn) for visualization, Creating custom visualizations, Exploratory data analysis using visuals, Communicating insights through dashboards |
| 21LOE6XXT | Open Elective | Open Elective | 3 | Topics determined by student choice, Interdisciplinary subjects, Skill enhancement areas, General knowledge and humanities, Emerging technologies |
| 21LIT601P | Project | Project | 4 | In-depth project planning and execution, Advanced problem-solving, Developing a comprehensive system/application, Rigorous testing and validation, Detailed documentation and presentation |
