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B-TECH in Data Science Business Systems 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 & Business Systems at SRM Institute of Science and Technology (Deemed to be University) Chengalpattu?
This B.Tech Data Science & Business Systems program at SRM Institute of Science and Technology focuses on equipping students with a robust blend of data analytics, machine learning, and core business management skills. Addressing the burgeoning demand for data-driven decision-makers in the Indian corporate landscape, the program differentiates itself by integrating advanced technical competencies with practical business acumen. Its curriculum is meticulously designed to bridge the gap between complex data and strategic business outcomes, preparing graduates for a dynamic industry.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics, statistics, and logical reasoning, aspiring to build a career at the intersection of technology and business. It also caters to students keen on understanding complex datasets and leveraging them for strategic advantage. Individuals who enjoy problem-solving and are eager to contribute to India''''s rapidly digitizing economy will find this specialization particularly rewarding, offering a solid foundation for diverse professional roles.
Why Choose This Course?
Graduates of this program can expect diverse and high-demand career paths in India, including Data Scientist, Business Analyst, Machine Learning Engineer, AI Consultant, and Market Research Analyst. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning upwards of INR 15-30 LPA. The curriculum aligns with industry-recognized certifications in data science and business analytics, fostering growth trajectories in prominent Indian and multinational corporations operating within the country.
Student Success Practices
Foundation Stage
Build a Strong Programming and Math Core- (Semester 1-2)
Focus intensively on mastering foundational programming concepts (C, Python, OOP) and core mathematics (Linear Algebra, Calculus, Discrete Math). Regularly solve problems on online coding platforms to solidify logical thinking and algorithmic skills.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy, NPTEL courses
Career Connection
A strong foundation in these areas is crucial for success in advanced data science, machine learning, and business analytics courses, and is a prerequisite for most technical roles in the industry.
Cultivate Effective Study and Collaboration Habits- (Semester 1-2)
Develop consistent study routines, actively participate in class, and form study groups with peers. Practice presenting concepts and solutions to others to reinforce understanding and improve communication skills. Engage in early project work, even small ones, to learn teamwork.
Tools & Resources
Google Docs for collaborative notes, Trello for project management, University library resources, Peer-led workshops
Career Connection
Teamwork, communication, and structured learning are essential professional skills valued by employers, fostering academic excellence and project success.
Explore Emerging Tech and Foundational Data Concepts- (Semester 1-2)
Beyond the curriculum, start exploring introductory resources on data science and business systems. Read tech blogs, watch introductory videos on AI/ML, and understand the real-world applications of data. Participate in college tech clubs.
Tools & Resources
Towards Data Science blog, Coursera/edX introductory courses, Kaggle for datasets, College tech clubs, Department seminars
Career Connection
Early exposure helps in understanding career paths, developing a passion for the field, and identifying specific areas of interest for future specialization.
Intermediate Stage
Dive Deep into Data Science & Analytics Tools- (Semester 3-5)
Master Python/R for data manipulation, analysis, and visualization. Get hands-on with SQL for database management and explore fundamental machine learning libraries. Work on mini-projects to apply theoretical knowledge to practical scenarios.
Tools & Resources
Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, SQL tutorials, Kaggle competitions
Career Connection
Proficiency in these tools is non-negotiable for roles like Data Analyst, Jr. Data Scientist, and BI Developer, directly enhancing employability.
Seek Industry Exposure Through Internships and Workshops- (Semester 4-5)
Actively look for summer internships or short-term projects with companies, even small startups, to gain real-world experience. Attend workshops and seminars conducted by industry professionals to understand current trends and technologies.
Tools & Resources
LinkedIn, Internshala, University placement cell, Industry networking events, Company webinars
Career Connection
Internships provide practical skills, build professional networks, and are often a direct pipeline to full-time employment, significantly boosting placement chances.
Develop Business Acumen and Problem-Solving Skills- (Semester 3-5)
Beyond technical skills, focus on understanding how data analytics drives business decisions. Participate in case study competitions, read business magazines, and try to frame technical problems in a business context.
Tools & Resources
Harvard Business Review, Business news publications, Case study clubs, Discussions with faculty and industry mentors
Career Connection
The ability to translate technical insights into business value is highly sought after by employers for roles in Business Analytics, Product Management, and Consulting.
Advanced Stage
Specialize and Build a Robust Project Portfolio- (Semester 6-8)
Choose electives wisely to specialize in areas like Deep Learning, NLP, or Financial Analytics. Undertake significant capstone projects, preferably industry-sponsored or research-oriented, showcasing your specialized skills from end-to-end.
Tools & Resources
GitHub for project showcasing, Advanced libraries (TensorFlow, PyTorch, SpaCy), Research papers, Faculty advisors
Career Connection
A strong portfolio demonstrates practical expertise and problem-solving capabilities to potential employers, making you a competitive candidate for specialized roles.
Master Interview Skills and Placement Preparation- (Semester 7-8)
Dedicate substantial time to preparing for technical interviews, aptitude tests, and group discussions. Practice coding challenges, behavioral questions, and mock interviews. Tailor your resume and cover letter for specific job profiles.
Tools & Resources
InterviewBit, LeetCode (advanced), Glassdoor for company-specific interview questions, University career services, Alumni network
Career Connection
Effective interview preparation is critical for converting internship offers into full-time roles and securing positions in top companies during campus placements.
Network Strategically and Seek Mentorship- (Semester 6-8)
Actively network with professionals in the data science and business systems domain through LinkedIn, industry conferences, and alumni events. Seek out mentors who can guide your career path and provide insights into industry trends.
Tools & Resources
LinkedIn, Industry conferences (e.g., Data Science Summit India), Alumni meetups, Faculty network
Career Connection
Networking opens doors to job opportunities, mentorship, and professional growth, providing invaluable insights and support for long-term career development.
Program Structure and Curriculum
Eligibility:
- Minimum 50% aggregate in PCM/PCB in Higher Secondary Examination (10+2 pattern) or appearing in the current academic year with Physics, Chemistry and Mathematics / Biology / Biotechnology / Technical Vocational subject as major subjects in regular stream from any recognized board.
Duration: 4 years / 8 semesters
Credits: 184 Credits
Assessment: Internal: 50% (Continuous Assessment), External: 50% (End Semester Examination) - for theory courses. Practical courses are 100% internal.
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18FD101 | DISCRETE MATHEMATICS | Foundation | 4 | Logic and Proofs, Combinatorics, Graph Theory, Algebraic Structures, Lattices and Boolean Algebra |
| 18FD102 | ENGINEERING PHYSICS | Foundation | 3 | Quantum Physics, Materials Science, Optical Physics, Lasers and Fiber Optics, Nanotechnology |
| 18FD103 | ENGINEERING CHEMISTRY | Foundation | 3 | Water Technology, Instrumental Methods of Analysis, Electrochemistry and Corrosion, Polymer Chemistry, Nanomaterials |
| 18FD104 | PROGRAMMING FOR PROBLEM SOLVING | Foundation | 3 | Introduction to Programming, Data Types and Operators, Control Flow Statements, Functions and Modularity, Arrays and Pointers, Structures and Unions |
| 18FD105 | PRINCIPLES OF ELECTRICAL AND ELECTRONICS ENGINEERING | Foundation | 3 | DC and AC Circuits, Semiconductor Devices, Diodes and Transistors, Operational Amplifiers, Digital Electronics Fundamentals |
| 18FD106 | C PROGRAMMING LABORATORY | Lab | 2 | Basic C Programs, Control Structures Implementation, Functions and Arrays, Strings and Pointers, Structures and File Handling |
| 18FD107 | ENGINEERING PHYSICS AND CHEMISTRY LABORATORY | Lab | 1 | Experiments on Optics, Properties of Matter, Electrical Measurements, Chemical Analysis Techniques, Spectroscopy experiments |
| 18FD108 | BASIC ENGINEERING PRACTICES LABORATORY | Lab | 1 | Welding and Carpentry, Fitting and Sheet Metal Operations, Plumbing and Foundry, Electrical Wiring and Soldering, Machine shop practices |
| 18FD109 | ENGLISH COMMUNICATION | Foundation | 2 | Listening Comprehension, Spoken English and Pronunciation, Reading Skills, Writing for Professional Contexts, Presentation and Group Discussion Skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18FD201 | LINEAR ALGEBRA AND CALCULUS | Foundation | 4 | Matrices and Determinants, Vector Spaces and Transformations, Eigenvalues and Eigenvectors, Differential Calculus, Integral Calculus and Applications |
| 18FD202 | ENVIRONMENTAL SCIENCE | Foundation | 3 | Ecosystems and Biodiversity, Pollution and Control, Global Environmental Issues, Waste Management, Sustainable Development |
| 18FD203 | DATA STRUCTURES AND ALGORITHMS | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Techniques |
| 18FD204 | DIGITAL LOGIC DESIGN | Core | 3 | Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Registers and Counters, Memory Architectures |
| 18FD205 | OBJECT ORIENTED PROGRAMMING | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Exception Handling, File Input/Output |
| 18FD206 | DATA STRUCTURES AND ALGORITHMS LABORATORY | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Implementations |
| 18FD207 | OBJECT ORIENTED PROGRAMMING LABORATORY | Lab | 2 | Class and Object Creation, Inheritance and Polymorphism Exercises, Interface and Abstract Class Usage, Exception Handling Programs, GUI Applications Development |
| 18FD208 | DIGITAL LOGIC DESIGN LABORATORY | Lab | 1 | Verification of Logic Gates, Design of Combinational Circuits, Implementation of Sequential Circuits, Flip-Flops and Latches, Memory Interfacing |
| 18FD209 | SOFT SKILLS | Foundation | 1 | Verbal and Non-verbal Communication, Interpersonal Skills, Time Management and Stress Management, Professional Etiquette, Personality Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CS301 | DATABASE MANAGEMENT SYSTEMS | Core | 3 | Introduction to Databases, Entity-Relationship Model, Relational Model and SQL, Normalization and Query Processing, Transaction Management and Concurrency Control |
| 18CS302 | DESIGN AND ANALYSIS OF ALGORITHMS | Core | 3 | Asymptotic Notations and Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| 18CS303 | COMPUTER NETWORKS | Core | 3 | OSI and TCP/IP Models, Network Topologies and Devices, Addressing and Routing, Transport Layer Protocols, Application Layer Protocols |
| 18DS301 | FOUNDATIONS OF DATA SCIENCE | Core | 3 | Introduction to Data Science, Data Collection and Preprocessing, Exploratory Data Analysis, Data Visualization Fundamentals, Introduction to Statistical Methods |
| 18AD301 | WEB PROGRAMMING | Core | 3 | HTML5 and CSS3, JavaScript Fundamentals, DOM Manipulation and AJAX, Web APIs and Services, Introduction to Web Frameworks |
| 18CS304 | DATABASE MANAGEMENT SYSTEMS LABORATORY | Lab | 2 | SQL Querying and Data Definition, Database Design and Implementation, PL/SQL Programming, JDBC/ODBC Connectivity, Transaction Control |
| 18AD302 | WEB PROGRAMMING LABORATORY | Lab | 2 | Static Web Page Design, Interactive Web Elements with JavaScript, Dynamic Content with AJAX, Front-end Framework Basics, Web Form Validation |
| 18DS302 | DATA SCIENCE TOOLS AND TECHNIQUES LABORATORY | Lab | 2 | Python for Data Science, R Programming Basics, Data Manipulation with Pandas, Data Visualization with Matplotlib/Seaborn, Basic Statistical Analysis in Python/R |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CS401 | OPERATING SYSTEMS | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems and I/O Management |
| 18CS402 | ARTIFICIAL INTELLIGENCE | Core | 3 | Introduction to AI, Problem Solving and Search Algorithms, Knowledge Representation, Machine Learning Basics, Expert Systems and Logic Programming |
| 18CS403 | SOFTWARE ENGINEERING | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing and Quality Assurance, Software Project Management |
| 18DS401 | MACHINE LEARNING | Core | 3 | Supervised Learning, Unsupervised Learning, Regression and Classification Algorithms, Clustering Techniques, Model Evaluation and Selection |
| 18DS402 | BUSINESS INTELLIGENCE | Core | 3 | Data Warehousing Concepts, OLAP and ETL Processes, Data Mining Techniques, Business Analytics Frameworks, Reporting and Dashboarding Tools |
| 18CS404 | OPERATING SYSTEMS LABORATORY | Lab | 2 | Shell Scripting, Process Creation and Management, Inter-process Communication, CPU Scheduling Algorithms, Memory Allocation Strategies |
| 18DS403 | MACHINE LEARNING LABORATORY | Lab | 2 | Implementation of Regression Models, Classification Algorithms, Clustering Algorithms, Feature Engineering, Model Evaluation Metrics |
| 18DS404 | BUSINESS INTELLIGENCE LABORATORY | Lab | 2 | Data Extraction and Transformation, Data Warehousing Implementation, OLAP Cube Operations, Building Interactive Dashboards, BI Tool Usage (e.g., Tableau/Power BI) |
| 18CS405 | UNIVERSAL HUMAN VALUES | Audit Elective Course | 1 | Harmony in Human Being, Harmony in Family and Society, Harmony in Nature/Existence, Holistic Perception of Harmony, Understanding Human Values |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CS501 | COMPILER DESIGN | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization and Generation |
| 18AD501 | CLOUD COMPUTING | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization Technology, Cloud Security Challenges, Cloud Platforms (AWS/Azure/GCP Basics) |
| 18DS501 | BIG DATA ANALYTICS | Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases, Stream Processing |
| 18DS502 | DATA VISUALIZATION | Core | 3 | Principles of Effective Visualization, Types of Charts and Graphs, Interactive Dashboards, Data Storytelling, Visualization Tools (Tableau, Power BI) |
| 18DS503 | BUSINESS ANALYTICS AND STRATEGY | Core | 3 | Decision Making Frameworks, Strategic Planning and Analysis, Predictive and Prescriptive Analytics, Optimization Techniques, Risk Analytics |
| 18DS504 | BIG DATA ANALYTICS LABORATORY | Lab | 2 | Hadoop Cluster Setup, MapReduce Programming, Spark Application Development, Hive and Pig Queries, NoSQL Database Operations |
| 18DS505 | DATA VISUALIZATION LABORATORY | Lab | 2 | Creating Static and Interactive Visualizations, Dashboard Design and Development, Using Tableau/Power BI, Python Visualization Libraries (Plotly, Bokeh), Visualizing Complex Datasets |
| 18CS505 | PROFESSIONAL ELECTIVE – I | Elective | 3 | Refer Elective Basket for specific topics. Examples include Advanced Data Structures, Blockchain Technology, Image Processing. |
| 18CS506 | OPEN ELECTIVE – I | Elective | 3 | Refer Elective Basket for specific topics. General interdisciplinary subjects offered by other departments. |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CS601 | PRINCIPLES OF MANAGEMENT | Core | 3 | Management Functions (Planning, Organizing, Leading, Controlling), Organizational Structures, Leadership and Motivation, Decision Making and Communication, Managerial Ethics |
| 18DS601 | NATURAL LANGUAGE PROCESSING | Core | 3 | Text Preprocessing and Tokenization, N-grams and Language Models, Word Embeddings (Word2Vec, GloVe), POS Tagging and Named Entity Recognition, Sentiment Analysis and Text Classification |
| 18DS602 | DEEP LEARNING | Core | 3 | Neural Network Architectures, Perceptron and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Deep Learning Frameworks (TensorFlow, PyTorch) |
| 18DS603 | MARKETING ANALYTICS | Core | 3 | Consumer Behavior Analysis, Market Segmentation and Targeting, Pricing and Promotion Analytics, Campaign Management, Web Analytics and Social Media Metrics |
| 18DS604 | NATURAL LANGUAGE PROCESSING LABORATORY | Lab | 2 | Text Preprocessing using NLTK/SpaCy, Building Custom Language Models, Implementing Named Entity Recognition, Sentiment Analysis Applications, Text Classification Tasks |
| 18DS605 | DEEP LEARNING LABORATORY | Lab | 2 | Implementing Feedforward Neural Networks, Training CNNs for Image Classification, Developing RNNs for Sequence Data, Using TensorFlow/Keras or PyTorch, Transfer Learning Techniques |
| 18CS605 | PROFESSIONAL ELECTIVE – II | Elective | 3 | Refer Elective Basket for specific topics. |
| 18CS606 | OPEN ELECTIVE – II | Elective | 3 | Refer Elective Basket for specific topics. |
| 18CS607 | MINI PROJECT WITH SEMINAR | Project | 2 | Problem Identification and Scoping, System Design and Implementation, Testing and Debugging, Project Documentation, Technical Presentation Skills |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18DS701 | DATA MINING AND WAREHOUSING | Core | 3 | Data Preprocessing and Cleaning, Association Rule Mining, Classification and Prediction Techniques, Clustering Algorithms, Data Warehouse Design and ETL |
| 18DS702 | FINANCIAL ANALYTICS | Core | 3 | Financial Statement Analysis, Investment Analysis, Risk Management Techniques, Portfolio Optimization, Time Series Analysis in Finance |
| 18DS703 | DIGITAL MARKETING AND E-COMMERCE | Core | 3 | Search Engine Optimization (SEO), Search Engine Marketing (SEM), Social Media Marketing, Content Marketing Strategy, E-commerce Platforms and Analytics |
| 18DS704 | DATA MINING AND WAREHOUSING LABORATORY | Lab | 2 | Implementing Classification Algorithms, Performing Clustering Analysis, Discovering Association Rules, Building Data Warehouse Schemas, ETL Process Automation |
| 18CS704 | PROFESSIONAL ELECTIVE – III | Elective | 3 | Refer Elective Basket for specific topics. |
| 18CS705 | PROFESSIONAL ELECTIVE – IV | Elective | 3 | Refer Elective Basket for specific topics. |
| 18CS706 | OPEN ELECTIVE – III | Elective | 3 | Refer Elective Basket for specific topics. |
| 18CS707 | PROJECT PHASE – I | Project | 6 | Problem Statement Formulation, Extensive Literature Review, Requirement Gathering and Analysis, System Design and Architecture, Preliminary Implementation and Prototyping |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CS801 | INDUSTRIAL INTERNSHIP | Internship/Project | 12 | Real-world Problem Solving, Industry Best Practices, Professional Skill Development, Application of Theoretical Knowledge, Project Implementation in Industrial Setting |
| 18CS802 | PROJECT PHASE – II | Project | 8 | Advanced Implementation and Development, System Testing and Validation, Performance Evaluation, Comprehensive Project Report Writing, Final Project Presentation and Defense |
| 18CS803 | PROFESSIONAL ELECTIVE – V | Elective | 3 | Refer Elective Basket for specific topics. |
