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B-TECH in Computer Science And Engineering With Data Science at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science and Engineering with Data Science at SRM Institute of Science and Technology Chengalpattu?
This B.Tech Computer Science and Engineering with Data Science program at SRM Institute of Science and Technology focuses on equipping students with expertise in data analytics, machine learning, and artificial intelligence, crucial for India''''s booming digital economy. The program emphasizes hands-on experience and theoretical foundations to tackle complex data challenges. It stands out by integrating core CSE principles with advanced data methodologies, preparing graduates for diverse roles across various Indian industries.
Who Should Apply?
This program is ideal for aspiring engineers and curious minds passionate about data-driven problem-solving. It caters to fresh graduates seeking entry into the high-growth fields of data science, machine learning, and AI within India. It also suits working professionals aiming to upskill and transition into advanced analytics roles, requiring a strong foundation in mathematics, statistics, and programming. No prior advanced data science knowledge is strictly required, but a keen analytical aptitude is beneficial.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative India-specific career paths such as Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Developer, and AI Engineer. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning INR 15-30+ LPA in top Indian tech companies. The curriculum aligns with certifications from AWS, Google Cloud, and Microsoft Azure, fostering continuous professional growth and leadership roles in India''''s technology sector.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus intensely on C/C++ and Python programming basics, data structures, and algorithms. Dedicate daily practice time to coding exercises and problem-solving.
Tools & Resources
HackerRank, LeetCode (easy level), GeeksforGeeks, Python Documentation
Career Connection
Strong coding skills are the bedrock for any CSE role, especially in data science technical interviews and competitive programming challenges, securing early career opportunities.
Build a Strong Mathematical & Statistical Base- (Semester 1-2)
Pay close attention to Calculus, Linear Algebra, Probability, and Statistics courses. Understand the theoretical underpinnings as they form the backbone of machine learning.
Tools & Resources
Khan Academy, NPTEL courses on Mathematics, Reference textbooks (e.g., ''''Probability and Statistics for Engineers'''')
Career Connection
Essential for understanding machine learning algorithms, model building, and accurately interpreting results in complex data science applications and research.
Engage in Peer Learning & Problem Solving- (Semester 1-2)
Form study groups, discuss complex topics, and collaboratively solve programming and mathematical problems. Actively participate in college-level coding contests.
Tools & Resources
College forums, Discord study groups, Local hackathons and coding competitions
Career Connection
Develops crucial teamwork, communication, and advanced problem-solving skills, which are highly valued by employers for collaborative data science projects and team environments.
Intermediate Stage
Dive into Data Science & Machine Learning Projects- (Semester 3-5)
Start working on small, independent data science projects using real-world datasets. Focus on data cleaning, exploratory data analysis, and basic model building.
Tools & Resources
Kaggle, UCI Machine Learning Repository, Google Colab, Scikit-learn, Pandas, Matplotlib
Career Connection
Building a portfolio of practical projects is crucial for demonstrating applied skills to recruiters for internships and entry-level positions in the data science field.
Seek Early Industry Exposure through Internships- (Semester 4-5)
Actively search for and apply to internships, even short-term ones, in data analytics or software development roles. Network with professionals through college and online platforms.
Tools & Resources
LinkedIn, Internshala, College placement cell, Professional networking events
Career Connection
Gaining practical industry experience, understanding real-world data workflows, and making professional connections are vital for future placements and career growth.
Participate in Workshops & Certifications- (Semester 4-5)
Enroll in specialized workshops on Python for Data Science, SQL, or Tableau. Consider introductory certifications from reputable online learning platforms.
Tools & Resources
NPTEL, Coursera (e.g., Google Data Analytics, IBM Data Science Professional Certificate), Udemy
Career Connection
Enhances specific technical skills beyond the core curriculum, making you a more competitive candidate for specialized data roles and demonstrating initiative.
Advanced Stage
Specialize and Build a Capstone Project- (Semester 6-8)
Choose advanced elective subjects in areas like Deep Learning, NLP, or Big Data. Undertake a comprehensive capstone project that solves a real-world problem using advanced data science techniques.
Tools & Resources
TensorFlow, PyTorch, AWS/Azure/GCP platforms, Domain-specific libraries and APIs
Career Connection
Showcases deep expertise in a specific data science domain, highly attractive for specialized roles and providing strong talking points in technical interviews for senior positions.
Master Interview Preparation & Soft Skills- (Semester 7-8)
Practice technical interview questions (DSA, ML concepts, SQL), work on resume building, and meticulously hone presentation and communication skills for job interviews.
Tools & Resources
LeetCode (medium/hard), HackerRank, Pramp (mock interviews), LinkedIn profile optimization, Career services workshops
Career Connection
Crucial for converting interview opportunities into desirable job offers, demonstrating not just technical prowess but also professional readiness and cultural fit.
Network Strategically & Explore Advanced Research- (undefined)
Attend industry conferences, connect with alumni, and explore opportunities for publishing research papers or presenting at academic forums in data science.
Tools & Resources
Industry events and conferences, LinkedIn for alumni connections, University research groups, arXiv.org
Career Connection
Opens doors to advanced roles, research positions, and provides insights into emerging trends, fostering long-term career growth, innovation, and leadership opportunities.
Program Structure and Curriculum
Eligibility:
- Minimum aggregate of 50% in Physics, Chemistry, and Mathematics (PCM) in 10+2 or equivalent examination. Passed with Physics, Chemistry, and Mathematics as compulsory subjects.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MAB101T | Calculus and Linear Algebra | Core | 4 | Differential Calculus, Integral Calculus, Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors |
| 18LEM101T | Communicative English | Core | 2 | Basic Grammar, Reading Comprehension, Writing Skills, Listening Practice, Spoken English Fundamentals |
| 18PYB101T | Physics | Core | 3 | Optics and Lasers, Modern Physics, Quantum Mechanics, Solid State Physics, Materials Science |
| 18CYB101T | Chemistry | Core | 3 | Electrochemistry, Corrosion and its Control, Organic Reaction Mechanism, Biomolecules and Polymers, Phase Rule and Alloys |
| 18CSE101J | Computer Programming | Core | 4 | Programming Fundamentals, C Language Basics, Control Structures, Functions and Arrays, Pointers and Strings |
| 18PDG101L | Life Skills and Ethics | Core | 1 | Self-Awareness, Interpersonal Skills, Values and Ethics, Time Management, Decision Making |
| 18PYB101L | Physics Laboratory | Lab | 2 | Optical Experiments, Electrical Measurements, Semiconductor Characteristics, Laser Diffraction, Magnetic Fields |
| 18CYB101L | Chemistry Laboratory | Lab | 2 | Volumetric Analysis, Water Quality Testing, Organic Compound Synthesis, pH Metry, Conductometry |
| 18CSED101L | Computer Programming Laboratory | Lab | 2 | C Program Implementation, Debugging Techniques, Conditional Statements, Looping Structures, Function Calls |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MAB102T | Advanced Calculus and Transform Techniques | Core | 4 | Vector Calculus, Fourier Series, Fourier Transforms, Laplace Transforms, Z-Transforms |
| 18PDG102T | Professional Communication | Core | 2 | Advanced Grammar, Technical Report Writing, Presentation Skills, Group Discussions, Interview Techniques |
| 18PYB102J | Engineering Graphics and Design | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Introduction to CAD |
| 18CYB102T | Environmental Science | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Waste Management, Sustainable Development |
| 18CSE102J | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| 18MAB103J | Probability and Statistics | Core | 3 | Probability Theory, Random Variables, Probability Distributions, Sampling Distributions, Hypothesis Testing |
| 18CSE103L | Data Structures and Algorithms Laboratory | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| 18MEB101L | Engineering Workshop | Lab | 2 | Carpentry Shop, Welding Shop, Fitting Shop, Sheet Metal Shop, Foundry Practice |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MAB201T | Discrete Mathematics | Core | 4 | Set Theory and Logic, Relations and Functions, Combinatorics, Graph Theory, Algebraic Structures |
| 18CST201J | Object Oriented Programming | Core | 4 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Exception Handling, Java Programming |
| 18CST202J | Database Management Systems | Core | 4 | Relational Model, SQL Queries, Database Design, Normalization, Transaction Management |
| 18CST203J | Computer Architecture and Organization | Core | 4 | CPU Organization, Memory Hierarchy, Input/Output Organization, Pipelining, Instruction Set Architectures |
| 18CSE201J | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks and Concurrency |
| 18CST204L | Object Oriented Programming Laboratory | Lab | 2 | Java Class Implementations, Inheritance and Interface Practice, Polymorphism Applications, Exception Handling Exercises, File I/O in Java |
| 18CST205L | Database Management Systems Laboratory | Lab | 2 | SQL Querying, Database Creation and Manipulation, Normalization Practice, PL/SQL Programming, Report Generation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MAB202T | Applied Statistics | Core | 4 | Probability Distributions, Sampling Theory, Estimation and Inference, Analysis of Variance (ANOVA), Regression and Correlation |
| 18CST206J | Design and Analysis of Algorithms | Core | 4 | Algorithm Complexity, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| 18CST207J | Computer Networks | Core | 4 | OSI and TCP/IP Models, Data Link Layer, Network Layer Protocols, Transport Layer Protocols, Network Security Basics |
| 18CSD201J | Introduction to Data Science | Core | 4 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization, Machine Learning Overview |
| 18CSD202J | Big Data Analytics | Core | 4 | Big Data Concepts, Hadoop Ecosystem, MapReduce, HDFS, Spark Framework |
| 18CST208L | Design and Analysis of Algorithms Laboratory | Lab | 2 | Implementation of Divide and Conquer, Dynamic Programming Solutions, Greedy Algorithm Problems, Graph Traversal Implementations, Complexity Analysis Practice |
| 18CSD203L | Introduction to Data Science Laboratory | Lab | 2 | Python for Data Manipulation (Pandas), Data Visualization (Matplotlib, Seaborn), Data Preprocessing, Basic Statistical Analysis, Exploratory Data Analysis |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MAB301T | Optimization Techniques | Core | 4 | Linear Programming, Simplex Method, Duality Theory, Non-Linear Programming, Transportation and Assignment Problems |
| 18CST301J | Theory of Computation | Core | 4 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| 18CSD301J | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Clustering Techniques |
| 18CSD302J | Data Warehousing and Data Mining | Core | 4 | Data Warehouse Architecture, ETL Process, OLAP Operations, Data Mining Concepts, Association Rule Mining |
| 18CSTxxxJ | Professional Elective I | Elective | 3 | |
| 18CSTxxxL | Professional Elective I Laboratory | Lab | 2 | |
| 18CSD303L | Machine Learning Laboratory | Lab | 2 | Implementing Regression Models, Implementing Classification Models, Clustering Algorithms, Model Evaluation Metrics, Feature Engineering |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CSD304J | Deep Learning | Core | 4 | Neural Networks Fundamentals, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Backpropagation, Transfer Learning |
| 18CSD305J | Natural Language Processing | Core | 4 | Text Preprocessing, Language Models, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis |
| 18CSTxxxJ | Professional Elective II | Elective | 3 | |
| 18CSTxxxJ | Professional Elective III | Elective | 3 | |
| 18CSD306L | Deep Learning Laboratory | Lab | 2 | Building Neural Networks, CNN Implementation, RNN and LSTM Models, TensorFlow/Keras Practice, Image Classification Tasks |
| 18CSD307L | Natural Language Processing Laboratory | Lab | 2 | NLTK and SpaCy Library Usage, Text Classification, Topic Modeling, Word Embeddings, Chatbot Development Basics |
| 18CSD308P | Industrial Internship / Project Work | Project/Internship | 6 | Industry Exposure, Real-world Problem Solving, Project Implementation, Technical Report Writing, Presentation Skills |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CSD401J | Data Visualization | Core | 4 | Principles of Visualization, Statistical Graphics, Interactive Dashboards, Tools: Tableau, Power BI, D3.js, Storytelling with Data |
| 18CSD402J | Cloud Computing for Data Science | Core | 4 | Cloud Fundamentals, AWS/Azure/GCP Services, Data Storage in Cloud, Big Data Processing on Cloud, Serverless Architectures |
| 18CSTxxxJ | Professional Elective IV | Elective | 3 | |
| 18CSTxxxJ | Professional Elective V | Elective | 3 | |
| 18CSD403P | Project Work – Phase I | Project | 3 | Problem Identification, Literature Survey, System Design, Initial Implementation, Project Proposal |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CSD404J | Data Ethics and Privacy | Core | 4 | Ethical AI Principles, Data Privacy Regulations (GDPR, Indian Context), Algorithmic Bias and Fairness, Responsible Data Handling, Data Governance |
| 18CSTxxxJ | Professional Elective VI | Elective | 3 | |
| 18CSTxxxJ | Professional Elective VII | Elective | 3 | |
| 18CSD405P | Project Work – Phase II | Project | 8 | Advanced Implementation, Testing and Validation, Result Analysis, Comprehensive Documentation, Final Presentation |
