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B-TECH in Electronics And Communication Engineering With Data Science at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Electronics and Communication Engineering with Data Science at SRM Institute of Science and Technology Chengalpattu?
This B.Tech Electronics and Communication Engineering with Data Science program at SRM Institute of Science and Technology focuses on building a unique blend of hardware, software, and analytical skills. It integrates the foundational principles of ECE with advanced data science techniques, addressing the growing demand for professionals who can handle data-driven challenges in communication systems, IoT, and embedded devices. This interdisciplinary approach is highly relevant in the rapidly evolving Indian tech industry, which is adopting AI and ML across various sectors.
Who Should Apply?
This program is ideal for aspiring engineers who possess a strong aptitude for mathematics, programming, and an interest in applying data analytics to real-world communication and electronic systems. It caters to fresh graduates seeking entry into the data-driven tech sector, working professionals from traditional ECE backgrounds looking to upskill in AI/ML, and career changers transitioning to roles in intelligent systems development and data analytics within the electronics domain.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Scientist, Machine Learning Engineer, IoT Data Analyst, Embedded AI Specialist, and Research Engineer in R&D firms. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning INR 15-30+ LPA. The program aligns with industry certifications in AI/ML and cloud platforms, providing significant growth trajectories in leading Indian and multinational companies.
Student Success Practices
Foundation Stage
Build Strong Programming and Math Fundamentals- (Semester 1-2)
Focus intently on mastering C, C++, and Python programming. Simultaneously, develop a robust understanding of Calculus, Linear Algebra, Probability, and Statistics. Utilize online platforms like HackerRank and LeetCode for coding practice and NPTEL/Coursera for conceptual clarity in mathematics. Engage with peers to solve complex problems and solidify foundational knowledge.
Tools & Resources
HackerRank, LeetCode, NPTEL (for Math courses), GeeksforGeeks, Python crash courses
Career Connection
A strong foundation in programming and mathematics is critical for data science roles, enabling efficient algorithm development, data manipulation, and understanding of complex models. It directly impacts eligibility for core tech and data science internships.
Engage in Early Project-Based Learning- (Semester 1-2)
Participate in mini-projects, hackathons, or departmental competitions focused on basic electronics or data manipulation. Start with simple projects like building an LED circuit with Arduino or analyzing small datasets using Python. This hands-on experience builds practical skills and helps apply theoretical knowledge from core ECE and early data science modules.
Tools & Resources
Arduino, Raspberry Pi, Kaggle (for beginner datasets), VS Code, GitHub
Career Connection
Practical project experience differentiates candidates in the competitive Indian job market. It demonstrates problem-solving abilities and a proactive learning approach, essential for securing internships and entry-level positions.
Cultivate Effective Study Habits and Peer Learning- (Semester 1-2)
Develop disciplined study routines, focusing on understanding concepts rather than rote memorization. Form study groups with classmates to discuss challenging topics, teach each other, and prepare for continuous assessments. Attend all lab sessions diligently and seek clarity from faculty regularly. Effective time management is crucial from the start.
Tools & Resources
Google Scholar, Library resources, Microsoft Teams/Zoom (for study groups), Online academic forums
Career Connection
Strong academic performance and collaborative skills are highly valued by recruiters. This stage builds the discipline and teamwork necessary for complex engineering and data science projects in professional settings.
Intermediate Stage
Specialize in Data Science Tools and Platforms- (Semester 3-5)
Deep dive into specialized data science tools and platforms beyond basic programming. Master Python libraries like NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn. Gain proficiency in SQL for database management. Explore cloud platforms like AWS, Azure, or GCP by completing introductory certifications or hands-on labs. Focus on practical application through case studies.
Tools & Resources
NumPy, Pandas, Scikit-learn, Kaggle Kernels, Datacamp, AWS/Azure/GCP Free Tier
Career Connection
Proficiency in industry-standard data science tools is a direct requirement for ML Engineer, Data Analyst, and Data Scientist roles. Certifications and practical projects showcase immediate job readiness.
Seek Internships and Industry Exposure- (Semester 3-5)
Actively apply for summer internships in core ECE areas or data science roles. Look for opportunities in startups, SMEs, or larger corporations to gain real-world experience. Participate in industry workshops, webinars, and guest lectures hosted by SRMIST. Network with professionals through LinkedIn and college career fairs to understand industry trends and requirements.
Tools & Resources
LinkedIn, Internshala, Naukri.com, College Career Services, Industry meetups
Career Connection
Internships are crucial for bridging the gap between academia and industry. They provide valuable experience, build professional networks, and often lead to pre-placement offers, significantly boosting career prospects in India.
Develop a Robust Portfolio of Projects- (Semester 3-5)
Start building a strong project portfolio by working on complex data science problems, integrating ECE concepts where possible. For instance, analyze sensor data from IoT devices, apply machine learning to communication signals, or build predictive models for electronic component failures. Host your projects on GitHub and document them thoroughly with clear problem statements, methodologies, and results.
Tools & Resources
GitHub, Jupyter Notebooks, Google Colab, TensorFlow/PyTorch, Open-source ECE projects
Career Connection
A well-curated project portfolio is a testament to your skills and initiative, making you highly attractive to employers. It allows you to demonstrate practical application of knowledge, a key factor in Indian tech hiring.
Advanced Stage
Master Advanced AI/ML Concepts and Specializations- (Semester 6-8)
Focus on advanced topics like Deep Learning, Natural Language Processing, and Computer Vision. Engage in specialized research projects or advanced electives. Consider contributing to open-source projects or participating in global AI/ML competitions. Explore niche areas like MLOps, explainable AI, or ethical AI to differentiate your profile.
Tools & Resources
Kaggle Competitions, Papers With Code, Advanced NPTEL courses, Online Specializations (Coursera, edX), Research papers
Career Connection
Mastering advanced topics is essential for securing roles as Lead Data Scientist, AI/ML Researcher, or specialized Engineer in top Indian tech firms and research institutions. It showcases expertise and research potential.
Intensive Placement and Interview Preparation- (Semester 6-8)
Dedicate significant time to rigorous placement preparation. This includes practicing aptitude tests, technical coding rounds (DSA, ML algorithms), and HR interviews. Participate in mock interviews conducted by the university''''s placement cell. Update your resume and LinkedIn profile to reflect your specialized skills and projects, tailoring them for specific job roles.
Tools & Resources
Platform specific preparation for TCS/Infosys/Wipro, InterviewBit, Glassdoor, College Placement Cell, Resume builders
Career Connection
Thorough preparation directly translates into successful placements. Indian companies often have multi-stage interview processes, and being well-prepared is key to securing high-paying jobs in the competitive market.
Undertake a Capstone Project or Research Thesis- (Semester 6-8)
Leverage your final year project (Phase I & II) to develop a substantial, innovative solution that integrates both ECE and Data Science principles. Aim for a project with real-world impact, potentially collaborating with an industry partner. Consider publishing your research in a conference or journal, which adds significant value to your academic and professional profile.
Tools & Resources
Industry collaboration platforms, Research publication databases, University R&D grants, Mendeley/Zotero for referencing
Career Connection
A high-quality capstone project or research thesis is a powerful differentiator for higher studies (M.Tech/Ph.D), R&D roles, or direct entry into product development teams. It demonstrates comprehensive skill application and innovation, highly sought after in the Indian tech ecosystem.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 4 years / 8 semesters
Credits: 180 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LE101T | English | Core | 3 | Communication Skills, Grammar and Vocabulary, Reading Comprehension, Listening and Speaking, Writing Skills |
| 21BS101T | Calculus and Differential Equations | Core | 4 | Differential Calculus, Integral Calculus, Ordinary Differential Equations, Partial Differential Equations, Vector Calculus |
| 21BS101P | Engineering Physics Lab | Lab | 2 | Optics Experiments, Electricity and Magnetism, Semiconductor Characteristics, Modern Physics Concepts, Material Properties |
| 21ES101T | Problem Solving using C | Core | 3 | C Programming Fundamentals, Control Flow Statements, Functions and Arrays, Pointers and Structures, File Handling |
| 21ES101P | C Programming Lab | Lab | 2 | C Program Development, Debugging Techniques, Problem-Solving Algorithms, Command Line Arguments, Simple Data Structures |
| 21ES102T | Engineering Graphics and Design | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, CAD Software Basics, Solid Modeling |
| 21ES103P | Engineering Practice Lab | Lab | 2 | Workshop Tools and Safety, Fitting and Carpentry, Welding and Sheet Metal, Electrical Wiring, Plumbing Basics |
| 21EN101 | Environmental Science | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Waste Management, Sustainable Development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LE201T | Professional English | Core | 3 | Business Communication, Technical Report Writing, Presentation Skills, Group Discussion Techniques, Interview Skills |
| 21BS201T | Linear Algebra and Numerical Methods | Core | 4 | Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors, Numerical Solutions of Equations, Interpolation and Approximation |
| 21BS202T | Engineering Physics | Core | 3 | Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics, Nanomaterials, Superconductivity |
| 21ES201T | Object Oriented Programming | Core | 3 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Abstract Classes and Interfaces, Exception Handling |
| 21ES201P | Object Oriented Programming Lab | Lab | 2 | C++ Program Implementation, Object-Oriented Design, Debugging and Testing, GUI Development Basics, Data Structures in C++ |
| 21ES202P | Electrical and Electronics Engineering Lab | Lab | 2 | Basic Electrical Circuits, DC and AC Measurements, Diode Characteristics, Transistor Biasing, Logic Gates |
| 21BS202P | Chemistry Lab | Lab | 2 | Volumetric Analysis, pH Metry and Conductometry, Water Quality Analysis, Corrosion Studies, Polymer Synthesis |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MA301T | Probability and Statistics for Data Science | Core | 4 | Probability Theory, Random Variables and Distributions, Descriptive Statistics, Inferential Statistics, Hypothesis Testing and Regression |
| 21EC301T | Electronic Circuits | Core | 3 | Diode Circuits and Rectifiers, BJT Amplifiers, FET Amplifiers, Feedback Amplifiers, Oscillators |
| 21EC302T | Digital System Design | Core | 3 | Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Memory and Programmable Logic, Introduction to HDL |
| 21EC303T | Signals and Systems | Core | 3 | Signal Classification, Linear Time-Invariant Systems, Fourier Series and Transform, Laplace Transform, Sampling Theorem |
| 21EC301P | Electronic Circuits Lab | Lab | 2 | Diode and Transistor Characteristics, Amplifier Design and Testing, Op-Amp Applications, Multivibrator Circuits, Power Supply Design |
| 21EC302P | Digital System Design Lab | Lab | 2 | Combinational Logic Implementation, Sequential Logic Implementation, HDL Programming (Verilog/VHDL), FPGA Based Design, Memory Interfacing |
| 21DS301T | Data Structures and Algorithms | Specialization Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| 21DS301P | Data Structures and Algorithms Lab | Specialization Lab | 2 | Implementation of Data Structures, Algorithm Efficiency Analysis, Recursion and Backtracking, Dynamic Programming Problems, Problem Solving with Data Structures |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MA401T | Discrete Mathematics for Data Science | Core | 4 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Recurrence Relations |
| 21EC401T | Analog and Digital Communication | Core | 3 | Amplitude Modulation, Frequency and Phase Modulation, Pulse Modulation Techniques, Digital Modulation Schemes, Noise in Communication Systems |
| 21EC402T | Electromagnetic Fields and Waves | Core | 3 | Electrostatics, Magnetostatics, Maxwell''''s Equations, Plane Wave Propagation, Transmission Lines |
| 21EC403T | Microprocessors and Microcontrollers | Core | 3 | 8086 Microprocessor Architecture, Instruction Set and Assembly Language, Interfacing Techniques, 8051 Microcontroller Architecture, Peripheral Programming |
| 21EC401P | Analog and Digital Communication Lab | Lab | 2 | AM/FM Modulation and Demodulation, Sampling and Quantization, ASK, FSK, PSK Implementation, Error Detection and Correction, TDM and FDM Systems |
| 21EC402P | Microprocessors and Microcontrollers Lab | Lab | 2 | Assembly Language Programming, I/O Device Interfacing, Timer and Interrupt Programming, Serial Communication, Embedded System Applications |
| 21DS401T | Database Management Systems | Specialization Core | 3 | ER Model and Relational Model, Relational Algebra and Calculus, SQL Queries and Joins, Normalization Techniques, Transaction Management |
| 21DS401P | Database Management Systems Lab | Specialization Lab | 2 | SQL DDL and DML Commands, Database Design and Implementation, Stored Procedures and Triggers, NoSQL Database Basics, Database Connectivity (Python/Java) |
| 21HSXXX | Humanities Elective | Elective | 2 | Professional Ethics, Indian Culture and Society, Introduction to Economics, Psychology Basics, Critical Thinking |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21EC501T | Digital Signal Processing | Core | 3 | Discrete Time Signals and Systems, Z-Transform, Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), FIR and IIR Filter Design |
| 21EC502T | Control Systems | Core | 3 | System Modeling (Transfer Functions), Time Domain Analysis, Frequency Domain Analysis, Stability Analysis (Routh-Hurwitz, Bode Plot), PID Controllers |
| 21DS501T | Machine Learning | Specialization Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation Metrics, Feature Engineering, Introduction to Neural Networks |
| 21DS502T | Artificial Intelligence | Specialization Core | 3 | AI Problem Solving Agents, Search Algorithms (BFS, DFS, A*), Knowledge Representation, Reasoning under Uncertainty, Expert Systems |
| 21DS501P | Machine Learning Lab | Specialization Lab | 2 | Python Libraries (NumPy, Pandas, Scikit-learn), Data Preprocessing and Visualization, Implementing ML Algorithms, Hyperparameter Tuning, Model Deployment Basics |
| 21DS502P | Artificial Intelligence Lab | Specialization Lab | 2 | Implementing Search Algorithms, Constraint Satisfaction Problems, Logic Programming (Prolog), Decision Making under Uncertainty, Rule-Based Systems |
| 21EC501P | Digital Signal Processing Lab | Lab | 2 | MATLAB/Python for DSP, Signal Generation and Analysis, Filter Design and Implementation, Spectral Analysis, Image and Audio Processing Basics |
| 21ECPEXXX | Professional Elective I (ECE Domain) | Elective | 3 | Advanced Digital Communications, Embedded System Design, VLSI Technology, Biomedical Instrumentation, IoT Architectures |
| 21DSPEXXX | Professional Elective I (Data Science Domain) | Elective | 3 | Big Data Technologies, Data Warehousing, Cloud Computing Basics, Data Mining Techniques, Business Intelligence |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21DS601T | Big Data Analytics | Specialization Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases (MongoDB, Cassandra), Data Stream Processing |
| 21DS602T | Data Visualization Techniques | Specialization Core | 3 | Principles of Data Visualization, Exploratory Data Analysis, Static and Interactive Visualizations, Dashboard Design, Data Storytelling |
| 21DS603T | Cloud Computing for Data Science | Specialization Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, AWS/Azure/GCP Services for Data, Serverless Computing, Cloud Security and Governance |
| 21EC601T | VLSI Design | Core | 3 | CMOS Logic Gates, VLSI Design Flow, ASIC Design Concepts, FPGA Architectures, Layout Design and Simulation |
| 21EC602T | Optical Communication | Core | 3 | Optical Fiber Characteristics, Light Sources (LEDs, Lasers), Photodetectors, Optical Transmitters and Receivers, Wavelength Division Multiplexing |
| 21DS601P | Big Data Analytics Lab | Specialization Lab | 2 | Hadoop Cluster Setup, MapReduce Programming, Spark Data Processing, NoSQL Database Operations, Data Ingestion Tools |
| 21DS602P | Data Visualization Techniques Lab | Specialization Lab | 2 | Tableau/Power BI for Dashboards, Python (Matplotlib, Seaborn, Plotly), Interactive Visualizations, Geospatial Data Visualization, Visualizing Big Data |
| 21DS603P | Cloud Computing for Data Science Lab | Specialization Lab | 2 | AWS/Azure/GCP Account Setup, Deploying Data Science Workloads, Cloud Storage (S3, Blob Storage), Serverless Functions (Lambda), Managed Database Services |
| 21ECPEXXX | Professional Elective II (ECE Domain) | Elective | 3 | Wireless Communication Systems, Advanced Embedded Systems, Digital Image Processing, Medical Electronics, RF and Microwave Engineering |
| 21DSPEXXX | Professional Elective II (Data Science Domain) | Elective | 3 | Deep Learning Architectures, Natural Language Processing, Computer Vision, Reinforcement Learning, Time Series Analysis |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21DS701T | Deep Learning | Specialization Core | 3 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformers |
| 21DS702T | Natural Language Processing | Specialization Core | 3 | Text Preprocessing, Word Embeddings (Word2Vec, GloVe), Syntactic and Semantic Analysis, Named Entity Recognition, Sentiment Analysis and Text Classification |
| 21DS703P | Deep Learning and NLP Lab | Specialization Lab | 2 | TensorFlow/PyTorch Implementation, Building CNNs for Image Classification, RNNs for Sequence Data, NLP Libraries (NLTK, spaCy, Hugging Face), Text Generation and Translation Models |
| 21EC701T | Internet of Things | Core | 3 | IoT Architecture and Protocols, Sensors, Actuators, and Microcontrollers, IoT Communication Technologies, Cloud Platforms for IoT, IoT Security and Privacy |
| 21EC702P | IoT Lab | Lab | 2 | Arduino/Raspberry Pi Programming, Interfacing Sensors and Actuators, Data Acquisition and Transmission, Cloud Integration (AWS IoT, Azure IoT), Building Smart Solutions |
| 21DS780 | Project Work Phase I | Project | 6 | Problem Identification and Scope Definition, Literature Survey and State-of-Art, Methodology and Design Phase, Project Planning and Management, Report Writing and Presentation |
| 21ECPEXXX | Professional Elective III (ECE Domain) | Elective | 3 | Antenna Theory and Design, Satellite Communication Systems, Robotics and Automation, Medical Imaging Systems, Embedded Linux |
| 21DSPEXXX | Professional Elective III (Data Science Domain) | Elective | 3 | Ethical AI and Bias, Data Governance and Compliance, MLOps Practices, Advanced Data Engineering, Cybersecurity Analytics |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21DS880 | Project Work Phase II | Project | 10 | System Implementation and Development, Testing and Validation, Results Analysis and Interpretation, Technical Report Writing, Project Defense and Presentation |
| 21ECPEXXX | Professional Elective IV (ECE Domain) | Elective | 3 | Advanced Communication Networks, Cognitive Radio, Automotive Electronics, Nanoelectronics, Smart Grid Technology |
| 21DSPEXXX | Professional Elective IV (Data Science Domain) | Elective | 3 | Financial Data Science, Health Informatics, Geospatial Analytics, Recommender Systems, Time Series Forecasting |
| 21OE00XXX | Open Elective | Elective | 2 | Interdisciplinary Studies, Entrepreneurship Development, Foreign Language, Digital Marketing, Human Resource Management |
