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B-TECH in Data Science at Maulana Azad National Institute of Technology Bhopal
Bhopal, Madhya Pradesh
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About the Specialization
What is Data Science at Maulana Azad National Institute of Technology Bhopal Bhopal?
This B.Tech Data Science program at Maulana Azad National Institute of Technology Bhopal focuses on equipping students with a robust foundation in statistics, computer science, and machine learning principles essential for processing, analyzing, and interpreting vast datasets. It addresses the growing demand for skilled data professionals in India, emphasizing practical applications and interdisciplinary knowledge, critical for driving innovation across various sectors.
Who Should Apply?
This program is ideal for aspiring data scientists, analytics professionals, and machine learning engineers. It attracts fresh graduates with strong analytical and mathematical aptitude seeking entry into the data-driven industry, as well as students with a passion for leveraging data to solve complex real-world problems. A background in science or mathematics is beneficial.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including roles as Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Developer, or AI Engineer. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more. The strong curriculum prepares students for growth trajectories in major Indian tech firms, startups, and analytics consultancies, aligning with industry demand.
Student Success Practices
Foundation Stage
Master Core Programming and Math Fundamentals- (Semester 1-2)
Dedicate significant effort to mastering C programming, Data Structures, and foundational mathematics (Calculus, Linear Algebra, Probability). These are the building blocks for advanced Data Science concepts. Regularly solve problems to solidify understanding.
Tools & Resources
GeeksforGeeks, HackerRank, Coursera/NPTEL courses on Data Structures and Algorithms, Khan Academy for Math refreshers
Career Connection
A strong grasp of these fundamentals is critical for cracking technical interviews and excelling in initial project assignments in any data-centric role. It forms the backbone for advanced machine learning algorithms.
Build a Portfolio of Mini-Projects- (Semester 1-2)
Start working on small, independent projects using Python for basic data analysis and visualization. Apply concepts learned in ''''Introduction to Data Science'''' and ''''Statistical Methods for Data Science''''. Document your code and findings on GitHub.
Tools & Resources
Python, Pandas, NumPy, Matplotlib, Seaborn, Kaggle datasets (for practice), GitHub for version control
Career Connection
Early project work demonstrates practical skills to recruiters, sets you apart from peers, and helps identify your areas of interest within Data Science. It''''s a tangible proof of your learning.
Engage in Peer Learning and Academic Clubs- (Semester 1-2)
Actively participate in study groups and data science-focused academic clubs or societies within MANIT Bhopal. Discuss challenging concepts, share resources, and collaborate on assignments. Seek guidance from seniors.
Tools & Resources
WhatsApp/Discord groups for study, MANIT CSE/DS student clubs
Career Connection
This fosters a collaborative learning environment, improves problem-solving skills, and builds a professional network that can be invaluable for referrals and shared opportunities later in your career.
Intermediate Stage
Deep Dive into Machine Learning and Databases- (Semester 3-5)
Beyond classroom learning, take online courses or certifications in Machine Learning and Database Management. Focus on practical implementation using Python libraries and SQL. Understand the nuances of different algorithms and database queries.
Tools & Resources
Andrew Ng''''s Machine Learning course (Coursera), DataCamp/Databases specializations, LeetCode for SQL practice
Career Connection
These are core competencies for almost any Data Science role. A deeper understanding and practical application will make you highly desirable for internships and entry-level positions.
Seek Early Internships and Industry Exposure- (Semester 3-5)
Actively look for summer internships or part-time projects in relevant companies (startups, tech firms, analytics consultancies). Even unpaid internships provide invaluable real-world experience and networking opportunities.
Tools & Resources
LinkedIn, Internshala, Naukri.com, MANIT''''s Training & Placement Cell
Career Connection
Internships are crucial for understanding industry challenges, applying academic knowledge, and often convert into pre-placement offers, significantly boosting your final year placement prospects.
Participate in Hackathons and Data Challenges- (Semester 3-5)
Engage in national and international hackathons (e.g., Smart India Hackathon) and data science competitions on platforms like Kaggle. This enhances problem-solving skills, teamwork, and exposes you to diverse datasets and real-world problems.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub, Local hackathon organizers
Career Connection
Winning or performing well in these competitions adds significant weight to your resume, showcases your ability to work under pressure, and attracts attention from potential employers.
Advanced Stage
Specialize and Build a Capstone Project- (Semester 6-8)
Choose a specific area within Data Science (e.g., Deep Learning, NLP, Big Data) for your Major Project. Develop a comprehensive, innovative project, leveraging advanced techniques and tools, ensuring it has practical applicability.
Tools & Resources
TensorFlow, PyTorch, Spark, AWS/Azure/GCP, Research papers, academic mentors
Career Connection
A strong capstone project demonstrates your expertise and ability to deliver end-to-end solutions, often becoming the highlight of your portfolio for specialized roles and advanced studies.
Network Extensively and Prepare for Placements- (Semester 6-8)
Attend industry conferences, workshops, and alumni meets. Polish your resume, practice technical and HR interviews, and prepare for aptitude tests. Tailor your applications to specific company roles and requirements.
Tools & Resources
LinkedIn for networking, Mock interview platforms, MANIT Placement Cell resources
Career Connection
Networking opens doors to hidden opportunities. Thorough placement preparation ensures you are well-equipped to convert interviews into job offers at top companies in India.
Contribute to Open Source or Research- (Semester 6-8)
Consider contributing to open-source Data Science projects or engaging in research under faculty guidance. This demonstrates advanced technical skills, collaboration abilities, and a commitment to the field.
Tools & Resources
GitHub, GitLab, arXiv (for research papers), Faculty mentors
Career Connection
Such contributions highlight your proactive learning and advanced capabilities, making you an attractive candidate for R&D roles, product development, or even pursuing higher education/Ph.D. in India or abroad.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 8 semesters / 4 years
Credits: 177 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSDS101 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Vector Calculus, Ordinary Differential Equations |
| BSDS102 | Engineering Physics | Core | 4 | Wave Optics, Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics, Semiconductor Physics |
| ESDS103 | Basic Electrical Engineering | Core | 3 | DC Circuit Analysis, AC Circuit Analysis, Transformers, Electrical Machines, Basic Electronic Components |
| ESDS104 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Basics, Development of Surfaces |
| HSDS105 | Professional Communication | Core | 2 | Principles of Communication, Technical Report Writing, Presentation Skills, Group Discussion, Interview Techniques |
| BSDS106 | Engineering Physics Lab | Lab | 1 | Optics Experiments, Solid State Device Characteristics, Magnetic Field Measurements, Laser and Fiber Optics Applications |
| ESDS107 | Basic Electrical Engineering Lab | Lab | 1 | Circuit Laws Verification, AC Circuit Measurements, Transformer Characteristics, DC Motor Control, Electronic Component Testing |
| PCDS108 | C Programming for Problem Solving | Core | 3 | C Language Fundamentals, Control Structures, Functions and Pointers, Arrays and Strings, File Handling |
| DS109 | Introduction to Data Science | Core | 3 | Data Science Ecosystem, Data Collection and Preprocessing, Exploratory Data Analysis, Introduction to Machine Learning, Big Data Concepts |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSDS201 | Engineering Mathematics-II | Core | 4 | Linear Algebra, Laplace Transforms, Fourier Series, Probability and Statistics, Partial Differential Equations |
| BSDS202 | Engineering Chemistry | Core | 4 | Water Technology, Corrosion and its Control, Polymers and Composites, Fuels and Combustion, Electrochemistry and Batteries |
| ESDS203 | Basic Civil Engineering | Core | 3 | Building Materials, Surveying and Leveling, Structural Elements, Water Resource Engineering, Transportation Engineering |
| ESDS204 | Workshop Practice | Core | 2 | Carpentry, Welding, Fitting, Sheet Metal Operations, Machining Processes |
| HSDS205 | Environmental Science | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Waste Management, Renewable Energy Sources, Environmental Policies |
| BSDS206 | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis, Instrumental Analysis, Water Quality Testing, Corrosion Rate Measurement |
| PCDS207 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| PCDS208 | Object-Oriented Programming | Core | 3 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Encapsulation and Abstraction, Java/Python Basics |
| DS209 | Statistical Methods for Data Science | Core | 3 | Probability Distributions, Hypothesis Testing, Regression Analysis, ANOVA, Time Series Fundamentals |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSDS301 | Engineering Mathematics-III | Core | 4 | Complex Analysis, Vector Spaces, Optimization Techniques, Numerical Methods, Transform Techniques |
| PCDS302 | Discrete Mathematics | Core | 4 | Mathematical Logic, Set Theory and Relations, Graph Theory, Combinatorics, Recurrence Relations |
| DS303 | Database Management Systems | Core | 4 | ER Model, Relational Algebra and SQL, Normalization, Transaction Management, Concurrency Control |
| PCDS304 | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| PCDS305 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness |
| DS306 | Database Management Systems Lab | Lab | 1 | SQL Queries, Schema Design, Database Operations, PL/SQL Programming |
| PCDS307 | Operating Systems Lab | Lab | 1 | Shell Programming, Process Synchronization, Memory Allocation, File System Calls |
| DS308 | Data Visualization | Core | 3 | Principles of Data Visualization, Dashboard Design, Chart Types and Usage, Visualization Tools (e.g., Tableau, Matplotlib), Interactive Visualizations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCDS401 | Digital Logic and Computer Architecture | Core | 4 | Boolean Algebra, Combinational Circuits, Sequential Circuits, CPU Organization, Memory Hierarchy |
| PCDS402 | Principles of Programming Languages | Core | 4 | Language Paradigms, Syntax and Semantics, Data Types and Control Structures, Subprograms and Functions, Object-Oriented Features |
| DS403 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression and Classification, Clustering Algorithms, Model Evaluation and Selection, Neural Networks Introduction |
| PCDS404 | Computer Networks | Core | 4 | OSI/TCP-IP Model, Data Link Layer, Network Layer, Transport Layer, Application Layer, Network Security Basics |
| PCDS405 | Theory of Computation | Core | 4 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability |
| DS406 | Machine Learning Lab | Lab | 1 | Python for ML, Scikit-learn Implementation, Data Preprocessing, Model Training and Evaluation, Hyperparameter Tuning |
| PCDS407 | Computer Networks Lab | Lab | 1 | Network Configuration, Socket Programming, Packet Analysis (Wireshark), Routing Protocols, Network Security Tools |
| DS408 | Data Mining | Core | 3 | Data Preprocessing, Association Rule Mining, Classification Techniques, Clustering Algorithms, Outlier Detection, Web Mining |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCDS501 | Compiler Design | Core | 4 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization, Runtime Environments |
| DS502 | Artificial Intelligence | Core | 4 | Problem Solving Agents, Search Algorithms, Knowledge Representation, Logical Reasoning, Planning, AI in Robotics |
| DS503 | Big Data Analytics | Core | 4 | Big Data Concepts, Hadoop Ecosystem, HDFS and MapReduce, Apache Spark, NoSQL Databases, Data Stream Processing |
| PEDS5xx | Professional Elective – I | Elective | 3 | Specific topics depend on chosen elective |
| OEDS5xx | Open Elective – I | Elective | 3 | Specific topics depend on chosen elective |
| DS504 | Big Data Analytics Lab | Lab | 1 | Hadoop Setup and Commands, MapReduce Programming, Spark Data Processing, Hive and Pig Scripting, NoSQL Database Interaction |
| DS505 | Minor Project – I | Project | 2 | Problem Identification, Literature Survey, System Design, Implementation and Testing, Report Writing |
| HSDS506 | Universal Human Values | Core | 3 | Self-Exploration, Harmony in the Family, Harmony in Society, Harmony in Nature, Professional Ethics |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCDS601 | Software Engineering | Core | 4 | Software Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing, Software Project Management |
| DS602 | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), Deep Learning Frameworks (TensorFlow/PyTorch) |
| DS603 | Natural Language Processing | Core | 4 | Text Preprocessing, Language Models, Word Embeddings, POS Tagging and NER, Sentiment Analysis, Machine Translation |
| PEDS6xx | Professional Elective – II | Elective | 3 | Specific topics depend on chosen elective |
| OEDS6xx | Open Elective – II | Elective | 3 | Specific topics depend on chosen elective |
| DS604 | Deep Learning Lab | Lab | 1 | Neural Network Implementation, CNN for Image Classification, RNN for Sequence Data, Pre-trained Model Usage, Deep Learning Project Development |
| DS605 | Minor Project – II | Project | 2 | Advanced Problem Solving, Tool Selection, Complex System Implementation, Testing and Validation, Documentation and Presentation |
| DS606 | Industrial Training/Internship | Internship | 2 | Real-world Project Experience, Industry Best Practices, Team Collaboration, Professional Skill Development |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PEDS7xx | Professional Elective – III | Elective | 3 | Specific topics depend on chosen elective |
| PEDS7xx | Professional Elective – IV | Elective | 3 | Specific topics depend on chosen elective |
| OEDS7xx | Open Elective – III | Elective | 3 | Specific topics depend on chosen elective |
| OEDS7xx | Open Elective – IV | Elective | 3 | Specific topics depend on chosen elective |
| DS701 | Major Project – I | Project | 4 | Large Scale Problem Definition, Advanced Research Methodology, Complex System Design, Partial Implementation, Interim Report and Presentation |
| DS702 | Industrial Training | Internship | 2 | Advanced Industry Project, Problem Solving in Real-world Context, Professional Networking, Mentorship and Feedback |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PEDS8xx | Professional Elective – V | Elective | 3 | Specific topics depend on chosen elective |
| OEDS8xx | Open Elective – V | Elective | 3 | Specific topics depend on chosen elective |
| DS801 | Major Project – II | Project | 8 | Full Project Implementation, Extensive Testing and Validation, Performance Optimization, Comprehensive Documentation, Final Presentation and Viva |
