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B-TECH in Data Science Engineering at Indian Institute of Technology Mandi
Mandi, Himachal Pradesh
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About the Specialization
What is Data Science & Engineering at Indian Institute of Technology Mandi Mandi?
This Data Science & Engineering program at IIT Mandi focuses on equipping students with expertise in data analytics, machine learning, artificial intelligence, and big data technologies. It is highly relevant to the Indian industry, experiencing exponential growth in data-driven decision-making and digital transformation, creating high demand for skilled professionals.
Who Should Apply?
This program is ideal for fresh graduates with strong aptitude in mathematics, programming, and problem-solving, seeking entry into data science, AI, and analytics roles. It also benefits working professionals looking to upskill in cutting-edge technologies or career changers transitioning into India''''s rapidly expanding data industry.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as Data Scientists, Machine Learning Engineers, AI Specialists, and Big Data Analysts in top Indian and multinational companies. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning more, reflecting robust growth trajectories in the Indian data market.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Build a strong foundation in C/C++ and Python. Practice regularly on coding platforms to solidify logical thinking and problem-solving skills crucial for data structures and algorithms.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, Codecademy
Career Connection
Essential for cracking technical interviews and building efficient data processing scripts in any data science role.
Excel in Core Mathematics- (Semester 1-3)
Develop a deep understanding of Calculus, Linear Algebra, and Probability/Statistics. These subjects are the backbone of machine learning and data modeling.
Tools & Resources
Khan Academy, NPTEL courses on Mathematics, MIT OpenCourseware, Sheldon Ross Probability textbook
Career Connection
Crucial for understanding the theoretical underpinnings of ML algorithms and developing novel data science solutions.
Engage in Peer Learning- (Semester 1-2)
Form study groups with peers to discuss complex concepts, solve problems together, and explain topics to each other. This reinforces learning and builds collaborative skills.
Tools & Resources
WhatsApp groups, Google Meet, campus common rooms, library study areas
Career Connection
Enhances teamwork and communication skills, vital for data science projects that are typically collaborative.
Intermediate Stage
Build a Strong Data Science Portfolio- (Semester 3-5)
Apply concepts learned in Data Structures, Machine Learning, and Databases to create small, impactful data science projects. Focus on end-to-end implementation from data collection to visualization.
Tools & Resources
Kaggle, GitHub, Google Colab, Jupyter Notebooks, Pandas, NumPy, Scikit-learn, Matplotlib
Career Connection
Demonstrates practical skills to recruiters and provides talking points for interviews, increasing internship and placement chances.
Explore Industry Internships- (Summer breaks after Sem 4 and Sem 6)
Actively seek internships after your 2nd or 3rd year at startups, established tech companies, or research labs. This provides invaluable real-world experience and industry exposure.
Tools & Resources
LinkedIn, Internshala, college placement cell, company career portals
Career Connection
Converts theoretical knowledge into practical skills, builds professional networks, and often leads to pre-placement offers.
Participate in Data Science Competitions- (Semester 3-6)
Join hackathons and data science challenges on platforms like Kaggle. Compete individually or in teams to solve real-world problems under time pressure.
Tools & Resources
Kaggle, HackerEarth, DrivenData, GitHub
Career Connection
Sharpens problem-solving, analytical, and coding skills under competitive conditions, and high rankings are a strong resume booster.
Advanced Stage
Specialize and Deepen Expertise- (Semester 6-8)
Choose departmental and open electives strategically to specialize in areas like Deep Learning, NLP, Big Data, or Reinforcement Learning. Pursue advanced projects in these domains.
Tools & Resources
Advanced NPTEL courses, Coursera/edX specializations, research papers arXiv, project mentors
Career Connection
Positions you as a specialist in a high-demand niche, leading to more targeted and higher-paying roles in advanced R&D or core data science teams.
Focus on Placement Preparation- (Semester 7-8)
Dedicate time to rigorous interview preparation, including mock interviews, behavioral questions, and revising core concepts in DS/ML, algorithms, and system design. Tailor your resume for specific job roles.
Tools & Resources
InterviewBit, LeetCode premium, company-specific interview experiences, career services, alumni network
Career Connection
Maximizes chances of securing top-tier placements with desired companies and roles.
Engage in Research or Major Project- (Semester 7-8)
Undertake a significant research project or major project (Major Project Part I & II, B.Tech Thesis) under faculty guidance. Aim for a publication or a deployable solution if possible.
Tools & Resources
Research papers, academic journals, faculty advisors, institute research labs
Career Connection
Develops independent research capabilities, critical thinking, and contributes to academic or industry innovation, highly valued for advanced roles or higher studies.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, and Mathematics (PCM) and qualification in JEE Advanced examination.
Duration: 8 semesters / 4 years
Credits: 154 Credits
Assessment: Internal: Varies by course and instructor as per Institute guidelines, External: Varies by course and instructor as per Institute guidelines
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS101 | English Communication | Humanities | 2 | Communication Process, English Grammar and Usage, Reading Comprehension and Vocabulary, Writing Skills, Oral Communication |
| MA101 | Calculus | Basic Science | 4 | Differential Calculus, Integral Calculus, Sequences and Series, Functions of Several Variables, Vector Calculus |
| PH101 | Physics | Basic Science | 4 | Oscillations and Waves, Quantum Mechanics, Statistical Physics, Solid State Physics, Optics |
| PH102 | Physics Lab | Basic Science | 2 | Basic Measurements, Optics Experiments, Mechanics Experiments, Electrical Circuitry, Modern Physics Experiments |
| CS101 | Introduction to Programming | Engineering Science | 3 | Programming Fundamentals, Data Types and Operators, Control Flow, Functions, Arrays and Pointers, File I/O |
| ES101 | Engineering Graphics | Engineering Science | 2 | Engineering Drawing Fundamentals, Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Basics |
| ES102 | Workshop Practice | Engineering Science | 2 | Carpentry, Fitting, Welding, Machining, Sheet Metal Work, Foundry |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA102 | Linear Algebra and Differential Equations | Basic Science | 4 | Matrices and Determinants, Vector Spaces, Linear Transformations, Ordinary Differential Equations, Partial Differential Equations |
| CH101 | Chemistry | Basic Science | 4 | Quantum Chemistry, Chemical Bonding, Organic Chemistry, Electrochemistry, Thermodynamics |
| CH102 | Chemistry Lab | Basic Science | 2 | Volumetric Analysis, pH and Conductivity Measurements, Organic Synthesis, Electrochemistry Experiments, Spectrophotometry |
| ES103 | Basic Electrical and Electronics Engineering | Engineering Science | 3 | DC Circuits, AC Circuits, Transformers, Diodes and Transistors, Operational Amplifiers, Digital Electronics |
| CS102 | Data Structures and Algorithms | Engineering Science | 3 | Arrays and Linked Lists, Stacks and Queues, Trees, Graphs, Sorting Algorithms, Searching Algorithms |
| ES104 | Engineering Mechanics | Engineering Science | 3 | Statics of Particles, Rigid Bodies, Trusses and Frames, Friction, Dynamics of Particles, Kinematics and Kinetics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA201 | Probability and Statistics | Basic Science | 4 | Probability Axioms, Random Variables, Probability Distributions, Hypothesis Testing, Regression Analysis, Correlation |
| CS201 | Discrete Structures | Departmental Core | 3 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Combinatorics, Algebraic Structures |
| CS202 | Object Oriented Programming | Departmental Core | 3 | Classes and Objects, Inheritance, Polymorphism, Abstraction, Encapsulation, Exception Handling |
| CS203 | Computer Architecture and Organization | Departmental Core | 3 | Digital Logic, CPU Organization, Memory Hierarchy, I/O Organization, Pipelining, Instruction Set Architectures |
| DSE201 | Introduction to Data Science | Departmental Core | 3 | Data Science Lifecycle, Data Collection, Data Preprocessing, Exploratory Data Analysis, Data Visualization, Introduction to Machine Learning |
| DSE202 | Data Structures and Algorithms Lab | Departmental Core | 2 | Implementation of Arrays, Linked Lists, Stacks, Queues, Trees, Graph Algorithms |
| DSE203 | Python Programming Lab for Data Science | Departmental Core | 2 | Python Fundamentals, Data Manipulation Pandas, Numerical Computing NumPy, Data Visualization Matplotlib, Seaborn, Web Scraping |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS Elective I | HS Elective I | Humanities | 3 | Refer to list of HS Electives offered by the Humanities department., Specific topics vary based on chosen elective. |
| CS204 | Operating Systems | Departmental Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, I/O Systems |
| CS205 | Design and Analysis of Algorithms | Departmental Core | 3 | Asymptotic Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness |
| DSE204 | Database Management Systems | Departmental Core | 3 | Relational Model, SQL, ER Diagrams, Normalization, Query Processing, Transaction Management |
| DSE205 | Machine Learning | Departmental Core | 3 | Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Model Evaluation, Ensemble Methods |
| DSE206 | Data Science Lab I | Departmental Core | 2 | Data Preprocessing, Exploratory Data Analysis, Statistical Modeling, Machine Learning Model Implementation, Predictive Analytics |
| DSE207 | Database Management Systems Lab | Departmental Core | 2 | SQL Queries, Database Design, PL/SQL Programming, Data Definition Language, Data Manipulation Language |
| DSE208 | Machine Learning Lab | Departmental Core | 2 | Implementing ML Algorithms Scikit-learn, Data Loading and Preprocessing, Feature Engineering, Model Training and Evaluation, Hyperparameter Tuning |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS Elective II | HS Elective II | Humanities | 3 | Refer to list of HS Electives offered by the Humanities department., Specific topics vary based on chosen elective. |
| DSE301 | Deep Learning | Departmental Core | 3 | Neural Networks, Backpropagation, Convolutional Neural Networks CNNs, Recurrent Neural Networks RNNs, Generative Adversarial Networks GANs, Deep Learning Frameworks TensorFlow/PyTorch |
| DSE302 | Big Data Analytics | Departmental Core | 3 | Big Data Ecosystem, Hadoop, MapReduce, Spark, NoSQL Databases, Stream Processing |
| DSE303 | Artificial Intelligence | Departmental Core | 3 | Problem Solving by Search, Knowledge Representation, Logic Programming, Planning, Uncertainty and Reasoning, Machine Learning in AI |
| DSE304 | Data Science Lab II | Departmental Core | 2 | Advanced Data Manipulation, Feature Selection, Model Deployment, A/B Testing, Time Series Analysis |
| DSE305 | Deep Learning Lab | Departmental Core | 2 | Implementing CNNs, RNNs, LSTMs, Transfer Learning, Image Classification, Natural Language Generation |
| Departmental Elective I | Departmental Elective I | Departmental Elective | 3 | Refer to list of Departmental Electives offered by the DSE department., Specific topics vary based on chosen elective. |
| Open Elective I | Open Elective I | Open Elective | 3 | Refer to list of Open Electives offered by various departments., Specific topics vary based on chosen elective. |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSE306 | Natural Language Processing | Departmental Core | 3 | Text Preprocessing, Word Embeddings, Language Models, Text Classification, Sequence Labeling, Machine Translation |
| DSE307 | Cloud Computing for Data Science | Departmental Core | 3 | Cloud Service Models IaaS, PaaS, SaaS, Cloud Deployment Models, Virtualization, Cloud Storage, Distributed Computing on Cloud, Cloud Security |
| DSE308 | Data Visualization | Departmental Core | 3 | Principles of Visualization, Visual Perception, Static and Interactive Visualization, Storytelling with Data, Visualization Tools Tableau, Power BI, D3.js |
| DSE309 | NLP Lab | Departmental Core | 2 | Text Preprocessing with NLTK/SpaCy, Implementing Word2Vec, Sentiment Analysis, Named Entity Recognition, Chatbot Development |
| DSE310 | Cloud Computing Lab for Data Science | Departmental Core | 2 | Deploying Applications on AWS/Azure/GCP, Setting up Cloud Databases, Distributed Data Processing on Cloud, Containerization Docker |
| Departmental Elective II | Departmental Elective II | Departmental Elective | 3 | Refer to list of Departmental Electives offered by the DSE department., Specific topics vary based on chosen elective. |
| Open Elective II | Open Elective II | Open Elective | 3 | Refer to list of Open Electives offered by various departments., Specific topics vary based on chosen elective. |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| Departmental Elective III | Departmental Elective III | Departmental Elective | 3 | Refer to list of Departmental Electives offered by the DSE department., Specific topics vary based on chosen elective. |
| Departmental Elective IV | Departmental Elective IV | Departmental Elective | 3 | Refer to list of Departmental Electives offered by the DSE department., Specific topics vary based on chosen elective. |
| Open Elective III | Open Elective III | Open Elective | 3 | Refer to list of Open Electives offered by various departments., Specific topics vary based on chosen elective. |
| DSE401 | Major Project Part I | Project Work | 3 | Problem Identification, Literature Review, Methodology Design, Data Collection, Project Planning, Interim Report |
| DSE402 | Data Science Internship | Project Work | 3 | Industry Problem Solving, Practical Skill Application, Report Writing, Professional Communication, Teamwork |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| Departmental Elective V | Departmental Elective V | Departmental Elective | 3 | Refer to list of Departmental Electives offered by the DSE department., Specific topics vary based on chosen elective. |
| Open Elective IV | Open Elective IV | Open Elective | 3 | Refer to list of Open Electives offered by various departments., Specific topics vary based on chosen elective. |
| DSE403 | Major Project Part II | Project Work | 6 | System Implementation, Experimentation, Performance Evaluation, Results Analysis, Thesis Writing, Project Defense |
| DSE404 | B.Tech Thesis / Project Report | Project Work | 6 | Research Methodology, Data Analysis, Scientific Writing, Presentation Skills, Innovation and Contribution, Ethical Considerations |
