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B-TECH in Artificial Intelligence Data Science at Dr. D. Y. Patil Vidyapeeth, Pune
Pune, Maharashtra
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About the Specialization
What is Artificial Intelligence & Data Science at Dr. D. Y. Patil Vidyapeeth, Pune Pune?
This Artificial Intelligence & Data Science program at Dr. D. Y. Patil Vidyapeeth focuses on equipping students with expertise in intelligent systems design and data-driven decision-making. Catering to the burgeoning Indian tech industry, the program integrates core AI principles with advanced data science techniques, preparing graduates for cutting-edge roles in this rapidly evolving domain. Its holistic approach balances theoretical knowledge with practical applications.
Who Should Apply?
This program is ideal for ambitious fresh graduates seeking entry into the high-demand fields of AI, machine learning, and data analytics. It also benefits working professionals aiming to upskill and leverage AI/DS in their careers, as well as career changers from related engineering backgrounds transitioning into the AI/DS industry, provided they possess a strong foundation in mathematics and programming.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Scientist, AI Engineer, Machine Learning Engineer, Business Intelligence Analyst, and Big Data Engineer. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning upwards of INR 15-30 LPA. Growth trajectories are steep, often aligning with professional certifications in cloud AI or specific ML frameworks.
Student Success Practices
Foundation Stage
Master Core Programming & Math Fundamentals- (Semester 1-2)
Dedicate significant time in Semesters 1 and 2 to build an unshakeable foundation in C/Java programming, data structures, algorithms, and applied mathematics. Actively solve problems on platforms like HackerRank, CodeChef, and LeetCode to solidify logical thinking and coding proficiency.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Khan Academy for math concepts
Career Connection
A strong foundation is crucial for cracking technical interviews and understanding advanced AI/DS concepts, directly impacting internship and placement opportunities.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with peers to discuss challenging concepts, collaborate on assignments, and teach each other. Explaining concepts to others reinforces your own understanding and exposes you to different problem-solving approaches, enhancing collective learning.
Tools & Resources
College library, Dedicated study rooms, WhatsApp/Discord groups for discussions
Career Connection
Develops teamwork and communication skills, highly valued in corporate environments, and helps build a supportive network for future career challenges.
Explore Basic AI/DS Concepts Early- (Semester 1-2)
Beyond classroom curriculum, watch introductory online courses or read articles on basic AI and Data Science concepts. Understand what the field entails and start familiarizing yourself with Python fundamentals, which will be critical in later semesters.
Tools & Resources
Coursera (free courses), NPTEL (IIT lectures), YouTube channels (e.g., freeCodeCamp, Krish Naik)
Career Connection
Provides a head start and helps clarify career interests, making subsequent specialization choices more informed and focused towards high-growth areas in India.
Intermediate Stage
Build Projects & Participate in Hackathons- (Semester 3-5)
Actively apply learned concepts by building mini-projects in Python for machine learning, databases, or web development. Participate in college-level or national hackathons (e.g., Smart India Hackathon) to gain practical experience, develop problem-solving skills, and network with industry professionals.
Tools & Resources
GitHub for version control, Kaggle for datasets, Google Colab, IDE like PyCharm/VS Code
Career Connection
Project portfolios are critical for showcasing skills to recruiters, significantly boosting internship and job prospects in AI/DS roles.
Seek Early Industry Exposure & Mentorship- (Semester 3-5)
Look for summer internships or industrial training opportunities, even if unpaid, to understand industry workflows. Connect with alumni and industry professionals on LinkedIn to gain insights, seek mentorship, and understand current industry trends and skill requirements in India.
Tools & Resources
LinkedIn, Internshala, College career cell
Career Connection
Provides valuable real-world experience, clarifies career goals, and often leads to pre-placement offers or strong recommendations, accelerating career entry.
Specialize in a Niche & Certify Skills- (Semester 3-5)
As you grasp core AI/ML, choose a sub-field (e.g., NLP, Computer Vision, Reinforcement Learning) and delve deeper. Consider pursuing online certifications from platforms like Coursera, edX, or NPTEL in specific technologies (e.g., TensorFlow Developer, AWS Machine Learning Specialty).
Tools & Resources
Coursera, edX, NPTEL, Google AI/ML certifications, AWS ML certifications
Career Connection
Demonstrates specialized knowledge and commitment, making you a more attractive candidate for specialized AI/DS roles and potentially higher starting salaries in the competitive Indian market.
Advanced Stage
Focus on Placement-Oriented Preparation- (Semester 6-8)
In Semesters 6-8, intensify preparation for placements. Practice advanced data structures and algorithms, review core AI/ML concepts, and work on behavioral interview skills. Participate in mock interviews and group discussions organized by the college''''s placement cell.
Tools & Resources
LeetCode (Hard problems), Glassdoor for interview experiences, College placement cell resources
Career Connection
Directly impacts success in campus placements, leading to securing desirable job offers from top Indian and multinational companies.
Undertake a Capstone Project or Research- (Semester 6-8)
For your final year project (Phase I & II), choose a challenging real-world problem, ideally with an industry partner, and apply advanced AI/DS techniques. If interested in academia, collaborate with faculty on research papers for national/international conferences.
Tools & Resources
Research papers (arXiv, IEEE, ACM), Open-source AI frameworks, Industry problem statements
Career Connection
A significant capstone project or publication enhances your resume, showcases deep expertise, and provides excellent talking points in interviews for specialized roles or higher studies.
Network and Stay Updated with Industry- (Semester 6-8)
Continuously engage with the AI/DS community through tech talks, webinars, and conferences (e.g., India AI Conclave, Data Science Summit). Build a professional network on LinkedIn and follow leading researchers and companies to stay abreast of the latest advancements and job market trends in India.
Tools & Resources
LinkedIn, Medium blogs for AI/DS, Tech event platforms, Industry newsletters
Career Connection
Opens doors to unforeseen opportunities, helps in career transitions, and ensures your skills remain relevant in a rapidly evolving technological landscape, crucial for long-term career growth.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject/Computer Science/Information Technology/Informatics Practices/Agriculture/Engineering Graphics/Business Studies. Obtained at least 45% marks (40% in case of candidates belonging to reserved category) in the above subjects taken together.
Duration: 4 years / 8 semesters
Credits: 182 Credits
Assessment: Internal: 40% (for theory subjects), 50% (for practical/oral subjects), External: 60% (for theory subjects), 50% (for practical/oral subjects)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BM101 | Applied Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Matrices, Differential Equations, Vector Calculus |
| BP101 | Applied Physics | Core | 3 | Quantum Physics, Lasers and Fiber Optics, Semiconductors, Wave Optics, Nanotechnology |
| BE101 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines |
| CS101 | Programming and Problem Solving | Core | 3 | Introduction to C Programming, Control Structures, Arrays and Strings, Functions and Pointers, Structures and File Handling |
| ES101 | Environmental Engineering | Core | 3 | Environmental Pollution, Ecosystems, Natural Resources, Waste Management, Environmental Legislation |
| EG101 | Engineering Graphics | Lab | 1 | Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Basics, Dimensioning |
| BP101L | Applied Physics Laboratory | Lab | 1 | Experimental Physics, Measurement Techniques, Data Analysis, Optics Experiments, Semiconductor Device Characteristics |
| BE101L | Basic Electrical Engineering Laboratory | Lab | 1 | Circuit Laws Verification, AC Circuit Analysis, Transformer Testing, DC Machine Characteristics, Power Measurement |
| CS101L | Programming and Problem Solving Laboratory | Lab | 1 | C Programming Exercises, Conditional Statements, Looping Constructs, Functions Implementation, Array and String Manipulation |
| WP101 | Workshop Practice | Lab | 1 | Carpentry, Fitting, Welding, Sheet Metal Work, Foundry |
| PS101 | Professional Skills | Lab | 1 | Communication Skills, Presentation Skills, Teamwork, Professional Etiquette, Report Writing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BM102 | Applied Mathematics-II | Core | 4 | Laplace Transforms, Fourier Series, Partial Differential Equations, Probability and Statistics, Complex Numbers |
| BC101 | Applied Chemistry | Core | 3 | Water Technology, Corrosion and its Control, Polymer Chemistry, Fuels and Combustion, Electrochemistry |
| EC101 | Basic Electronics Engineering | Core | 3 | Semiconductor Diodes, Transistors (BJT, FET), Rectifiers and Filters, Operational Amplifiers, Digital Logic Gates |
| CS102 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees (Binary, BST, AVL), Graphs (Traversal, Shortest Path), Sorting and Searching Algorithms |
| EM101 | Engineering Mechanics | Core | 3 | Forces and Moments, Equilibrium, Friction, Kinematics of Particles, Work and Energy |
| BC101L | Applied Chemistry Laboratory | Lab | 1 | Titration Experiments, Instrumental Analysis, Water Quality Testing, Polymer Synthesis, Corrosion Rate Measurement |
| EC101L | Basic Electronics Engineering Laboratory | Lab | 1 | Diode Characteristics, Transistor Amplifier Circuits, Rectifier Design, Op-Amp Applications, Logic Gate Implementation |
| CS102L | Data Structures Laboratory | Lab | 1 | Linked List Operations, Stack and Queue Implementation, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Programs |
| CS103L | Object Oriented Programming | Lab | 1 | Classes and Objects, Inheritance, Polymorphism, Abstraction, Exception Handling |
| AC101 | Audit Course 1 | Audit Course | 0 | Indian Constitution, Environmental Science, Disaster Management, Essence of Indian Traditional Knowledge |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS301 | Discrete Structures | Core | 3 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Recurrence Relations |
| CS302 | Data Structures and Algorithms | Core | 3 | Advanced Data Structures, Algorithm Analysis, Tree and Graph Algorithms, Hashing Techniques, Algorithm Design Paradigms |
| CS303 | Database Management System | Core | 3 | Database Architecture, ER Model, Relational Algebra and Calculus, SQL and PL/SQL, Normalization and Transaction Management |
| CS304 | Computer Organization and Architecture | Core | 3 | Processor Organization, Memory Hierarchy, I/O Organization, Control Unit Design, Pipelining and Parallel Processing |
| EC301 | Digital Electronics and Microprocessor | Core | 3 | Logic Families, Combinational Circuits, Sequential Circuits, Microprocessor Architecture (e.g., 8086), Assembly Language Programming |
| CS302L | Data Structures and Algorithms Laboratory | Lab | 1.5 | Advanced Tree Implementations, Graph Algorithm Applications, Dynamic Programming Problems, Hashing based Data Structures, Algorithm Efficiency Analysis |
| CS303L | Database Management System Laboratory | Lab | 1.5 | SQL Queries, Database Schema Design, PL/SQL Programming, Transaction Control, Database Connectivity |
| EC301L | Digital Electronics Laboratory | Lab | 1 | Logic Gate Experiments, Flip-Flops and Counters, Multiplexers and Demultiplexers, Microprocessor Interfacing, Assembly Language Exercises |
| CS305L | Object Oriented Programming using JAVA | Lab | 2 | Java Fundamentals, Classes, Objects, Inheritance, Interfaces and Packages, Multithreading and Exception Handling, GUI Programming (Swing/AWT) |
| MP301 | Mini Project I | Project | 1 | Problem Identification, System Design, Implementation, Testing and Debugging, Project Reporting |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIAD401 | Applied Mathematics-III for AI & DS | Core | 3 | Linear Algebra, Probability Distributions, Statistical Inference, Optimization Techniques, Numerical Methods |
| CS401 | Operating System | Core | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks and Synchronization |
| CS402 | Design and Analysis of Algorithms | Core | 3 | Algorithm Complexity, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms and NP-Completeness |
| AIAD402 | Machine Learning Fundamentals | Core | 3 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Model Evaluation and Selection, Ensemble Methods and Neural Networks Basics |
| AIAD403 | Python Programming for AI & DS | Core | 3 | Python Basics, Data Structures in Python, NumPy and Pandas, Data Visualization (Matplotlib, Seaborn), Introduction to Scikit-learn |
| CS401L | Operating System Laboratory | Lab | 1.5 | Shell Scripting, Process Creation and Management, CPU Scheduling Algorithms, Memory Allocation Strategies, Synchronization Problems |
| CS402L | Design and Analysis of Algorithms Laboratory | Lab | 1.5 | Sorting Algorithm Implementations, Graph Traversal and Pathfinding, Dynamic Programming Problems, Greedy Algorithm Solutions, Time and Space Complexity Analysis |
| AIAD402L | Machine Learning Laboratory | Lab | 1.5 | Implementing Regression Models, Classification Algorithms, Clustering Techniques, Feature Engineering, Model Hyperparameter Tuning |
| AIAD403L | Python Programming Laboratory | Lab | 1.5 | NumPy Array Operations, Pandas Data Manipulation, Data Visualization with Matplotlib, Basic ML Model Implementation in Python, Web Scraping Basics |
| AC401 | Audit Course II | Audit Course | 0 | Universal Human Values, Soft Skills Development, Stress Management, Entrepreneurship Development |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIAD501 | Artificial Intelligence | Core | 3 | Problem Solving Agents, Heuristic Search, Knowledge Representation (Logic, Rules), Uncertainty and Probabilistic Reasoning, Machine Learning Overview |
| AIAD502 | Deep Learning | Core | 3 | Neural Network Fundamentals, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| CS501 | Cloud Computing | Core | 3 | Cloud Computing Architecture, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security and Management |
| AIAD503 | Data Visualization | Core | 3 | Principles of Visual Perception, Types of Visualizations (Charts, Graphs), Tools for Data Visualization (Tableau, PowerBI), Interactive Visualizations, Storytelling with Data |
| AIAD504 | Elective I | Elective | 3 | Computer Graphics, High Performance Computing, Digital Image Processing |
| AIAD501L | Artificial Intelligence Laboratory | Lab | 1.5 | Search Algorithms Implementation, Logic Programming (Prolog), Expert Systems Development, Game Playing Algorithms, Constraint Satisfaction Problems |
| AIAD502L | Deep Learning Laboratory | Lab | 1.5 | Building Simple Neural Networks, Image Classification with CNNs, Sequence Modeling with RNNs, Transfer Learning Techniques, Hyperparameter Optimization |
| CS501L | Cloud Computing Laboratory | Lab | 1.5 | AWS/Azure/GCP Basics, Virtual Machine Deployment, Containerization (Docker), Serverless Computing, Cloud Storage Services |
| MP501 | Mini Project II | Project | 2 | Advanced Project Planning, Literature Review, System Development Life Cycle, Module Integration, Testing and Validation |
| OE501 | Open Elective I | Open Elective | 3 | Various Interdisciplinary Subjects |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIAD601 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases, Big Data Security and Governance |
| AIAD602 | Natural Language Processing | Core | 3 | Text Preprocessing, Language Models, Word Embeddings, Sequence Models (RNNs, Transformers), Applications (Sentiment Analysis, Machine Translation) |
| AIAD603 | Data Warehousing & Mining | Core | 3 | Data Warehouse Architecture, OLAP Operations, Data Preprocessing, Association Rule Mining, Classification and Clustering Techniques |
| CS601 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Routing Protocols, Transport Layer Protocols, Network Security Basics |
| AIAD604 | Elective II | Elective | 3 | Internet of Things, Reinforcement Learning, Generative AI |
| AIAD601L | Big Data Analytics Laboratory | Lab | 1.5 | Hadoop Installation and Configuration, MapReduce Programming, Spark Data Processing, Hive/Pig Scripting, NoSQL Database Operations (MongoDB/Cassandra) |
| AIAD602L | Natural Language Processing Laboratory | Lab | 1.5 | Text Preprocessing with NLTK, Sentiment Analysis Implementation, Named Entity Recognition, Text Summarization, Chatbot Development |
| AIAD603L | Data Warehousing & Mining Laboratory | Lab | 1.5 | ETL Process Implementation, OLAP Cube Creation, Association Rule Mining, Classification Algorithm Practice, Clustering Analysis |
| HS601 | Professional Ethics and Cyber Security | Core | 2 | Ethical Theories, Cyber Ethics, Cybercrime, Data Privacy and Security Laws, Intellectual Property Rights |
| OE601 | Open Elective II | Open Elective | 3 | Various Interdisciplinary Subjects |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIAD701 | Information Retrieval | Core | 3 | Boolean and Vector Space Models, Ranking Algorithms (TF-IDF, PageRank), Web Search Engines, Relevance Feedback, Evaluation Metrics |
| AIAD702 | Ethical Hacking and Cyber Security | Core | 3 | Introduction to Ethical Hacking, Network Scanning and Enumeration, System Hacking, Web Application Security, Malware Analysis and Countermeasures |
| AIAD703 | Elective III | Elective | 3 | Data Stream Processing, Business Intelligence, Quantum Computing |
| AIAD704 | Elective IV | Elective | 3 | Computer Vision, Robotics and Automation, Blockchain Technology |
| PR701 | Project Phase I | Project | 6 | Feasibility Study, Detailed Design and Architecture, Prototype Development, Requirement Analysis, Project Documentation |
| IT701 | Internship/Industrial Training | Internship | 4 | Industry Exposure, Real-world Project Experience, Professional Skill Enhancement, Networking, Report Writing |
| OE701 | Open Elective III | Open Elective | 3 | Various Interdisciplinary Subjects |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS801 | Distributed Computing | Core | 3 | Distributed Systems Concepts, Remote Procedure Calls, Distributed File Systems, Consistency and Replication, Fault Tolerance |
| CS802 | Software Engineering | Core | 3 | Software Development Life Cycle (SDLC), Requirement Engineering, Software Design Principles, Software Testing Techniques, Project Management and Quality Assurance |
| AIAD801 | Elective V | Elective | 3 | Social Network Analysis, Human Computer Interaction, Virtual and Augmented Reality |
| AIAD802 | Elective VI | Elective | 3 | Recommender Systems, Game Theory, Explainable AI |
| PR801 | Project Phase II | Project | 6 | Implementation and Integration, Testing and Debugging, Performance Evaluation, Final Documentation and Report, Presentation and Demonstration |
| TS801 | Technical Seminar | Seminar | 2 | Research Skill Development, Technical Paper Presentation, Review of Latest Technologies, Public Speaking, Question and Answer Session Management |
| OE801 | Open Elective IV | Open Elective | 3 | Various Interdisciplinary Subjects |
