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B-SC-ARTIFICIAL-INTELLIGENCE-AND-DATA-SCIENCE in General at Datta Meghe Institute of Medical Sciences (Deemed to be University)
Wardha, Maharashtra
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About the Specialization
What is General at Datta Meghe Institute of Medical Sciences (Deemed to be University) Wardha?
This B.Sc. Artificial Intelligence and Data Science program at Datta Meghe Institute of Higher Education and Research focuses on equipping students with core competencies in AI, machine learning, deep learning, and data analytics. It addresses the rapidly growing demand for skilled professionals in the Indian technology sector, blending theoretical knowledge with practical applications. The program emphasizes ethical considerations and real-world problem-solving, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a background in Science (Physics, Chemistry, Maths/Biology/Computer Science) seeking entry into the dynamic fields of AI and Data Science. It also suits individuals passionate about problem-solving through data, keen to develop analytical and programming skills for a career in technology, and those aiming for higher studies in specialized AI/DS domains.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as Data Scientists, AI Engineers, Machine Learning Engineers, Data Analysts, or Business Intelligence Developers. Entry-level salaries typically range from INR 3-6 lakhs annually, with experienced professionals earning significantly more. The program aligns with industry needs, fostering skills for roles in IT services, finance, healthcare, and e-commerce sectors across India.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate extra time to Python and Java. Beyond classroom, practice coding challenges daily on platforms like HackerRank or LeetCode to build strong logical thinking and problem-solving skills.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, Java documentation
Career Connection
Strong programming basics are non-negotiable for AI/DS roles; early mastery leads to better internship opportunities and technical interview performance.
Build a Strong Mathematical Base- (Semester 1-2)
Reinforce concepts from Mathematics for AI and DS, focusing on linear algebra, calculus, and probability. These are the bedrock of machine learning algorithms. Use online courses or textbooks for deeper understanding.
Tools & Resources
Khan Academy, MIT OpenCourseware (Mathematics), 3Blue1Brown YouTube channel, Standard textbooks
Career Connection
A solid mathematical foundation helps in understanding and optimizing complex AI/ML models, crucial for advanced research and development roles.
Engage in Peer Learning and Projects- (Semester 1-2)
Form study groups to discuss complex topics and collaborate on small projects. Participate in college hackathons or coding clubs to apply learned concepts in a team environment, fostering practical application and teamwork.
Tools & Resources
GitHub, VS Code, Discord/Slack for team communication
Career Connection
Develops teamwork, communication, and practical application skills, all highly valued by recruiters for entry-level positions in Indian tech companies.
Intermediate Stage
Develop Practical Expertise with Tools- (Semester 3-5)
Beyond theoretical understanding, gain hands-on proficiency with essential AI/DS tools like SQL for databases, Linux for operating systems, and frameworks like scikit-learn or TensorFlow/Keras for ML/DL.
Tools & Resources
SQL Practice platforms, Linux command line tutorials, Kaggle notebooks, Google Colab, Official documentation for ML frameworks
Career Connection
Direct applicability of these tools translates to immediate productivity in internships and job roles, a major plus for Indian startups and MNCs alike.
Seek Internships and Industry Exposure- (Semester 4-5 (especially during summer breaks))
Actively search for internships (paid or unpaid) in relevant companies, even if for a short duration. Participate in industry workshops, seminars, and guest lectures organized by the department to gain real-world insights.
Tools & Resources
LinkedIn, Internshala, Company career pages, University career services
Career Connection
Internships provide invaluable real-world experience, help in building a professional network, and often lead to pre-placement offers (PPOs) in the competitive Indian job market.
Contribute to Open Source or Personal Projects- (Semester 3-5)
Start building a portfolio of personal projects on GitHub, or contribute to open-source AI/DS initiatives. Focus on solving real-world problems using learned techniques, demonstrating initiative and specialized skills.
Tools & Resources
GitHub, Kaggle, Hugging Face, Project documentation tools
Career Connection
A strong project portfolio differentiates candidates during interviews and showcases practical problem-solving abilities, highly sought after by Indian tech companies.
Advanced Stage
Intensive Placement Preparation- (Semester 6)
Focus on advanced data structures, algorithms, and system design for technical interviews. Practice mock interviews, refine resume/CV, and prepare for aptitude tests that are common in Indian campus placements for maximum success.
Tools & Resources
InterviewBit, LeetCode (Hard problems), Glassdoor, Career counseling services, Professional resume builders
Career Connection
Direct impact on securing desirable placements in top-tier companies. This stage is crucial for translating academic knowledge into a successful career launch.
Specialize through Advanced Electives and Capstone Project- (Semester 5-6)
Choose electives wisely, aligning with personal career aspirations (e.g., NLP, Computer Vision). Dedicate significant effort to the final year project, aiming for a robust solution that showcases deep technical understanding and problem-solving.
Tools & Resources
Research papers, Advanced textbooks, Specific AI/ML libraries, Project management tools
Career Connection
Specialization makes you a valuable asset for niche roles, while a strong capstone project serves as a powerful portfolio piece for both job applications and higher studies.
Build Professional Network and Personal Brand- (Semester 5-6)
Attend industry conferences, connect with professionals on LinkedIn, and potentially publish research papers or blog posts about project work. Cultivate a strong online presence demonstrating expertise to attract opportunities.
Tools & Resources
LinkedIn, Professional networking events, Technical blogs (Medium, personal website), Research publication platforms
Career Connection
A strong network can lead to referrals, mentorship, and awareness of opportunities not publicly advertised, enhancing long-term career growth in the Indian tech ecosystem.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, Biology / Maths/Computer Science/Information Practice with English and minimum of 45% aggregate marks (40% for Backward Class Category) at the qualifying examination.
Duration: 3 years (6 semesters)
Credits: 132 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-1.1 | Mathematics for AI and DS | Core | 4 | Matrices and Determinants, Differential Equations, Vector Calculus, Probability and Statistics, Discrete Mathematics |
| DSC-1.2 | Introduction to Programming using Python | Core | 4 | Python Basics, Control Flow, Functions, Data Structures (Lists, Tuples, Dictionaries), Object-Oriented Programming |
| AECC-1.1 | English and Communication Skills | Ability Enhancement Compulsory Course (AECC) | 2 | Grammar, Vocabulary, Reading Comprehension, Written Communication, Oral Communication |
| GE-1.1 | Environmental Studies | Generic Elective (GE) | 2 | Ecosystems, Biodiversity, Environmental Pollution, Natural Resources, Sustainable Development |
| Lab-1.1 | Python Programming Lab | Lab | 4 | Python data types, Control statements, Functions, List/Tuple/Dictionary operations, File handling |
| Lab-1.2 | Mathematics for AI and DS Lab | Lab | 4 | Matrix operations, Solving differential equations, Probability distributions, Statistical analysis, Basic graph theory |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-2.1 | Data Structures and Algorithms | Core | 4 | Arrays, Linked Lists, Stacks, Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms, Hashing |
| DSC-2.2 | Object Oriented Programming using Java | Core | 4 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Interfaces, Exception Handling, Collections Framework |
| AECC-2.1 | Indian Constitution | Ability Enhancement Compulsory Course (AECC) | 2 | Preamble and Basic Structure, Fundamental Rights and Duties, Directive Principles of State Policy, Union and State Legislature, Judiciary and Emergency Provisions |
| GE-2.1 | Digital Marketing | Generic Elective (GE) | 2 | Introduction to Digital Marketing, Search Engine Optimization (SEO), Search Engine Marketing (SEM), Social Media Marketing, Content Marketing |
| Lab-2.1 | Data Structures and Algorithms Lab | Lab | 4 | Implementation of arrays, Linked lists, Stacks and Queues operations, Tree traversals, Sorting and searching algorithms, Graph representation and traversal |
| Lab-2.2 | Object Oriented Programming using Java Lab | Lab | 4 | Java class and object creation, Inheritance and method overriding, Polymorphism and abstract classes, Exception handling mechanisms, GUI programming with AWT/Swing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-3.1 | Operating System Concepts | Core | 4 | OS functions and services, Process Management and CPU Scheduling, Memory Management techniques, File Systems, I/O Management and Deadlocks |
| DSC-3.2 | Database Management System | Core | 4 | Data Models (ER, Relational), Relational Algebra and Calculus, Structured Query Language (SQL), Normalization, Transaction Management |
| DSC-3.3 | Computer Network | Core | 4 | Network Topologies and Devices, OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer |
| SEC-3.1 | Web Technology | Skill Enhancement Course (SEC) | 2 | HTML and CSS Fundamentals, JavaScript Basics, Client-Server Architecture, Web Servers and Hosting, Introduction to Web Security |
| Lab-3.1 | Operating System Concepts Lab | Lab | 3 | Linux commands and Shell scripting, Process management utilities, Inter-process communication, Thread synchronization, Memory allocation strategies |
| Lab-3.2 | Database Management System Lab | Lab | 3 | DDL and DML commands in SQL, Joins, Views, and Indices, Stored Procedures and Functions, Triggers and Cursors, Database connectivity (JDBC/ODBC) |
| Lab-3.3 | Computer Network Lab | Lab | 3 | Network device configuration, TCP/IP protocol analysis, Socket programming, Routing protocols implementation, Packet sniffing using Wireshark |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-4.1 | AI Fundamentals | Core | 4 | Introduction to AI and Intelligent Agents, Uninformed Search Algorithms, Heuristic Search Algorithms (A*, Hill Climbing), Knowledge Representation and Logic, Game Playing and Adversarial Search |
| DSC-4.2 | Data Mining and Warehousing | Core | 4 | Data Preprocessing and Cleaning, Data Warehousing Concepts and OLAP, Association Rule Mining (Apriori), Classification Techniques, Clustering Algorithms, Data Visualization |
| DSC-4.3 | Machine Learning | Core | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Support Vector Machines (SVM), Decision Trees and Random Forests, Introduction to Neural Networks |
| SEC-4.1 | Soft Skills | Skill Enhancement Course (SEC) | 2 | Effective Communication Skills, Teamwork and Collaboration, Leadership and Motivation, Problem Solving and Decision Making, Time Management and Professional Etiquette |
| Lab-4.1 | AI Fundamentals Lab | Lab | 3 | Implementing search algorithms, Logic programming exercises (Prolog/Python), Constraint Satisfaction Problems, MiniMax algorithm for game playing, Knowledge representation experiments |
| Lab-4.2 | Data Mining and Warehousing Lab | Lab | 3 | ETL processes using tools, Data preprocessing with Python libraries, Implementing Association Rule Mining, Classification and Clustering algorithms, Data visualization techniques |
| Lab-4.3 | Machine Learning Lab | Lab | 3 | Implementing Linear/Logistic Regression, Support Vector Machine applications, Decision tree construction, K-Means clustering, Model evaluation metrics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-5.1 | Deep Learning | Core | 4 | Neural Network Architectures, Activation Functions and Optimizers, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs |
| DSC-5.2 | Big Data Analytics | Core | 4 | Introduction to Big Data and Hadoop Ecosystem, HDFS and MapReduce Programming, Apache Spark for Data Processing, NoSQL Databases (MongoDB, Cassandra), Data Ingestion and Stream Processing |
| DSE-5.1 | Elective-I (AI) | Discipline Specific Elective (DSE) | 4 | Advanced Natural Language Processing, Computer Vision Fundamentals, Reinforcement Learning Basics, AI Ethics and Bias, Robotics Process Automation (RPA) |
| DSE-5.2 | Elective-II (DS) | Discipline Specific Elective (DSE) | 4 | Time Series Analysis, A/B Testing and Experimentation, Data Privacy and Security, Cloud Data Platforms (AWS/Azure/GCP), Business Intelligence and Dashboards |
| Lab-5.1 | Deep Learning Lab | Lab | 3 | Implementing basic Neural Networks, Building CNNs for image classification, Developing RNNs/LSTMs for sequence data, Transfer Learning applications, Introduction to GANs |
| Lab-5.2 | Big Data Analytics Lab | Lab | 3 | Hadoop cluster setup and commands, MapReduce program implementation, Spark RDD and DataFrame operations, Hive queries and Pig scripts, NoSQL database interactions |
| PROJECT | Project | Project | 1 | Problem Identification and Scope Definition, Literature Survey and Research, System Design and Architecture, Implementation and Testing, Project Documentation and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-6.1 | Natural Language Processing | Core | 4 | Text Preprocessing (Tokenization, Stemming), N-grams and Language Models, Word Embeddings (Word2Vec, GloVe), POS Tagging and Named Entity Recognition, Sentiment Analysis and Text Classification |
| DSC-6.2 | Computer Vision | Core | 4 | Image Processing Fundamentals, Feature Detection and Extraction (SIFT, SURF), Image Segmentation and Object Detection, Image Classification with Deep Learning, Face Recognition and Gesture Recognition |
| DSE-6.1 | Elective-III (AI) | Discipline Specific Elective (DSE) | 4 | Advanced Reinforcement Learning, Explainable AI (XAI) Methods, Conversational AI and Chatbots, Generative Models (GANs, VAEs), AI in Robotics and Autonomous Systems |
| DSE-6.2 | Elective-IV (DS) | Discipline Specific Elective (DSE) | 4 | Advanced Statistical Modeling, Data Governance and Ethics, Real-time Analytics and Dashboards, Predictive Analytics Applications, Data Product Development |
| Lab-6.1 | Natural Language Processing Lab | Lab | 3 | Text tokenization and normalization, Building custom language models, Implementing sentiment analysis, Chatbot development using NLTK/SpaCy, Named entity recognition systems |
| Lab-6.2 | Computer Vision Lab | Lab | 3 | Image manipulation using OpenCV, Feature detection algorithms, Object detection with YOLO/SSD, Image segmentation techniques, Face detection and recognition projects |
| Project | Project | Project | 1 | Advanced problem formulation, End-to-end AI/DS system development, Experimentation and Evaluation, Technical Report Writing, Project Presentation and Viva |
