Data ScienceSemester 4-1 syllabus

The final taught semester combines Big Data Analytics as the last major core subject with a human-resources/project-management course, two professional elective slots spanning topics like blockchain, DevOps, NLP, agile methods, and high-performance computing, and two open electives taken from other departments. A second full-stack development course and a Constitution of India audit course round out the term, alongside evaluation of the prior summer’s industry internship or mini-project.

Semester load: roughly 19 lecture, 1 tutorial and 2 practical hours per week, totaling 21 credits.

Subjects

Big Data Analytics

• Unit 1: Java data structures and generics needed for MapReduce-style programming
• Unit 2: Hadoop Distributed File System architecture and cluster configuration
• Unit 3: Writing MapReduce programs — mappers, reducers, and combiners
• Unit 4: Stream processing concepts and Spark’s RDD-based architecture
• Unit 5: Pig and Hive for higher-level querying over Hadoop data

Human Resources & Project Management (Management Course-II)

• Unit 1: HRM functions, planning, recruitment, and selection
• Unit 2: Training, performance appraisal, and career development
• Unit 3: Project management basics, resource planning, and project life cycle
• Unit 4: Managing different project types and their unique challenges
• Unit 5: Project implementation, control, and post-project review

Professional Elective-IV options:

Software Architecture & Design Patterns, Blockchain Technology, DevOps, Natural Language Processing, or an approved NPTEL/SWAYAM course.

Software Architecture & Design Patterns

• Unit 1: What design patterns are and how object-oriented design approaches them
• Unit 2: Systems analysis — gathering and structuring requirements
• Unit 3: The structural design-pattern catalog (adapter, bridge, composite, decorator, etc.)
• Unit 4: The MVC architectural pattern in practice
• Unit 5: Distributed-object design, including web services

Blockchain Technology

• Unit 1: Origins of Bitcoin, blockchain fundamentals, and cryptographic building blocks
• Unit 2: Underlying technologies — hash pointers, wallets, mining, and double-spending
• Unit 3: Consensus mechanisms — proof of work, proof of stake, and hybrid models
• Unit 4: Ethereum, smart contracts, and Solidity
• Unit 5: Hyperledger Fabric and broader blockchain applications beyond cryptocurrency

DevOps

• Unit 1: DevOps lifecycle, workflows, and CI/CD automation concepts
• Unit 2: Source-code management with Git, plus unit-testing and code-coverage tools
• Unit 3: Continuous integration with Jenkins
• Unit 4: Continuous delivery and containerization with Docker
• Unit 5: Configuration management with Ansible and container orchestration with Kubernetes

Natural Language Processing

• Unit 1: Language modeling basics, morphology, and tokenization
• Unit 2: N-grams, part-of-speech tagging, and statistical language models
• Unit 3: Syntactic parsing and context-free grammars
• Unit 4: Semantics, word-sense disambiguation, and pragmatics
• Unit 5: Discourse analysis, coreference resolution, and standard NLP lexical resources

Professional Elective-V options:

Agile Methodologies, Expert Systems, Reinforcement Learning, High Performance Computing, or an approved NPTEL/SWAYAM course.

Agile Methodologies

• Unit 1: Agile theory, the manifesto, and agile project management
• Unit 2: Agile process families — Scrum, Crystal, XP, and feature-driven development
• Unit 3: Knowledge-sharing practices such as story cards
• Unit 4: Requirements engineering in agile environments
• Unit 5: Agile metrics, quality assurance, and test-driven development

Expert Systems

• Unit 1: AI search strategies and game-playing algorithms
• Unit 2: Knowledge representation — predicate logic, semantic nets, and rule-based systems
• Unit 3: Expert system architecture and problem types
• Unit 4: Expert-system development tools and knowledge engineering
• Unit 5: Building an expert system and common pitfalls in practice

Reinforcement Learning

• Unit 1: Core reinforcement-learning concepts and terminology
• Unit 2: The multi-armed bandit problem and action-value methods
• Unit 3: Finite Markov decision processes and value functions
• Unit 4: Monte Carlo prediction and control methods
• Unit 5: Applied case studies such as TD-Gammon and job-shop scheduling

High Performance Computing

• Unit 1: Motivations for parallelism and parallel programming platforms
• Unit 2–5: Parallel algorithm design, interconnection networks, performance analysis, and techniques for parallelizing computational tasks

Open Elective-III and Open Elective-IV:

cross-department electives; Data Science students typically draw from subjects such as Operating Systems, Computer Networks, Software Engineering, or IoT Based Smart Systems as offered.

Full Stack Development-2 (Skill Enhancement Course)

• ExpressJS routing, middleware, sessions, and RESTful API design
• ReactJS components, props/state, conditional rendering, and hooks
• MongoDB installation, CRUD operations, and aggregation queries
• A capstone build such as a to-do list or quiz application
• Total: 1 tutorial and 2 practical hours per week, 2 credits

Constitution of India (Audit Course)

• Unit 1: History and drafting of the Indian Constitution
• Unit 2: Fundamental rights, directive principles, and fundamental duties
• Unit 3: Structure of the legislature, executive, and judiciary
• Unit 4: Local self-government — municipalities and panchayati raj institutions
• Unit 5: The Election Commission and welfare bodies for marginalized groups
• Ungraded audit course; no credits attached

Note: this semester includes evaluation of the Industry Internship or Mini Project completed the previous summer.