Course Structure
| # | Category | Subject | L-T-P | Credits |
|---|---|---|---|---|
| 1 | Professional Core | Compiler Design | 3-0-0 | 3 |
| 2 | Professional Core | Cloud Computing | 3-0-0 | 3 |
| 3 | Professional Core | Cryptography & Network Security | 3-0-0 | 3 |
| 4 | Professional Elective II | Software Testing Methodologies / Cyber Security / DevOps / Machine Learning / NPTEL-Swayam MOOC | 3-0-0 | 3 |
| 5 | Professional Elective III | Software Project Management / Mobile Adhoc Networks / Natural Language Processing / Big Data Analytics / Distributed Operating System / NPTEL-Swayam MOOC | 3-0-0 | 3 |
| 6 | Open Elective | Open Elective – II | 3-0-0 | 3 |
| 7 | Professional Core (Lab) | Cloud Computing Lab | 0-0-3 | 1.5 |
| 8 | Professional Core (Lab) | Cryptography & Network Security Lab | 0-0-3 | 1.5 |
| 9 | Skill Enhancement Course | Soft Skills / SWAYAM Plus – 21st Century Employability Skills | 0-1-2 | 2 |
| 10 | Audit Course | Technical Paper Writing & IPR | 2-0-0 | — |
Total: 20-1-08, 23 credits.
Students also complete a mandatory 8-week industry internship or mini-project during the summer vacation between III and IV year — it doesn’t carry a semester credit here but is a non-negotiable graduation requirement.
Note on electives: Professional Elective II and III are each a single 3-credit slot where a student picks one subject from the listed options (or an approved 12-week NPTEL/SWAYAM MOOC). Below, every option is covered individually since different colleges offer different choices. Open Elective II is drawn from a rotating cross-department pool defined outside the CSE syllabus book, so its actual content depends on which department’s elective a student selects that semester.
Professional Core Subjects
Compiler Design
— the course that explains what actually happens between writing code and a running program, and a frequent source of systems-interview questions.
- Unit 1: Lexical analysis fundamentals — how source code is tokenized, the finite-automata and regular-expression theory behind scanner generators like LEX, plus an introduction to context-free grammars and parse trees.
- Unit 2: Parsing techniques — top-down methods including recursive-descent and LL(1) parsing, and bottom-up methods covering shift-reduce, SLR, CLR, and LALR parsing-table construction.
- Unit 3: Syntax-directed translation — attaching semantic rules to grammar productions and generating intermediate representations such as three-address code, syntax trees, and backpatched jumps.
- Unit 4: Code optimization — basic-block analysis, flow graphs, data-flow analysis, and loop and peephole optimizations that make generated code more efficient.
- Unit 5: Runtime environments and code generation — activation records, procedure-call mechanics, and the design choices behind object-code generation and register allocation.
Cloud Computing
— a core paper for students aiming at infrastructure, platform, or DevOps-adjacent roles, covering how modern applications get deployed and scaled without owning physical servers.
- Unit 1: Cloud fundamentals — service models (IaaS/PaaS/SaaS), deployment models, and an overview of major providers such as AWS, Azure, and Google App Engine.
- Unit 2: Enabling technologies — parallel and distributed computing foundations, remote procedure calls, service-oriented architecture, and the virtualization concepts that make cloud computing work.
- Unit 3: Virtualization and containers — hypervisor-based virtualization versus containerization, Docker fundamentals, and orchestration with Kubernetes and Docker Swarm.
- Unit 4: Cloud challenges — interoperability, scalability, fault tolerance, energy efficiency, and the shared-responsibility security model across deployment types.
- Unit 5: Advanced cloud paradigms — serverless/Function-as-a-Service computing, cloud-centric IoT, edge and fog computing, and how DevOps and infrastructure-as-code fit into the cloud stack.
Cryptography & Network Security
(Common to CSE, CS & IT) — the paper that explains how the internet keeps secrets, from block ciphers to the protocols securing an ordinary browser session.
- Unit 1: Security foundations — attack classifications, security services, and the modular-arithmetic and matrix math that cryptographic algorithms depend on.
- Unit 2: Symmetric-key cryptography — algebraic structures behind block ciphers, and the design and security analysis of DES and AES.
- Unit 3: Asymmetric-key cryptography — primality testing and factorization, and public-key systems including RSA, Rabin, ElGamal, and elliptic-curve cryptography.
- Unit 4: Data integrity and key management — hash functions, message authentication, digital signature schemes, and symmetric key distribution via Kerberos.
- Unit 5: Network security protocols — PGP and S/MIME at the application layer, SSL/TLS at the transport layer, IPSec at the network layer, and an introduction to firewalls, intrusion detection, and malware.
Professional Elective II (choose one)
Software Testing Methodologies
(Common to CSE, CS, IT, CSD, CSE-AI, CSE-AI&ML, CSE-AI&DS) — the theory backbone behind QA engineering and test-automation careers.
- Unit 1: Testing fundamentals — why bugs occur, testing models, and path testing built around flow graphs and path predicates.
- Unit 2: Transaction-flow and data-flow testing — techniques centered on how data and control move through a program, along with domain testing.
- Unit 3: Path expressions and logic-based testing — regular expressions for flow-anomaly detection, decision tables, and KV charts.
- Unit 4: State-based testing — modeling software as state graphs and designing test cases around state transitions.
- Unit 5: Graph matrices — matrix representations of program graphs used to drive systematic test design, with hands-on exposure to a tool such as JMeter, Selenium, SoapUI, or Katalon.
Cyber Security
— a practical, law-and-forensics-facing companion to the cryptography paper, aimed at students headed toward security operations or digital forensics.
- Unit 1: Cybercrime landscape — definitions, attacker categories, mobile and wireless attack surfaces, and botnets.
- Unit 2: Attack tools and techniques — phishing, keyloggers, malware, sniffing and spoofing, session hijacking, DoS/DDoS, SQL injection, and social engineering.
- Unit 3: Cybercrime investigation — evidence collection and preservation, email tracing, and password-recovery case studies.
- Unit 4: Computer forensics — evaluating forensic tools, OS-specific forensics on Windows and Linux, and biometric/multimedia forensic analysis.
- Unit 5: Legal perspectives — the Indian IT Act, comparative global cyber law, and digital-signature regulation.
DevOps
(Common to CSE, CS, IT, AI&ML, CSE-AI, CSE-AI&ML) — the toolchain course behind modern CI/CD pipelines and release engineering.
- Unit 1: DevOps foundations — SDLC and Agile background, the DevOps lifecycle, and release practices like Scrum and Kanban.
- Unit 2: Source control and code quality — Git workflows and branching, plus unit testing and code-coverage analysis with SonarQube.
- Unit 3: Continuous integration — Jenkins architecture, pipelines, and master-slave build automation.
- Unit 4: Continuous delivery and containerization — CD workflow concepts, Docker fundamentals, and browser-test automation with Selenium.
- Unit 5: Configuration management and orchestration — Ansible playbooks and roles, plus container orchestration with Kubernetes and OpenShift.
Machine Learning
— the theory course underneath most ML-adjacent CSE career paths, building the statistical intuition that libraries like scikit-learn abstract away.
- Unit 1: ML foundations — learning paradigms, data representation, feature engineering, and the general model-building pipeline.
- Unit 2: Nearest-neighbor models — distance and similarity measures, KNN classification and regression, and classifier performance evaluation.
- Unit 3: Decision trees and Bayesian methods — impurity measures, random forests, and the Naive Bayes classifier.
- Unit 4: Linear discriminants — perceptrons, support vector machines, kernel methods, logistic and linear regression, and multi-layer perceptrons with backpropagation.
- Unit 5: Clustering — partitional and hierarchical clustering, K-means, fuzzy C-means, and spectral clustering.
(A 12-week NPTEL/SWAYAM MOOC recommended by the Board of Studies may substitute for a classroom subject in this slot.)
Professional Elective III (choose one)
Software Project Management
— for students who want the delivery-management side of software engineering, not just the code.
- Unit 1: Conventional versus modern management — the waterfall model, software economics, and the shift toward iterative development.
- Unit 2: Project life cycle — inception, elaboration, construction, and transition phases, and the artifacts produced at each stage.
- Unit 3: Process planning — architecture-centric planning perspectives, workflow checkpoints, work-breakdown structures, and cost/schedule estimation.
- Unit 4: Organization and automation — team structures, role responsibilities, and tooling that automates project tracking and metrics.
- Unit 5: Agile and DevOps adoption — Scrum practices, DevOps delivery pipelines, and organizational rollout of DevOps tooling.
Mobile Adhoc Networks
(Common to CSE, CS, IT, CSE-AI, CSE-AI&ML, CSD) — pairs wireless-network theory with sensor-network design.
- Unit 1: Ad hoc network basics — MANET characteristics, applications, design challenges, and MAC-layer protocol design.
- Unit 2: Routing and transport — routing-protocol classification and TCP adaptations for ad hoc wireless environments.
- Unit 3: Security protocols — MANET-specific attacks, key management, secure routing, and intrusion detection.
- Unit 4: Wireless sensor fundamentals — sensor hardware constraints, energy consumption, clustering, and the WSN protocol stack.
- Unit 5: WSN security and tooling — key management and secure data aggregation in sensor networks, sensor operating systems like TinyOS, and simulators such as NS-2 and TOSSIM.
Natural Language Processing
— the linguistics-meets-algorithms course behind search, chatbots, and text analytics.
- Unit 1: NLP foundations — language modeling, regular expressions, finite-state automata, tokenization, and spelling correction.
- Unit 2: Word-level analysis — N-gram models, smoothing techniques, part-of-speech tagging, and hidden Markov/maximum-entropy models.
- Unit 3: Syntactic analysis — context-free grammars, treebanks, dependency grammar, and probabilistic parsing.
- Unit 4: Semantics and pragmatics — first-order logic representations, word-sense disambiguation, and thematic roles.
- Unit 5: Discourse analysis — coreference and anaphora resolution, plus standard lexical resources like WordNet and the Penn Treebank.
Big Data Analytics
— explains why scaling up a single server stops working, and what Hadoop/Spark-era tooling does instead.
- Unit 1: Big data landscape — industry use cases, the Hadoop ecosystem, and open-source, cloud-scale analytics technologies.
- Unit 2: NoSQL data models — key-value, document, and graph databases, sharding, replication, and consistency trade-offs, with a look at Cassandra.
- Unit 3: Hadoop internals — HDFS architecture, MapReduce data flow, and HiveQL for data definition and querying.
- Unit 4: Apache Spark — RDDs, DataFrames, the Catalyst optimizer, and cluster deployment on YARN or standalone mode.
- Unit 5: Stream processing — event-time processing, windowing, watermarking, and structured-streaming fundamentals.
Distributed Operating System
— a systems-level elective for students who want to understand what runs underneath distributed applications.
- Unit 1: Distributed system fundamentals — system models, distributed-OS design issues, and message-passing mechanisms.
- Unit 2: Remote procedure calls — RPC transparency, stub generation, argument marshaling, and client-server binding.
- Unit 3: Distributed shared memory — consistency models, synchronization, clock ordering, and deadlock/election algorithms.
- Unit 4: Resource and process management — global scheduling, load balancing and sharing, and process migration.
- Unit 5: Distributed file systems — file-sharing semantics, caching schemes, replication, and fault tolerance.
(A 12-week NPTEL/SWAYAM MOOC recommended by the Board of Studies may substitute for a classroom subject in this slot.)
Open Elective
Open Elective – II
— a cross-department elective. CSE students pick this from the pool of subjects offered by other engineering branches that semester (the specific subject and its syllabus therefore aren’t fixed within the CSE curriculum document and vary by college/department offering).
Labs
Cloud Computing Lab
— the hands-on companion to the Cloud Computing theory paper.
- Covers setting up virtual machines with VirtualBox/VMware, launching and configuring Amazon EC2 or OpenStack instances, deploying apps on Google App Engine, and running a containerized web server with Docker.
- Also includes serverless function experiments using OpenFaaS, a single-node Hadoop cluster setup with a word-count job, and cloud simulation/scheduling exercises using CloudSim.
Cryptography & Network Security Lab
— the applied counterpart to the cryptography theory paper.
- Covers implementing classical ciphers (Caesar, substitution, Hill) and modern block ciphers (DES, Blowfish, AES/Rijndael) in C/Java.
- Also includes RSA key generation and encryption, a Diffie-Hellman key-exchange simulation in JavaScript, and computing message digests with SHA-1.
Soft Skills
(Skill Enhancement Course) — an employability-focused lab-cum-classroom subject rather than a technical one.
- Covers listening and verbal/non-verbal communication, self-management (time, stress, and anger management), and workplace/social etiquette.
- Also includes grammar and business-writing practice, plus job-oriented sessions on group discussions, resume building, and mock interviews.
Technical Paper Writing & IPR
(Audit Course — no credit weight) — rounds out the semester with academic-writing and IP literacy.
- Covers structuring and drafting technical reports, proofreading, and presentation skills.
- Also covers word-processor tooling for citations, tracked changes, and document management, plus the basics of intellectual property — patents, designs, trademarks, copyright, and the patenting process.
Mandatory Industry Internship / Mini Project
Running alongside (technically, between) III-II and IV-I coursework, students must complete an 8-week industry internship or mini-project during the intervening summer vacation. It’s a graduation requirement rather than a semester-credit subject, and some colleges let students substitute it with an approved SWAYAM Plus program (e.g., a hands-on data-analytics or applied-AI masterclass).