Data ScienceSemester 3-2 syllabus

The second half of third year shifts focus to deep learning, operating systems, and data visualization as core subjects, with two elective slots covering topics from cryptography and cloud computing to computer vision and NoSQL databases. A soft-skills course and a technical-writing/IPR audit course prepare students for placements and research communication, alongside a mandatory summer industry internship or mini-project.

Semester load: roughly 20 lecture, 1 tutorial and 8 practical hours per week, totaling 23 credits.

Subjects

Deep Learning

• Unit 1: Biological neuron models and the perceptron learning algorithm
• Unit 2: Multilayer perceptrons, backpropagation, and regularization
• Unit 3: Modern optimizers (Adam, RMSprop, etc.) and training stabilization techniques
• Unit 4: Recurrent networks (LSTM, GRU) and convolutional architectures
• Unit 5: Recent developments — variational autoencoders and transformer models

Operating Systems

• Unit 1: OS services, system calls, and overall system structure
• Unit 2: Process management, threading models, and CPU scheduling
• Unit 3: Synchronization primitives and deadlock handling
• Unit 4: Memory management strategies and virtual memory
• Unit 5: File systems and protection mechanisms

Data Visualization

• Unit 1: The visualization process and principles of visual perception
• Unit 2: Building visual representations and visual-analytics workflows
• Unit 3: Classifying visualization systems and handling multi-dimensional/text data
• Unit 4: Visualizing hierarchical and network structures
• Unit 5: Visualizing volumetric, geographic, and collaborative data; evaluating visualizations

Professional Elective-II options:

Social Media Analytics, Cryptography & Network Security, Recommender Systems, Cloud Computing, or Sensor Networks.

Social Media Analytics

• Unit 1: Evolution of the web and characteristics of social media platforms
• Unit 2: The seven-layer social media analytics framework
• Unit 3: Text analytics techniques applied to social content
• Unit 4: Action analytics on social platforms
• Unit 5: Hyperlink analytics and viral-content detection

Cryptography & Network Security

• Unit 1: Core security concepts and classical encryption techniques
• Unit 2: Mathematical foundations of symmetric and asymmetric cryptography
• Unit 3: Block and stream ciphers, plus public-key algorithms (RSA, Diffie-Hellman, elliptic curve)
• Unit 4: Hash functions, message authentication, and digital signatures
• Unit 5: Transport, IP, and email security protocols

Recommender Systems

• Unit 1: Recommender system fundamentals and rating data
• Unit 2: Collaborative filtering approaches
• Unit 3: Content-based and knowledge-based recommendation
• Unit 4: Hybrid recommendation strategies
• Unit 5: Evaluation methods and the role of community/trust signals

Cloud Computing

• Unit 1: Cloud service and deployment models (IaaS/PaaS/SaaS, public/private/hybrid)
• Unit 2: Distributed computing foundations and service-oriented architecture
• Unit 3: Virtualization and container technologies (Docker, Kubernetes)
• Unit 4: Cloud economics, interoperability, and security challenges
• Unit 5: Serverless computing and cloud-centric IoT/edge computing

Sensor Networks

• Unit 1: Wireless network types and an introduction to sensor networks
• Unit 2: Single-node hardware architecture and network scenarios
• Unit 3: MAC and routing protocols for sensor networks
• Unit 4: Topology control, time synchronization, and localization
• Unit 5: Sensor node platforms, operating systems, and simulation tools

Professional Elective-III options:

Software Project Management, Quantum Computing, Computer Vision, NoSQL Databases, or an approved NPTEL/SWAYAM course.

Software Project Management

• Unit 1: Conventional software management and software economics
• Unit 2: Project life-cycle phases from inception through transition
• Unit 3–5: Project planning, tracking, organizational structures, and the tools used to manage schedule, cost, and resources

Quantum Computing

• Unit 1: Origins of quantum computing and qubits versus classical bits
• Unit 2: Underlying linear algebra and quantum-mechanical principles
• Unit 3: Qubit representation and quantum circuit design
• Unit 4: Core quantum algorithms (Deutsch-Jozsa, Shor, Grover)
• Unit 5: Quantum error correction and quantum cryptography

Computer Vision

• Unit 1: Camera models, radiometry, and shading
• Unit 2: Linear filtering, edge detection, and texture analysis
• Unit 3: Multi-view geometry and image segmentation
• Unit 4: Model fitting and motion tracking
• Unit 5: Geometric camera calibration and model-based vision

NoSQL Databases

• Unit 1: History and categories of NoSQL databases
• Unit 2: Comparing relational and NoSQL data models, replication, and sharding
• Unit 3: Document databases (MongoDB) and their use cases
• Unit 4: Column-family stores (HBase, Cassandra)
• Unit 5: Key-value and graph databases (Riak, Neo4j)

Deep Learning Lab

• Multi-layer perceptron and CNN implementations for image classification
• Text-classification exercises using embeddings and RNNs
• Transfer learning with pre-trained models such as VGG16
• Total: 3 practical hours per week, 1.5 credits

Data Visualization Lab

• Histogram, line-chart, and bar-chart exercises in R
• Box plots, scatter plots, and mosaic plots across sample datasets
• Heatmaps, geographic map visualizations, and 3D graphing
• Total: 3 practical hours per week, 1.5 credits

Soft Skills (Skill Enhancement Course)

• Unit 1: Analytical thinking, listening, and communication skills
• Unit 2: Self-management — time, stress, and anger management, plus workplace etiquette
• Unit 3: Grammar, correspondence, and professional writing
• Unit 4: Group discussions, resumes, and interview preparation
• Unit 5: Interpersonal relationships in professional settings
• Total: 1 tutorial and 2 practical hours per week, 2 credits

Technical Paper Writing & IPR (Audit Course)

• Unit 1: Fundamentals of technical report writing
• Unit 2: Drafting, editing, and plain-English writing conventions
• Unit 3: Proofreading and presenting final reports
• Unit 4: Word-processor techniques for formatting long documents
• Unit 5: Intellectual property fundamentals — patents, copyright, and the patenting process
• Ungraded audit course; no credits attached

Note: this semester is paired with a mandatory eight-week Industry Internship or Mini Project during the summer vacation.