MCA Semester 4 Syllabus – Gurugram University (GU) NEP

CORE

Soft Computing Syllabus – Unit-wise Topics

📊 Exam Pattern: 25 Internal Theory + 50 External Theory + 5 Practical Internal + 20 Practical External = 100 Marks

Soft Computing (CC-A10) — 4 Credits | 100 Marks. This course introduces the fundamental concepts of soft computing, genetic algorithms, artificial neural networks, fuzzy systems, and their applications in solving real-world problems. Unit I: Introduction to Soft Computing and Genetic Algorithms — Introduction to soft computing, Soft Computing versus Hard Computing, components of soft computing including Artificial Intelligence systems, Neural Networks, Fuzzy Logic, and Genetic Algorithms. Genetic Algorithms (GA) covering basic concepts, encoding techniques, fitness functions, reproduction methods including roulette wheel selection, Boltzmann selection, tournament selection, rank selection, and steady-state selection. Convergence of genetic algorithms and problem-solving using GA. Unit II: Artificial Neural Networks — Introduction to biological and artificial neural networks, various artificial neural network models, supervised learning, unsupervised learning, and reinforcement learning. Hebbian learning and Generalized Hebbian Learning Algorithm. Artificial Neural Network architecture including the basic building block of an artificial neuron, activation functions, McCulloch-Pitts model, Single Perceptron, Backpropagation Networks, Multi-Layer Perceptron (MLP), Hopfield Network, and applications of neural networks. Unit III: Fuzzy Systems and Applications — Concepts of fuzziness, membership functions, fuzzification and defuzzification processes, operations on fuzzy sets, fuzzy functions, linguistic variables, fuzzy relations, fuzzy rules, fuzzy inference mechanisms, fuzzy control systems, and fuzzy rule-based systems. Unit IV: Applications of Soft Computing — Applications of soft computing in pattern recognition, image processing, biological sequence alignment, drug design, robotics and sensor systems, information retrieval systems, share market analysis, and natural language processing (NLP).

CORE

Data Science and Visualization Syllabus – Unit-wise Topics

📊 Exam Pattern: 25 Internal Theory + 50 External Theory + 5 Internal Practical + 20 External Practical = 100 Marks

Data Science and Visualization (CC-A11) — 4 Credits | 100 Marks (25 Internal Theory + 50 External Theory + 5 Internal Practical + 20 External Practical). This course introduces Python programming, data analysis using NumPy and Pandas, data visualization techniques, machine learning, deep learning frameworks, and natural language processing tools. Unit I: Python Programming Fundamentals — Overview of Python programming concepts including data types, variables, assignments, numerical data types, operators and expressions, control structures, string manipulation, file handling (creating, reading, and writing text and numeric files), dictionaries, functions, and object-oriented programming (OOP) concepts. Unit II: NumPy and Pandas for Data Analysis — Introduction to NumPy including array creation, array generation using uniform distributions, random array generation, reshaping arrays, finding maximum and minimum values, arithmetic operations, mathematical functions, bracket indexing and selection, broadcasting, and indexing two-dimensional arrays (matrices). Data manipulation using Pandas including creating Series from lists, arrays, and dictionaries, storing data from intrinsic sources, creating DataFrames, data imputation, grouping and aggregation, merging, joining, concatenation, finding null values, and reading data from CSV, TXT, Excel, and web sources. Unit III: Data Visualization Techniques — Introduction to visualization, installation and setup of visualization libraries, canvas and axes, subplots, common plots including scatter plots, histograms, boxplots, logarithmic scale plots, tick placement and custom labels. Pandas visualization, style sheets, plot types, area plots, bar plots, line plots, scatter plots, box plots, hexagonal bin plots, Kernel Density Estimation (KDE) plots, distribution plots, categorical data plots, combined categorical plots, matrix plots, regression plots, grids, and overview of Python visualization toolkits and libraries. Unit IV: Machine Learning and Natural Language Processing — Introduction to machine learning using Scikit-Learn and PyTorch, data representation, estimators, parameters, model validation, model selection, learning curves, grid search, feature engineering, Naive Bayes Classification, Linear Regression, Support Vector Machines (SVM), and overview of Python machine learning and deep learning libraries. Introduction to Natural Language Processing (NLP) using NLTK, including tokenization, speech tagging, parsing, segmentation, recognition, text cleaning and normalization, along with an overview of other Python NLP toolkits and libraries. Examination Pattern: The examiner will set nine questions in total. Question 1 will contain seven short-answer parts from all units and will carry 20% of the total marks. The remaining eight questions will be set by taking two questions from each unit. Students must attempt five questions in total, including the compulsory Question 1 and one question from each unit.

ELECTIVE (DSE)

Full Stack Programming – 2 Syllabus – Unit-wise Topics

Full Stack Programming – 2 (DSE-04) — 3 Credits | 75 Marks (15 Internal Theory + 35 External Theory + 5 Internal Practical + 20 External Practical). This course focuses on modern full-stack web development technologies including Node.js, Express.js, MongoDB, Angular, React, and the MERN stack for building scalable web applications. Unit I: Web Development Frameworks and Architecture — Understanding the fundamentals of web development including User, Browser, Web Server, Backend Services, and MVC (Model-View-Controller) Architecture. Overview of different technology stacks and the roles of Express.js, Angular, Node.js, MongoDB, and React in modern full-stack application development. Unit II: Node.js Fundamentals and Backend Development — Basics of Node.js, installation and setup, working with Node packages, using Node Package Manager (NPM), creating simple Node.js applications, event-driven programming, event listeners, timers, callbacks, handling data input/output operations, and implementing HTTP services using Node.js. Unit III: NoSQL Databases and MongoDB — Introduction to NoSQL databases and MongoDB, setting up the MongoDB environment, user accounts and access control, database administration, collection management, connecting MongoDB with Node.js applications, and developing simple database-driven applications. Unit IV: Express.js, Angular and React Development — Implementing Express.js in Node.js applications, Angular fundamentals including TypeScript, Angular components, expressions, data binding, and built-in directives. MERN Stack development concepts, creating basic React applications, React components, React state management, Express REST APIs, modularization using Webpack, routing with React Router, and server-side rendering techniques. Examination Pattern: The examiner will set nine questions in total. Question 1 will contain seven short-answer parts from all units and carry 20% of the total marks. The remaining eight questions will be set by taking two questions from each unit. Students must attempt five questions in total, including the compulsory Question 1 and one question from each unit.

MULTIDISCIPLINARY (MDC)

MDC Options – Semester 4 (Choose One from Pool): Syllabus – Unit-wise Topics

MDC Options – Semester 4 (Choose One from Pool):

Cloud, Edge & Fog Computing (241/MCA/MD401) — 3 Credits | 75 Marks. Unit I: Introduction to Cloud Computing, cloud characteristics, benefits and limitations, evolution of cloud computing, NIST model, cloud cube model, cloud vs client-server, cluster and grid computing, deployment models (public, private, hybrid, community), service models (IaaS, PaaS, SaaS, IDaaS, CaaS), cloud applications and healthcare use cases. Unit II: Cloud Management and Virtualization, Service Oriented Architecture (SOA), Service Level Agreements (SLAs), cloud lifecycle management, virtualization concepts, hypervisors, machine imaging, load balancing, VMware case study, cloud security challenges, security standards and cloud services by Amazon, Microsoft and Oracle. Unit III: Fog Computing concepts, architecture, applications, services, fog protocols, DDS/RTPS protocols, Fog Kit, privacy-preserving computation, blockchain technology and multi-party computation in fog environments. Unit IV: Edge Computing concepts, architectures, applications, cloud-edge-fog comparison, mobile edge computing, resource federation challenges, middleware infrastructures and security management in edge cloud architectures.

Internet of Things (41/MCA/MD402) — 3 Credits | 75 Marks. Unit I: Introduction to IoT, characteristics, physical and logical design of IoT, functional blocks, communication models, APIs, Machine-to-Machine (M2M) communication, software-defined networking and IoT security challenges. Unit II: Network and Communication Aspects, wireless medium access issues, MAC protocols, routing protocols, sensor deployment, node discovery, data aggregation and dissemination techniques. Unit III: Web of Things (WoT), IoT vs WoT, web architecture and standardization, unified multi-tier WoT architecture, business intelligence, cloud of things, cloud middleware and cloud standards. Unit IV: Resource Management in IoT, home automation, industrial and surveillance applications, domain-specific IoT solutions, clustering, synchronization, software agents and performance analysis of IoT systems.

Other MDC Subjects — Students may also be offered additional Multidisciplinary Course (MDC) subjects by the College/University as per NEP guidelines and university regulations.

ABILITY ENHANCEMENT (AEC)

English Language Communication – Level 3 Syllabus – Unit-wise Topics

English Language Communication – Level 3 (AEC | 241/ENG/AE301) — 2 Credits | 50 Marks (15 Internal + 35 External). This Ability Enhancement Course develops advanced professional communication competencies including technical writing, presentation skills, and workplace English for postgraduate students entering the IT industry. Unit I: Advanced Reading and Critical Thinking — Reading strategies for academic and professional texts, reading comprehension at advanced level, critical analysis of arguments, evaluating sources and evidence, inference and interpretation, summarizing and synthesizing information from multiple sources, and reading technical documentation and research abstracts. Unit II: Technical and Professional Writing — Writing technical reports, project reports, and proposals; executive summaries; formal and informal email etiquette in professional settings; writing research abstracts and literature reviews; documentation writing for software projects; editing and proofreading for clarity and conciseness; and writing for digital platforms. Unit III: Presentation and Oral Communication Skills — Planning and structuring presentations, visual aids and slide design principles, public speaking techniques, handling questions and answers, group discussions and debate skills, interview skills and mock interviews, telephone and video conferencing etiquette, and cross-cultural communication in global workplaces. Unit IV: Workplace English and Soft Skills — Professional vocabulary for the IT industry, writing minutes of meetings and agendas, negotiation language, conflict resolution communication, networking and professional relationship building, LinkedIn and professional digital presence, workplace etiquette, and preparing for campus placements (GD, PI, and aptitude communication). Examination Pattern: Question 1: Short-answer questions (attempt any four out of six) carrying 8 marks (4 × 2). Questions 2, 3, 4: One descriptive/essay question each from Units I, II, and III respectively, each carrying 9 marks.