Syllabus

---

Computer Networks

Definition: Computer Networks introduces network architectures, protocols, data communication, and application layer services for designing and understanding modern networks.

Module 1: Data Communication Components

• Representation of data and its flow Networks.
• Various Connection Topology, Protocols and Standards.
• OSI model, Transmission Media.
• LAN: Wired LAN, Wireless LANs, Connecting LAN and Virtual LAN.
• Techniques for Bandwidth utilization: Multiplexing - Frequency division, Time division and Wave division.
• Concepts on spread spectrum.

Module 2: Data Link Layer and Medium Access Sub Layer

• Error Detection and Error Correction - Fundamentals, Block coding, Hamming Distance, CRC.
• Flow Control and Error control protocols - Stop and Wait, Go back – N ARQ, Selective Repeat ARQ, Sliding Window, Piggybacking.
• Random Access, Multiple access protocols - Pure ALOHA, Slotted ALOHA, CSMA/CD, CDMA/CA.

Module 3: Network Layer

• Switching, Logical addressing – IPV4, IPV6.
• Address mapping – ARP, RARP, BOOTP and DHCP.
• Delivery, Forwarding and Unicast Routing protocols.

Module 4: Transport Layer

• Process to Process Communication.
• User Datagram Protocol (UDP), Transmission Control Protocol (TCP), SCTP.
• Congestion Control; Quality of Service.
• QoS improving techniques: Leaky Bucket and Token Bucket algorithm.

Module 5: Application Layer

• Domain Name Space (DNS), DDNS, TELNET, EMAIL.
• File Transfer Protocol (FTP), WWW, HTTP, SNMP.
• Bluetooth, Firewalls.
• Basic concepts of Cryptography.

---

Data Mining

Definition: Data Mining explores techniques for discovering patterns, associations, and knowledge from large datasets using classification, clustering, and association rules.

Module 1: Introduction

• Data mining concepts, architecture.
• Knowledge discovery process.

Module 2: Data Preprocessing

• Cleaning, integration.
• Transformation, reduction.

Module 3: Association Rules

• Apriori algorithm.
• FP-growth.

Module 4: Classification

• Decision trees, Naive Bayes.
• KNN, SVM.

Module 5: Clustering

• K-means, hierarchical.
• DBSCAN.

Module 6: Advanced Topics

• Web mining, text mining.
• Outlier detection.

---

Economics

Definition: Economics introduces micro and macro principles, demand-supply, production, markets, and Indian economy for informed decision-making.

Module 1: Basics

• Micro vs Macro economics.
• Production possibility curve.

Module 2: Time Value of Money

• Capital budgeting: NPV, IRR, Payback.

Module 3: Demand & Supply

• Elasticity, forecasting.
• Law of supply.

Module 4: Production & Costs

• Factors, returns to scale.
• Cost concepts, break-even.

Module 5: Markets

• Perfect competition, monopoly.
• Oligopoly features.

Module 6: Indian Economy

• Fiscal/monetary policy.
• LPG, inflation, WTO.

---

Statistics-II

Definition: Statistics-II advances inferential statistics, hypothesis testing, ANOVA, regression, and non-parametric methods.

Module 1: Sampling Distributions

• Central limit theorem.
• Standard error.

Module 2: Estimation

• Point and interval estimation.
• Confidence intervals.

Module 3: Hypothesis Testing

• Null/alternative hypothesis.
• Type I/II errors, p-value.

Module 4: Parametric Tests

• t-test, z-test.
• Chi-square test.

Module 5: ANOVA & Regression

• One-way ANOVA.
• Multiple linear regression.

Module 6: Non-Parametric Tests

• Mann-Whitney, Kruskal-Wallis.
• Correlation (Spearman).

---

Object Oriented Programming

Definition: Object Oriented Programming teaches programming paradigms using classes, inheritance, polymorphism, and encapsulation.

Module 1: OOP Concepts

• Classes, objects.
• Abstraction, encapsulation.

Module 2: Inheritance

• Types, method overriding.
• Polymorphism.

Module 3: Interfaces & Packages

• Abstract classes.
• Packages, access specifiers.

Module 4: Exception Handling

• Try-catch-finally.
• Custom exceptions.

Module 5: Multithreading

• Thread lifecycle.
• Synchronization.

Module 6: Advanced Features

• Collections framework.
• Generics.

---

Environmental Science

Definition: Environmental Science creates awareness about ecosystems, pollution, resources, biodiversity, and sustainable practices.

Module 1: Environment Basics

• Components, multidisciplinary nature.

Module 2: Ecosystems

• Structure, function.
• Energy flow, food webs.

Module 3: Resources

• Renewable/non-renewable.
• Conservation.

Module 4: Pollution

• Air, water, soil, noise.
• Causes, effects, control.

Module 5: Biodiversity

• Levels, threats.
• Conservation strategies.

Module 6: Sustainability

• Environmental laws.
• Sustainable development.

---