MA25C13 Probability and Statistics – Semester III – CSD – R-2025

Subject Code & Name: MA25C13 – Probability and Statistics

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer Science and Design (CSD)

Credits / L-T-P: 4 Credits | L-T-P: 3-1-0

Course Objectives

• To introduce data collection methods, classification techniques, and graphical representation of data using charts and plots.
• To explain the fundamental concepts of descriptive statistics, probability theory, random variables, and hypothesis testing for analyzing data.
• To demonstrate the application of statistical techniques such as experimental design and process control using R/Python for data-driven decision-making.

Full Unit-wise Syllabus

Unit I – Descriptive Statistics

Collection of Data – Classification – Tabulation – Graphical Representation – Simple Bar Chart – Pie Chart – Measures of Central Tendency: Arithmetic Mean, Median and Mode – Measures of Variation: Range, Quartile Deviation – Standard Deviation and Coefficient of Variation – Five Number Summary – Box Plot Technique.

Activities: Application of descriptive statistics and data presentation methods using R/Python programming and Analysing data using Box Plots using R/Python programming.

Unit II – Probability and Random Variables

Axioms of probability – Conditional probability – Total probability – Bayes' theorem – Random variable – Distribution function – properties – Probability mass function – Probability density function – Moments – Standard Distributions – Binomial, Poisson and Normal Distributions – Problems, Uniform Distribution and Exponential Distribution (Simple Problems).

Activities: Application of various distributions using R/Python programming.

Unit III – Two-Dimensional Random Variables

Joint distributions – Marginal and conditional distributions – Expected values of functions of two variables – Correlation and regression (for discrete data only) – Central limit theorem – Statement and Simple Problems.

Activities: Applications of Correlation and Regression using R/Python programming.

Unit IV – Testing of Hypothesis

Large sample tests for single mean and difference of means – Small samples tests based on t and F distributions (single mean, difference of means, paired t-test and variance ratio test) – Chi-square test for independence of attributes and goodness of fit.

Activities: Application of Student – t test, F test, Chi-square test using R/Python programming.

Unit V – Design of Experiments

Analysis of Variance (ANOVA) – Completely Randomized Design (CRD) – Randomized Block Design (RBD) – Latin Square Design (LSD).

Activities: Application and visualization of One-way ANOVA and Two-way ANOVA using R/Python programming.

Course Outcomes (COs)

• CO1: Understand concepts of descriptive statistics, probability theory and testing of hypothesis.
• CO2: Apply probability distributions and statistical methods to solve engineering problems.
• CO3: Analyze data using correlation, regression, and probability models.
• CO4: Utilize hypothesis testing, ANOVA for data-driven decision-making.

Assessment Pattern (Quick Note)

• Weightage: Continuous Theory Assessment 20% | Continuous Lab Assessment 20% | End Semester Examinations 60%
• Assessment Methodology: Quiz 10%, Assignments 20%, Lab Manual 15%, Lab Examination 15%, Internal Examinations 40%

Source: Official Anna University – B.E. Computer Science and Design R-2025 Syllabus
Last Updated: September 2026

ANNA UNIVERSITY | SYLLABUS | UNIVERSITY QUESTION PAPER | NOTES

CD25301 Object Oriented Analysis and Design – Semester III – CSD – R-2025

Subject Code & Name: CD25301 – Object Oriented Analysis and Design

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer Science and Design (CSD)

Credits / L-T-P: 4 Credits | L-T-P: 3-0-2

Course Objectives

• To provide a strong foundation in object-oriented analysis and design, enabling learners to model, design, and develop scalable software systems using modern principles, UML, and design patterns.
• To equip students with practical skills in requirements engineering, testing, DevOps, and cloud-native architectures, aligned with current industry practices and emerging trends.

Full Unit-wise Syllabus

Unit I – Object-Oriented Principles and Requirements Engineering

OOP Concepts, Object Relationships & Benefits, SDLC, Agile & Scrum Basics. Requirements Engineering & Design: Functional & Non-functional Requirements, User Stories, DDD Basics, UML (Use Case, Class, Object Diagrams).

Practical: Develop Requirements and User Stories for a Real System (Banking/Library) – Tool: Trello; Design UML Diagrams for the System (Use Case, Class, Object) – Tool: StarUML

Unit II – Advanced UML Modeling & System Design

CRC Modeling, Sequence & Communication Diagrams, Activity & State Chart Diagrams, SOLID Principles, GRASP Patterns, Coupling & Cohesion. Package, Component & Deployment Diagrams, API-first Design, RESTful Modeling Basics.

Practical: Prepare CRC Cards and Develop Sequence & Activity Diagrams – Tool: Lucidchart or StarUML; Model RESTful API and Design Deployment Diagram for a Distributed System – Tool: Postman and StarUML

Unit III – Design Patterns & Architectural Styles

Design Patterns: Creational, Structural, Behavioral, Anti-patterns. Architectures: MVC, Layered, Clean, Hexagonal, Microservices, Containerization (Docker).

Practical: Implement Factory and Observer Design Patterns – Tool: Eclipse IDE or Visual Studio Code; Develop MVC-based Mini Application and Compare Monolithic vs Microservices Design – Tool: Spring Boot or Node.js

Unit IV – Testing, Quality & DevOps Practices

OO Testing, Unit Testing (JUnit/PyTest), TDD, BDD, Refactoring, Code Smells, Quality Metrics. Git, CI/CD, DevOps & DevSecOps, Secure Coding.

Practical: Write Unit Test Cases and Refactor Code – Tool: JUnit or pytest; Git Branching, Merging and CI/CD Demonstration – Tool: Git and GitHub

Unit V – Enterprise Application Design & Cloud-Native Systems

Enterprise Architecture, Monolithic vs Microservices, Distributed Systems, Service Decomposition & DDD. REST & Messaging, API Gateway, Data Management, Scalability, Cloud-native Deployment, Observability.

Practical: Design Microservice Boundaries and Create Component Diagram – Tool: Lucidchart or Draw.io; Containerization Demo and Architecture Documentation – Tool: Docker and Google Docs

Unit VI – Emerging Trends in OOAD & Industry Practices

AI-assisted Design, Model-driven Engineering, Low-code/No-code, Secure Architecture & Zero Trust. Sustainable & Ethical Design, Documentation Standards, UML Automation, Project Integration.

Practical: UML Tool-based Diagram Generation and Architecture Documentation – Tool: StarUML or Lucidchart; Security Risk Identification and Mini Project Review Presentation – Tool: Microsoft PowerPoint or Google Slides

Course Outcomes (COs)

• CO1: Describe the fundamental concepts of object-oriented analysis and design, including requirements engineering, UML modeling, design principles, and modern software development practices.
• CO2: Analyze software requirements and system designs using object-oriented principles, UML diagrams, and design patterns to understand system structure and behavior.
• CO3: Evaluate software architectures, testing strategies, and DevOps practices to assess quality, scalability, and maintainability in real-world applications.
• CO4: Design scalable and maintainable software systems by applying object-oriented design principles, UML modeling, architectural patterns, and cloud-native development practices.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50%
• Internal methodology: Activities 10% (Assignments 30, Quiz 10, Project based learning 25, Flipped Classroom 10, Review of GATE questions 25), Internal Theory Examinations 30%, Internal Laboratory Examination 10%

Source: Official Anna University – B.E. Computer Science and Design R-2025 Syllabus
Last Updated: September 2026

ANNA UNIVERSITY | SYLLABUS | UNIVERSITY QUESTION PAPER | NOTES

EC25C04 Signals and Systems – Semester III – CCE – R-2025

Subject Code & Name: EC25C04 – Signals and Systems

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer and Communication Engineering (CCE)

Credits / L-T-P: 4 Credits | L-T-P: 3-1-0

Course Objectives

• The course builds foundational skills for analyzing continuous and discrete-time signals, including their classification and properties, and explores key transforms (Fourier, Laplace, Z, DTFT).

Full Unit-wise Syllabus

Unit I – Introduction to Signals and Systems

Definition of Signals and Systems, Classification of Signals, Operations on signals, Singularity functions and related functions. Analogy between vectors and signals, orthogonal signal space, complete set of orthogonal functions, Parseval's relations.

Unit II – Fourier Series Analysis

Fourier series representation of continuous time periodic signals, Trigonometric and Exponential Fourier series, Properties of Fourier series.

Unit III – Fourier Transform & Laplace Transform

Fourier transform of aperiodic signals, standard signals and periodic signals, Properties of Fourier transforms. Hilbert transform and its properties. Laplace transforms, RoC, properties. Inverse Laplace transform.

Unit IV – Continuous-Time LTI Systems

Continuous time Systems and its properties. Linear time invariant (LTI) system, Impulse response. Convolution. Analysis of LTI System using Laplace and Fourier transforms.

Unit V – Sampling, Quantization & Discrete-Time Systems

Sampling and reconstruction of band limited signals. Low pass and band pass sampling theorems. Aliasing. Anti-aliasing filter. Practical Sampling-aperture effect. Quantization. Discrete-time signals and systems. Discrete Fourier series, DTFT, Z-transform and its properties. Analysis of LTI systems using Z – transform.

Suggested Activities: Quiz based on competitive examination problems (GATE, IES), Simulation Assignment.

Course Outcomes (COs)

• CO1: Define and classify continuous-time and discrete-time signals, systems, and their fundamental properties.
• CO2: Apply Fourier Series techniques to analyze periodic signals and interpret their frequency domain characteristics.
• CO3: Analyze Linear Time-Invariant (LTI) systems using convolution and transform methods.
• CO4: Develop and adapt solutions using sampling, quantization, and discrete-time signal processing techniques for real-world applications and continuous learning.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Assignment (20%), Software activity (20%), Quiz (20%), Internal Examinations (40%)

Source: Official Anna University – B.E. Computer and Communication Engineering R-2025 Syllabus
Last Updated: September 2026

ANNA UNIVERSITY | SYLLABUS | UNIVERSITY QUESTION PAPER | NOTES

MG25301 Economic Analysis for Business Systems – Semester III – CSBS – R-2025

Subject Code & Name: MG25301 – Economic Analysis for Business Systems

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.Tech. Computer Science and Business Systems (CSBS)

Credits / L-T-P: 3 Credits | L-T-P: 3-0-0

Course Objectives

• This course aims to equip students with a robust understanding of fundamental and advanced concepts in managerial economics, consumer behaviour, and market structures, integrating standard economic theories with contemporary behavioural approaches.
• It seeks to develop analytical skills in cost and production optimisation, capital budgeting, and financial accounting to facilitate effective managerial decision-making.

Full Unit-wise Syllabus

Unit I – Introduction to Economic Principles

The problem of scarcity and allocation of resources – Nature, Scope, & Significance of Managerial Economics – Roles and Responsibilities of Managerial Economist – The circular flow of Economy – The Basic process of decision making – Types of Economic Analysis: Micro and Macro Economics.

Activities: A Group task to demonstrate scarcity opportunity cost.

Unit II – Demand, Supply, and Consumer Behaviour

Market Equilibrium – Law of Demand, Demand Function, and Exceptions to the Law of Demand – Elasticity of Demand: Classification of Price, Income & Cross elasticity, Advertising, and promotional elasticity of demand – Measurement of elasticity of demand – Law of supply, Supply Function, Elasticity of supply.

Activities: Real time data based analysis of elasticity of demand.

Unit III – Theory of Production and Cost Analysis

Production Function: Meaning, Features and types – Short-run and Long-run Production Function – Isoquants and ISO costs, Least-cost combination factor – Economies and Diseconomies of scale – Technological progress – Cost behaviour in Short-run and Long-run – Types of cost: Accounting and Economic cost – Cost concepts – LAC curve Break-Even Analysis (BEA): Determination of Break-Even Point.

Activities: Illustration of economies of scale/break even analysis based real time news reports.

Unit IV – Market Structures, Pricing, and Capital Budgeting

Forms of Business Organizations: Sole Proprietary, Partnership, Joint Stock Companies – Types of Markets: Perfect and Imperfect Markets – Features of Perfect Competition, Monopoly, Monopolistic and Oligopoly – Price-Output Determination and Price Discrimination.

Activities: Identifying and classifying sectors/companies that come under different market structures.

Unit V – Capital Budgeting

Capital and its Significance: Estimation of fixed and working capital requirements – Methods and sources of raising capital – Capital Budgeting Methods: Payback period, Accounting rate of return, Net present value, Internal Rate of return, and Profitability index.

Activities: Using real or hypothetical data to select project based on NPV, IRR and PI.

Unit VI – Macroeconomic Framework and Financial Analysis

National Income and its Components: GNP, NNP, GDP, NDP – Methods of measuring National Income – Business Cycles and Stabilisation Monetary and Fiscal Policy: Central Bank and the Government, FDI, Inflation, Unemployment, Taxes and Subsidies External sector Export and Import.

Activities: Recent budget analysis.

Course Outcomes (COs)

• CO1: Interpret foundational economic principles, scarcity, and opportunity costs to understand the macroeconomic environment.
• CO2: Demonstrate the laws of demand and supply to calculate elasticity and understand basic consumer behaviour.
• CO3: Analyse production functions and cost behaviours to mathematically determine the Break-Even Point (BEA) of a firm.
• CO4: Examine imperfect and perfect market structures to optimise pricing strategies and output determinations.
• CO5: Evaluate capital budgeting proposals (NPV, IRR) and macroeconomic policies to assess long-term organisational investments.
• CO6: Develop financial statements and calculate complex profitability/liquidity ratios using modern double-entry bookkeeping tools.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment (CIE) 40% | End Semester Examinations (SEE) 60%
• Assessment Methodology: Written Tests, Break-Even/Capital Budgeting Numerical Worksheets, Financial Ratio Analysis Project, and a Case Study on Organisational "Nudges."

Source: Official Anna University – B.Tech. Computer Science and Business Systems R-2025 Syllabus
Last Updated: September 2026

AD25C01 Exploratory Data Analysis – Semester III – AI&DS – R-2025

Subject Code & Name: AD25C01 – Exploratory Data Analysis

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.Tech. Artificial Intelligence and Data Science (AI&DS)

Credits / L-T-P: 4 Credits | L-T-P: 3-0-2

Course Objectives

• This course aims to provide in-depth knowledge and practical skills in performing Exploratory Data Analysis (EDA), using statistical and graphical techniques.

Full Unit-wise Syllabus

Unit I – Introduction

Purpose and goals of EDA – Mindset for effective data exploration – Significance of EDA in the data science lifecycle – Types of data and quality issues – EDA vs. Classical and Bayesian approaches – Tools and libraries for EDA – Visual techniques for initial insights.

Practical: Load a real-world dataset (CSV/JSON) into a Pandas DataFrame and inspect its structure using .head(), .info(), .shape, and .describe(); Choose one dataset and create a "data story" using 3 to 5 visualizations that highlight trends, patterns, or anomalies

Unit II – Data Wrangling

Importing, loading, and cleaning datasets – Merging, reshaping, and pivoting data – Handling missing values and outliers – Renaming, deduplication, discretization, and binning – Permutation and random sampling – Challenges in real-world data preprocessing.

Practical: Load a messy dataset with missing values, duplicates, and incorrect data types. Identify and handle missing values using Pandas functions, applying mean or median for numeric data and mode for categorical data. Remove duplicates and verify data consistency; Rename ambiguous column names to meaningful ones and convert improperly inferred data types to suitable formats (e.g., object to float). Apply binning (equal-width/equal-frequency) on a continuous column such as age or income. Display the new binned column and analyze its utility

Unit III – Multivariate Analysis, Relationship Exploration and Causal Inference

Univariate, Bivariate, and Multivariate Analysis – Interpreting relationships and Simpson's Paradox – Multicollinearity and correlation pitfalls – Visualizing multivariate data using pair plots and heatmaps – Causal inference in data science: correlation vs. causation, confounding variables.

Practical: Create and analyze a detailed correlation matrix. Identify and explain variable pairs with high correlation coefficients (r > 0.8 or r < –0.8); Construct a sns.pairplot() for 4–6 numerical columns with an additional categorical hue to observe grouped patterns across variables

Unit IV – Time-Oriented Data Exploration

Time-series data – Time-based indexing and date time conversion in pandas – Seasonal patterns, resampling (up/down), and aggregations – Line plots, rolling statistics, and anomaly spotting.

Practical: Perform both down-sampling (e.g., daily to monthly) and up-sampling (conceptual/fill methods) using resample() and aggregate meaningful summaries; Compute rolling mean and standard deviation for a chosen variable (e.g., 7-day moving average). Use plots to identify anomalies or trend shifts

Unit V – Statistical Testing for Insights

Hypothesis Testing: Null vs. Alternative, Type I & II Errors – Statistical tests: t-tests, p-values using scipy and statsmodels – Interpreting test results in the context of EDA.

Practical: Perform t-tests or other appropriate statistical tests using scipy.stats. Record t-values, p-values, and significance level conclusions; Interpret test results in layman terms: Report whether the difference is statistically significant, and what it implies for decision-making

Unit VI – From EDA to Model Deployment

Supervised vs. unsupervised models – Simple and Multiple Linear Regression – Train/Test Split, Cross-validation – Evaluation metrics: MAE, RMSE, R², Accuracy, F1-score – Saving models using pickle or joblib – Basic deployment using Flask for local predictions.

Practical: Load a dataset and select suitable independent and dependent variables for simple linear regression. Split the dataset into training and testing sets. Train a linear regression model using sklearn, evaluate it with MAE, RMSE, and R². Interpret the model's performance; Save the trained model using pickle or joblib. Build a minimal Flask application with a /predict route that takes input and returns a prediction. Test locally

Course Outcomes (COs)

• CO1: Describe the importance of exploratory data analysis in understanding and summarizing datasets.
• CO2: Analyze datasets identify patterns, trends, anomalies, and relationships among variables.
• CO3: Evaluate data quality and extract meaningful insights for decision-making.
• CO4: Design effective exploratory data analysis workflows for real-world datasets.
• CO5: Recognize the importance of continuous learning by adapting to emerging tools and evolving data-driven practices.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50%
• Internal methodology: Activities 10% (Assignments 30, Quiz 10, Virtual demonstration 25, Flipped Classroom 10, Review of GATE & IES questions 25), Internal Theory Examinations 30%, Internal Laboratory Examination 10%

Source: Official Anna University – B.Tech. Artificial Intelligence and Data Science R-2025 Syllabus
Last Updated: September 2026

IT25301 Web Technologies – Semester III – IT – R-2025

Subject Code & Name: IT25301 – Web Technologies

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.Tech. Information Technology (IT)

Credits / L-T-P: 4 Credits | L-T-P: 3-0-2

Course Objectives

• The objective of this course is to illustrate how the Internet works and how it is used in the real world. Students will learn to make web pages interactive using HTML, CSS, and JavaScript and build dynamic websites using both client-side and server-side tools.

Full Unit-wise Syllabus

Unit I – Introduction

World wide web and its evolution – E-mail, Telnet, FTP, E–commerce, Cloud Computing, Video conferencing – Internet service providers, IP Address, URL, Domain Name Servers – Web Browsers, Search Engine – Web Server vs Application Server.

Practical: Use Telnet to connect to a remote host and execute simple commands; Set up a Web Server and demonstrate serving static content

Unit II – HTML 5

HTML Tags, Structure, HTML Coding Conventions – Block Elements, Text Elements, Code Related Elements, Character References – Lists, Images, section, article, and aside Elements – nav and a Elements – header and footer Elements – Audio & Video Support – HTML Forms & Controls – Document Object Model (DOM).

Practical: Creation of registration or feedback form using form controls; Development of personal portfolio website

Unit III – Cascading Style Sheets (CSS) and Responsive Web Design

CSS Rules, Syntax and Style – Class Selectors, ID Selectors, span and div Elements – Cascading, style Attribute, style Container, External CSS Files – CSS Properties – UI Scripting Bootstrap, Bootstrap Grid System, Grid Classes, Basic Structure of a Bootstrap Grid, Typography, Components, Forms, Inputs, Bootstrap Themes, Templates.

Practical: CSS Styling Methods; Responsive website Design

Unit IV – Client-Side Scripting and Modern Javascript

Buttons, Functions, Variables, Identifiers – Assignment Statements and Objects – Document Object Model, Forms – reset and focus Methods – Event Handler Attributes External JavaScript Files – Manipulating CSS with JavaScript – Using z-index to Stack Elements Textarea Controls Pull-Down Menus – JSON: JavaScript Object Notation (JSON) – jQuery.

Practical: Perform Client-side validation and Event handling; Create an interactive To-Do List application

Unit V – Server-Side Programming

MVC – Servlet: Life Cycle – Types Request Dispatcher – Session Tracking – Servlet with JDBC – Hibernate: Architecture – Object Relation Mapping – Querying – JSP: Lifecycle – Components and Tags Elements Actions – Objects – JDBC. Spring – Architecture – Build Tools: Maven – Gradle – Spring Boot.

Practical: Develop dynamic websites using Servlet and JSP; Develop a Hibernate-Based Web Application

Unit VI – PHP and XML

PHP Variables – Program control Built-in functions – Form Validation – PHP Web app framework: Laravel. XML: Basic XML – Document Type Definition – XML Schema, XML Parsers and Validation, XSL.

Practical: Develop dynamic websites using PHP with database Connectivity; Create and Validate an XML Document with DTD or XML Schema

Unit VII – Angular and Web Applications Frameworks

AngularJS, MVC Architecture, Expressions and data binding, Conditional Directives, Style Directives, Controllers, Filters, Forms, Routers, Modules, Services; Web Applications Frameworks – React – Node JS – Express – Firebase – Docker – Django.

Practical: Develop Front-end application using AngularJS; Create a RESTful API Using Node.js and Express

Course Outcomes (COs)

• CO1: Describe the fundamental concepts of web technologies used in developing modern web applications.
• CO2: Analyze web technologies, to understand performance, and interoperability in web-based systems.
• CO3: Evaluate web development approaches to assess effectiveness in building scalable and secure web applications.
• CO4: Design web-based applications with appropriate technologies and frameworks for real-world deployment.
• CO5: Engage in continuous learning to keep pace with evolving industry practices for professional development.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50%
• Internal methodology: Activities 10% (Assignments 30, Quiz 10, Project based learning 25, Flipped Classroom 10, Review of GATE questions 25), Internal Theory Examinations 30%, Internal Laboratory Examination 10%

Source: Official Anna University – B.Tech. Information Technology R-2025 Syllabus
Last Updated: September 2026

EN25C03 English Communication Skills Laboratory – I – Semester III – CSE / IT / AI&DS / CSBS / CCE / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) / CSD – R-2025

Subject Code & Name: EN25C03 – English Communication Skills Laboratory – I

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer Science and Engineering (CSE) / B.Tech. Information Technology (IT) / B.Tech. Artificial Intelligence and Data Science (AI&DS) / B.Tech. Computer Science and Business Systems (CSBS) / B.E. Computer and Communication Engineering (CCE) / B.E. Computer Science and Engineering (Data Science) (CSE(DS)) / B.E. Computer Science and Engineering (Internet of Things) (CSE(IoT)) / B.E. Computer Science and Engineering (Cyber Security) (CSE(Cyber)) / B.E. Computer Science and Engineering (Artificial Intelligence and Machine Learning) (CSE(AI&ML)) / B.E. Computer Science and Design (CSD)

Credits / L-T-P: 1 Credits | L-T-P: 0-0-2

Course Objectives

• Foster students' confidence and fluency in professional and social communication.
• Bridge the gap between academic English and industry expectations.

Full Unit-wise Syllabus

Unit I – Elements of Effective Speaking and Listening

Sharing life experience / turning point in their life – SATORI; Situational Conversation – e.g. Talking to a Senior about Internship Tips; Welcoming a Guest Speaker at a Seminar; Pictography to represent data using images or symbols; B2-C1 Listening exercises include lectures, interviews, and discussions.

Unit II – Mastering Presentations

Presentation Skills – Non-verbal communication; Mini-Presentations: Topics like "My Dream Project," "Engineering in 2050," 3-minute technical pitches with logical flow; Technical Presentations with PPT.

Unit III – Group Discussion Strategies

Introduction to Group Discussions – Key skills for effective participation; Phases in a GD and Conversational Phrases in GD; Group Discussions – Abstract and Factual topics.

Unit IV – Resume & LinkedIn Optimization

Building LinkedIn Profile – Drafting headlines and summaries; Social Media Optimisation; Preparing Video Resume.

Unit V – Podcast-Based Language Learning

Listening to podcast (motivational, career oriented, success stories); Podcast Preparation – Purpose – Topic – Structure – Recording Tips – Publication of the Podcast.

Unit VI – Mock Interviews and Communication Strategies

Listening – Job interview; Speaking – Mock interviews.

Course Outcomes (COs)

• CO1: Communicate effectively in everyday professional situations with confidence.
• CO2: Deliver well-organised and effective presentations.
• CO3: Participate in group discussions and express ideas clearly and confidently.
• CO4: Create professional video resumes and participate in interviews effectively.
• CO5: Create, record and publish motivational podcasts.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 60% | End Semester Examinations 40%
• Internal Assessment: Listening (20 marks), Video Resume (20 marks), Creating a Podcast (30 marks), Mock interview (30 marks)
• End Semester Assessment: Presentation with PPT (50 marks), Group Discussion (50 marks)

Source: Official Anna University – B.E. Computer Science and Engineering R-2025 Syllabus
Last Updated: September 2026

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