CW25201 Computer Organization and Architecture – Semester II – CSBS – R-2025

Subject Code & Name: CW25201 – Computer Organization and Architecture

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.Tech. CSBS (CSBS)

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

Course Objectives

• To introduce the fundamental components of digital computer systems.
• To explain various Instruction Set Architecture (ISA) types and instruction execution processes.
• To impart knowledge on system performance metrics and evaluation techniques.

Full Unit-wise Syllabus

Unit I – Introduction

Functional Units of a Digital Computer, Classes of Computer Systems, Hardware-Software Interface, Operation and Operands of Computer Hardware, Instruction Set Architecture, RISC and CISC Architectures, Addressing Modes, Assembly Language Programming, Translation from High-Level Language to Machine Language, Performance Metrics, Benchmarks, Transition from Uniprocessors to Multiprocessors.

Activities: C code to machine code mapping; Assembly of computer system components.

Unit II – Arithmetic for Computers

Integer Arithmetic, Binary Parallel Adder, Carry Lookahead Adder, Carry Save Adder, Fast Adders, Binary Multiplication, Booth’s Algorithm, Bit Pair Recoding, Binary Division, Restoring and Non-Restoring Division, Floating Point Numbers (Single and Double Precision), Floating Point Representation, Arithmetic Operations on Floating Point Numbers, ALU Design, Parallelism and Computer Arithmetic.

Activities: Arithmetic Operations; Restoring / Non-restoring division.

Unit III – Processor Design

Design Conventions of a Processor, Datapath Design, Building the Datapath, Implementation of Basic MIPS ISA, Designing the Control Unit, Simple Implementation Scheme and Drawbacks, Execution of a Complete Instruction, Hardwired and Microprogrammed Control, Instruction Level Parallelism, Basic Concepts of Pipelining, Pipelined Datapath and Control, Performance, Pipeline Hazards – Structural, Data, and Control Hazards, Handling Exceptions.

Activities: CPU datapath analysis; Pipeline hazard analysis.

Unit IV – Memory and I/O

Types of Memories, Need for a Hierarchical Memory System, Cache Memories, Memory Mapping, Measuring and Improving Cache Performance, Virtual Memory, Paging and Segmentation, TLB, Implementing Protection with Virtual Memory, Memory Management Techniques, Associative Memories, Introduction to Virtual Machines, Memory and I/O Devices, Interfacing I/O Devices to the Processor, Memory and Operating System, Programmed Input/Output, Interrupts, Direct Memory Access (DMA), RAID.

Activities: CPU Cortex memory hierarchy; Cache memory mapping.

Unit V – Advanced ILP and Parallel Processing

Advanced Instruction Level Parallelism (ILP), Exploitation of ILP, Out-of-Order Execution, Dynamic Scheduling, Speculation, Dynamic Branch Prediction, Multiple Issue Processors – Static and Dynamic, Limitations of ILP, Multithreading.

Activities: Out-of-Order Execution and Dynamic Scheduling; Virtual Demonstration of processor performance in real workloads.

Unit VI – Next Generation Computer Architecture

Multicore Architectures, Superscalar Processors, VLIW, Introduction to Multicore and Multiprocessor Systems, Graphics Processing Units (GPU), CUDA Programming Paradigm, Neural Processing Units (NPU), AI Processing Chips (AI PC), Overview of Next Generation Processors.

Activities: ILP Pipeline Simulation; Dynamic branch prediction strategies.

Course Outcomes (COs)

• CO1: Describe the functional units and instruction set architectures of a computer system.
• CO2: Apply knowledge of processor functionality to implement and analyze the internal operations of a computer system.
• CO3: Design and analyze basic digital systems and control units for efficient instruction execution.
• CO4: Recognize the importance of learning advancements to keep up with evolving computer architecture.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Assignments (10%), Quiz (5%), Project based learning (20%), Flipped Classroom (5%), Review of GATE questions (10%) & Internal Assessment (50%)

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

AD25201 Python for Data Science – Semester II – AI&DS – R-2025

Subject Code & Name: AD25201 – Python for Data Science

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.Tech. AI&DS (AI&DS)

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

Course Objectives

• To impart knowledge on Python programming and how it can be used for solving problems.
• To illustrate how to handle, clean, and analyze data using Python libraries.
• To make use of Python tools and open datasets for real-world data science applications.

Full Unit-wise Syllabus

Unit I – Basics of Python

What is Python, Python Interpreter, Python language basics: Language Semantics, Data Types, Variables, Basic Functions, Operators, Flow Control Statements, Data Structures and Sequences: List, Tuple, Set, Dictionaries.

Practical: Programs using conditional and looping constructs; Programs using different data frames like list, tuple, set and dictionary.

Unit II – Functions and Files

Defining a Function, Passing Arguments, Return Values, Passing a List, Creating and Using a Class, Strings: Working with Strings, String Methods, Files: Reading from a File, Writing to a File, Exceptions, Python Libraries: Importing libraries.

Practical: Programs using functions and classes; Programs using strings and files.

Unit III – Foundations of Data Science

Introduction to Data Science – Applications of Data Science – Data Science Process: Overview, Defining Research Goals, Retrieving Data – Data Preparation: Data Wrangling – Handling Missing Data – Data Transformation, Outlier/Noise and Anomalies, Exploratory Data Analysis, Build the Model, Present Findings, Data Mining, Data Warehousing.

Practical: Data Creation and Mathematical operations; Graphs and Plotting.

Unit IV – Descriptive Analytics

Facets of Data, Types of Variables, Statistical Description of Data, Describing Data with Tables and Graphs, Describing Data with Averages, Describing Variability, Normal Distributions and Standard (z) Scores, Correlation, Scatter plots, correlation coefficient for quantitative data – computational formula for correlation coefficient, Regression, Regression line, least squares regression line.

Practical: Statistical description of data without libraries; Generation of correlation coefficient; Linear regression model.

Unit V – Numpy and Pandas Libraries

Creating Arrays, attributes, Numpy Arrays objects, Basic operations (Array Join – split – search – sort), Indexing, Slicing and Iterating, Copying Arrays, Arrays shape Manipulation, Identity Array, eye function. Exploring Data using Series – Exploring Data using Data Frames, Index objects – reindex, Drop Entry, Selecting Entries – Data Alignment, Rank and Sort, Summary Statistics, Index Hierarchy.

Practical: Creation of 1D, 2D, and 3D NumPy arrays; Array Slicing and Indexing operations; Reindexing, and aligning data across multiple Data Frames.

Unit VI – Data Visualization

Introduction to Matplotlib, Plots, making subplots, Controlling axes, Ticks, Labels and legends, Annotations and drawing on subplots, Saving plots to files, Seaborn library, Making sense of data through advanced visualization, Controlling the properties of Chart, Scatter plot, Line plot, Bar plot, Histogram, Box plot, Pair plot, Styling your plot, 3D plot of surface.

Practical: Line plot, bar plot, histogram, and box plot; Seaborn plots, plot styling and customization.

Course Outcomes (COs)

• CO1: Understand basic Python syntax and write simple programs.
• CO2: Apply Python functions, file handling, and object-oriented programming to solve structured problems.
• CO3: Design and analyze data-driven solutions using NumPy, Pandas, and Matplotlib.
• CO4: Develop continuous learning skills to use open-source tools and public datasets for data science tasks.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Assignments (10%), Quiz (5%), Project based learning (20%), Flipped Classroom (5%), Review of GATE questions (10%) & Internal Assessment 50%

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

IT25202 Digital Principles and System Design – Semester II – IT – R-2025

Subject Code & Name: IT25202 – Digital Principles and System Design

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.Tech. IT (IT)

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

Course Objectives

• To understand the basics of number systems and Boolean algebra.
• To learn how to design and analyze combinational and sequential logic circuits.
• To use hardware description languages (HDL) for implementing digital systems.

Full Unit-wise Syllabus

Unit I – Boolean Algebra

Number Systems, Binary, Octal, Hexadecimal, Representation of negative numbers, 1’s and 2s Complements, Arithmetic Operations, Binary Codes. Boolean Algebra, Theorems and Postulates, Functions, Truth Table, Logic Gates, Universal gates.

Practical: Simple functions using gates; Implementation of Boolean functions.

Unit II – Canonical Functions

Canonical and Standard Forms, Minterms and Maxterms, Sum of Products and Product of Sums, Conversions and Expansion.

Practical: Simplification and expansion of standard Boolean functions.

Unit III – Karnaugh Map and Combinational Logic

Simplification of Boolean Functions, Karnaugh Map, 2,3,4 variables, NAND / NOR Implementations, Combinational Circuits, Arithmetic Circuits, Half and Full Adders, Subtractors. Introduction to HDL.

Practical: Implementation of combinational circuits using gates for arbitrary functions; Implementation of Arithmetic circuits and extended operations.

Unit IV – Combinational Logic Design

Binary Parallel adder, Carry Look-ahead Adder, BCD Adder, Binary multiplier, Magnitude Comparator, Code Converters, Decoder, Encoder, Priority Encoder, Mux/Demux, Applications, Introduction to HDL and HDL for these circuits.

Practical: Combinational circuits using code converters; BCD adder, encoder and decoder circuits.

Unit V – Sequential Logic Design

R – S Latch, D Latch, Flip flops, SR, JK, T, D, Master /Slave Flip Flop, Flip flop excitation tables, Analysis of clocked sequential circuits, Moore /Mealy models, Registers, Shift Registers, Universal Shift Register. Counters, Asynchronous Ripple Counters, Synchronous Counters – Ring Counter, Johnson Counter.

Practical: Design of a digital circuit for solving practical problems.

Unit VI – System Design

Memory Systems, RAM, ROM, Memory Decoding, error detection and correction, Digital System Design using PROM, PLA, PAL, FPGA.

Activities: Combination of in class & Flipped.

Practical: Project demonstration and presentation; Mini project on the design of a digital circuit for solving practical problems.

Course Outcomes (COs)

• CO1: Identify number systems and basic logic gates.
• CO2: Apply Boolean algebra and Karnaugh maps to simplify and implement combinational logic circuits.
• CO3: Design and analyze digital systems with sequential components using HDL and hardware tools.
• CO4: Explore modern tools and resources to keep learning about digital system design.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Assignments (10%), Quiz (5%), Project based learning (20%), Flipped Classroom (5%), Review of GATE questions (10%) & Internal Assessment 50%

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

IT25201 Foundations of Data Science using Python – Semester II – IT – R-2025

Subject Code & Name: IT25201 – Foundations of Data Science using Python

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.Tech. IT (IT)

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

Course Objectives

• To equip students with a strong foundational understanding of data science concepts.
• To collect, clean, manipulate, and analyse data using Python libraries.
• To perform data operations and derive insights from real-world datasets.

Full Unit-wise Syllabus

Unit I – Python Language Basics and Data Structures

Python Language Basics – Scalar Types – Control Flow. Data Structures and Sequences: Tuple – List – Built-in Sequence Functions – dict – set – List, Set, and Dict Comprehensions. Functions: Namespaces, Scope, and Local Functions – Returning Multiple Values – Functions Are Objects – Files and the Operating System.

Practical: Programs using Data Frames; Programs using functions and files.

Unit II – Numpy Basics

The NumPy ndarray: A Multidimensional Array Object – Universal Functions: Fast Element-Wise Array Functions – Array-Oriented Programming with Arrays – File Input and Output with Arrays – Linear Algebra – Pseudorandom Number Generation.

Practical: Programs using numpy; Programs to solve linear algebra problems with numpy functions.

Unit III – Pandas Basics

Introduction to pandas Data Structures – Loading and Understanding Data – Data aggregation for computing Descriptive Statistics – Data Cleaning and Preprocessing.

Practical: Programs using numpy; Solving linear algebra problems.

Unit IV – Data Loading, Storage, and File Formats

Reading and Writing Data in Text Format – Binary Data Formats – Interacting with Web APIs – Interacting with Databases.

Practical: Data and Databases; Web APIs.

Unit V – Data Exploration and Wrangling

Data Transformation – String Manipulation. Data Wrangling: Hierarchical Indexing – Combining and Merging Datasets – Reshaping and Pivoting. Data Aggregation and Group Operations: GroupBy Mechanics – Data Aggregation – Apply: General split-apply-combine – Pivot Tables and Cross-Tabulation – Date and Time Data Types.

Practical: String manipulations; Data wrangling; Data aggregation operations; Handle time series data.

Unit VI – Data Visualization

Introduction to Data Visualization – Visualizing categorical data, visualizing time series data, Visualizing multiple variables – Visualizing Distribution & Relationships – Multivariate and Time Series Visualization exploration.

Practical: Visualization of Different kinds of Data; Distribution Analysis.

Course Outcomes (COs)

• CO1: Develop simple programs in Python with built-in data structures.
• CO2: Apply NumPy and Pandas libraries to organize and manipulate data efficiently.
• CO3: Design and analyze solutions involving APIs, databases, and real-world datasets.
• CO4: Enhance life-long learning skills to explore new data science tools and libraries beyond the classroom.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Assignments (10%), Quiz (5%), Project based learning (20%), Flipped Classroom (5%), Review of GATE questions (10%) & Internal Assessment 50%

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

UC25A04 Physical Education – II – Semester II – CSE / IT / AI&DS / CSBS / CCE / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) / CSE(AI) / CSD – R-2025

Subject Code & Name: UC25A04 – Physical Education – II

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.E. CSE (CSE)

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

Course Objectives

• To impart knowledge on gymnastic exercises and pressing needs for upskilling in a particular game.

Full Unit-wise Syllabus

Unit I – Basic Gymnastics Exercises

Warming up, Suitable exercise, Lead up games, Safety education, Movement education, Balanced Walk, execution, floor exercise, tumbling/acrobatics, grip, release, swinging, parallel bar exercise, horizontal bar exercise, flic-flac-walk and pyramids.

Unit II – Upskilling in Athletics

Upskilling in any one of the athletics: Broad Jump, High Jump, Triple Jump, Relay Sprints, Javelin Throw, Discuss Throw, Shot Put, Short and Long-distance Running.

Unit III – Advance Skills in Games

Advance skills in any one of the indoor/outdoor games, which has been opted by the student in the I semester.

Course Outcomes (COs)

• CO1: Understand and explain the importance of physical activity for mental and physical health.
• CO2: Apply safety principles and methods during sports activities.
• CO3: Develop teamwork, discipline, and leadership through sports and group activities and collaborate effectively.
• CO4: Demonstrate the advanced technical skills and strategic understanding in the game of their interest.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 100%
• Internal methodology: Attendance (60%), Quiz (10%), Participation in Sports and Games (20%) and Viva Voce (10%)

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

UC25A03 Life Skills for Engineers – II – Semester II – CSE / IT / AI&DS / CSBS / CCE / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) / CSE(AI) / CSD – R-2025

Subject Code & Name: UC25A03 – Life Skills for Engineers – II

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.E. CSE (CSE)

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

Course Objectives

• To impart and cultivate analytical reasoning, innovative thinking, effective collaboration, and ethical leadership to prepare students for complex challenges in professional and personal environments.

Full Unit-wise Syllabus

Unit I – Critical Thinking

Creativity, Critical Thinking, Collaboration, Problem Solving, Decision Making, Imagination, Intuition, Experience, Sources of Creativity, Lateral Thinking, Myths of creativity, Critical thinking Vs Creative thinking, Convergent & Divergent Thinking, Critical reading & Multiple Intelligence.

Activities: Two-Brainstorm Method, “30 Circles” Challenge, “Desert Survival” Simulation, Lateral thinking riddles and puzzles, "What If?" Scenario Writing, Fast vs. Slow Thinking Game, Creativity Myth Busters.

Unit II – Problem Solving

Techniques, Six Thinking Hats, Mind Mapping, Forced Connections. Analytical Thinking, Numeric, symbolic, and graphic reasoning. Scientific temperament and Logical thinking.

Activities: Case study analysis, Escape Room challenge.

Unit III – Leadership

Leadership Styles & Self-Assessment, Communication & Active Listening, Decision-Making & Responsibility, Teamwork & Delegation, Empathy, Integrity & Conflict Management, Vision, Motivation & Goal-Setting.

Activities: Crisis Leadership Simulation, Tower Challenge, Leadership Dilemmas Role-Play, Team Vision Board.

Course Outcomes (COs)

• CO1: Explain the importance of leadership and management skills in life.
• CO2: Apply and demonstrate creative thinking techniques to generate innovative solutions.
• CO3: Exhibit effective collaboration and communication skills through teamwork, active listening, and conflict resolution strategies.
• CO4: Integrate scientific temperament and logical reasoning into problem solving in engineering and real-world contexts.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 100%
• Internal methodology: Assignments (20%), Flipped Class & Worksheets (10%), Practical (30%), Internal Examinations (40%)

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

ME25C05 Re-Engineering for Innovation – Semester II – CSE / IT / AI&DS / CSBS / CCE / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) / CSE(AI) / CSD – R-2025

Subject Code & Name: ME25C05 – Re-Engineering for Innovation

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.E. CSE (CSE)

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

Course Objectives

• To cultivate foundational skills in prototyping, and automation for development of prototypes with real-world applications.
• To provide a comprehensive, hands-on exposure to product development through reverse engineering concepts.

Full Unit-wise Syllabus

Unit I – Bootcamp 1

Introduction to Product Development, Reverse Engineering, Overview of the product lifecycle, Hands-on disassembly of simple products, Practice of basic measurements and sketching, Introduction to CAD modeling of disassembled parts, Virtual assembly of parts.

Unit II – Bootcamp 2

Embedded System Programming (Open-source platforms), Practice of interfacing sensors, reading data, automation in home, healthcare and agriculture.

Unit III – Reverse Engineering

Sketch and prototype alternative designs, Group brainstorming sessions, Manufacture prototype parts using 3D printing and / or workshop tools, Assemble prototype product.

Course Outcomes (COs)

• CO1: Understand the product development lifecycle, including stages such as concept generation, design, prototyping, and testing.
• CO2: Apply reverse engineering techniques to analyze and document existing products.
• CO3: Collaborate in teams to fabricate prototypes using appropriate tools.
• CO4: Engage in independent learning and continuously adapt to emerging technologies in product design.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 60% | End Semester Examinations 40%
• Internal methodology: Project (30%), Assignment (10%), Practical (30%), Internal Examinations (30%)

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