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

EN25C02 English for Professional Communication – 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: EN25C02 – English Essentials – II

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.E. CSE (CSE)

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

Course Objectives

• Enable learners to improve fluency and accuracy in spoken and written communication.
• Develop learners’ ability to articulate ideas clearly and effectively in formal and informal spoken interactions.
• Help learners construct well-organised written documents relevant to academic and workplace contexts.

Full Unit-wise Syllabus

Unit I – Oral Communication

Types (Verbal and Nonverbal), Interpersonal and group communication, Telephonic conversation.

Suggested Activities: Short presentations, Debates, Formal Speeches (Welcome, Vote of Thanks and introducing guests), Listen and respond to short podcasts.

Unit II – Business Correspondence

Email Communication, Formal Letters (Types), Business Meeting.

Suggested Activities: Email and letter writing (Complaint, request, permission), Agenda, minutes of the meeting.

Unit III – Academic Writing

Paraphrasing, Summarizing, Essay Writing, Instructions and Recommendations.

Suggested Activities: Essay writing (Cause and effect, argumentative, persuasive), User guides/manuals, policy document.

Unit IV – Team Work

Leadership Skills (Team building, Team Leader, Team player), Negotiation and Problem solving skills.

Suggested Activities: SWOT Analysis, Brainstorming and Group discussions.

Course Outcomes (COs)

• CO1: Understand the importance of communication and drafting skills in engineering and technology.
• CO2: Apply listening strategies to comprehend spoken English in various contexts.
• CO3: Participate actively in group discussions by analysing critically from different views.
• CO4: Create written reports coherently for various purposes.
• CO5: Adapt communication styles to global, multicultural environments.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50%
• Internal methodology: Worksheets (10%), Group Activity (20%), Report Writing (20%), Internal Examinations (50%)

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

CS25C07 Object Oriented Programming – Semester II – CSE / CSBS / CCE / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) / CSE(AI) / CSD – R-2025

Subject Code & Name: CS25C07 – Object Oriented Programming

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.E. CSE (CSE)

Credits / L-T-P: 5 Credits | L-T-P: 3-0-4

Course Objectives

• To impart the principles of object-oriented programming and their advantages over procedural programming.
• To develop problem-solving skills by creating real-world applications using OOP features.

Full Unit-wise Syllabus

Unit I – Principles of Object-Oriented Programming

Characteristics of object-oriented languages, C++ Program structure, Procedure Oriented Programming vs Object Oriented Programming, C++ constructs and syntax, tokens, variables, data-types, type conversion, operators, Expressions, Namespace, flow Control and decision making statements.

Practical: Simple programs using Operators and type conversion; Programs using Conditional and Loop statements.

Unit II – Classes and Objects

Abstraction mechanism: Classes, Objects, member data, member functions – Constructors and types – destructors, inline function, friend function – array of objects, objects as function arguments – memory allocation for objects, static members static data and static function.

Practical: Programs using in-line and friend functions; Programs using constructors and destructors.

Unit III – Inheritance and Compile Time Polymorphism

Inheritance: Derived Classes – Single inheritance – Multilevel Inheritance – Multiple Inheritance – Hierarchical inheritance – Hybrid inheritance. Operator Overloading: Compile time Polymorphism – Overloading Functions, Overloading Operators, Overloading Unary Operators – Overloading Binary Operators – Operator Overloading with Friend Functions.

Practical: Programs for inheritance and its types; Programs using friend function and operator overloading.

Unit IV – Pointers and Runtime Polymorphism

Pointers with arithmetic operations – this pointer – Pointers to Derived classes and Base classes – Compile time versus Runtime Polymorphism – Virtual functions – Late Binding – Abstract classes – Pure virtual functions and Virtual Destructors – Virtual base class.

Practical: Programs for pointer manipulation; Programs for virtual functions.

Unit V – Templates and Exception Handling

Class Templates – Function Templates – Overloading of Template Functions – String, iterators, hashes, IO streams; Exception Handling.

Practical: Programs using function and class templates; Programs using exception handling.

Unit VI – I/O Systems and File I/O

C++ Streams – Formatted and Unformatted I/O – File stream classes – File modes – File operations, Sequential Read / Write operations – Binary and ASCII Files – Error handling in file I/O with member function.

Practical: Programs for error handling in file and I/O management; Develop applications using OOP features.

Course Outcomes (COs)

• CO1: Understand the core OOP concepts and applications.
• CO2: Apply Object Oriented Paradigms to solve problems using C++.
• CO3: Design and Analyze solutions involving code reusability and complexity management.
• CO4: Demonstrate life-long learning skills through application development.

Assessment Pattern (Quick Note)

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

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

PH25C03 Applied Physics (CSIE) – 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: PH25C03 – Applied Physics (CSIE) – II

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.E. CSE (CSE)

Credits / L-T-P: 3 Credits | L-T-P: 2-1-0

Course Objectives

• To provide a comprehensive understanding of physics concepts in computer science and engineering applications.

Full Unit-wise Syllabus

Unit I – Magnetic Materials

Parameters, Ferromagnetic materials, Ferrites – Soft and Hard magnetic materials – GMR sensors – magnetic disk memories – Principle of magnetic recording – Magnetic data storage.

Activities: Determination of Hysteresis loop for ferromagnetic materials.

Unit II – Logic Gates

Conversion of Binary to decimal – decimal to binary – binary coded decimal code – logic gates (OR, AND, NOT, NAND and NOR) – Exclusive OR gate – simplification based on basic Boolean theorems (sum of products, product of sums expression) – simplification by Karnaugh Map method (don’t care conditions).

Activities: Virtual demonstration of Logic Gates.

Unit III – Nano-Devices

Introduction – electron density in bulk material – size dependence of Fermi energy – quantum confinement – quantum structures: quantum wells, wires and dots – band gap of nanomaterials. Tunneling – Coulomb blockade – single electron transistor – resonant-tunneling diode – Carbon nanotubes: Properties and applications.

Activities: Virtual demonstration of single electron transistor.

Unit IV – Quantum Computing

Quantum system for information processing – quantum states – classical bits – quantum bits or qubits – Bloch sphere – CNOT gate – Single and multiple qubits – quantum gates (Pauli – X, Y and Z Gates, Hadamard Gate, Phase gate – T gate, CNOT Gate) – advantage of quantum computing over classical computing.

Activities: Virtual demonstration of quantum computing.

Course Outcomes (COs)

• CO1: Explain the concepts of physics in computer science stream.
• CO2: Apply appropriate techniques in physics to solve engineering problems.
• CO3: Analyse physical systems and interpret data from the virtual studies in the core branches in computer science and engineering.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Quiz (10%), Assignments (30%), Flipped Class (10%), Internal Examinations (50%)

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