This is a Markdown (.md) ver. of the pdf(s) located at THIS URL. Using a Web-based Tool (pdf2md.morethan) and later edited for better 'readability' on GitHub and vitepress. This Doc only includes Sem.II Syllabus for English, Sanskrit, Computer Sci.(DSA&DBMS), AI, and, Intro. to Social Work.
Sub Computer Sci. Source
BASED ON ANDHRA PRADESH STATE COUNCIL OF HIGHER EDUCATION WEB.
COURSE STRUCTURE (Semester I to VI) Computer Sci.
| Year | Sem | No. | Course Title | Hours/Week | Credits |
|---|---|---|---|---|---|
| I | I | 1 | 3 | 3 | |
| 2 | 1 | ||||
| 2 | 3 | 3 | |||
| 2 | 1 | ||||
| I | II | 3 | Data Structures using C | 3 | 3 |
| Data Structures using C – Practical | 2 | 1 | |||
| 4 | Database Management System | 3 | 3 | ||
| Database Management System – Practical | 2 | 1 | |||
| II | III | 5 | OOPS Through JAVA | 3 | 3 |
| OOPS Through JAVA – Practical | 2 | 1 | |||
| 6 | Computer Organisation | 3 | 3 | ||
| Computer Organisation – Practical | 2 | 1 | |||
| 7 | Probability and Statistics | 4 | 4 | ||
| II | IV | 8 | Operating Systems | 3 | 3 |
| Operating Systems – Practical | 2 | 1 | |||
| 9 | Python Programming | 3 | 3 | ||
| Python Programming – Practical | 2 | 1 | |||
| 10 | Software Engineering | 3 | 3 | ||
| Software Engineering – Practical | 2 | 1 | |||
| III | V | 11 | Data Mining | 3 | 3 |
| Data Mining – Practical | 2 | 1 | |||
| 12A | Web Interface Design Technologies | 3 | 3 | ||
| Web Interface Design Technologies – Practical | 2 | 1 | |||
| OR | Computer Networks | 3 | 3 | ||
| Computer Networks – Practical | 2 | 1 | |||
| 13A | Web Application Development using PHP & MySQL | 3 | 3 | ||
| Web Application Development using PHP & MySQL – Practical | 2 | 1 | |||
| OR | Cyber Security | 3 | 3 | ||
| Cyber Security – Practical | 2 | 1 | |||
| III | VI | 14A | Mobile Application Development | 3 | 3 |
| Mobile Application Development – Practical | 2 | 1 | |||
| OR | Cloud Fundamentals and Security | 4 | 4 | ||
| 15A | MERN Stack | 3 | 3 | ||
| MERN Stack – Practical | 2 | 1 | |||
| OR | Digital Forensics | 3 | 3 |
In the III Year (during the V and VI Semesters), students are required to select a pair of
electives from one of the Two specified domains. For example: if set ‘A’ is chosen, courses 12 to 15 to be chosen as 12 A, 13 A, 14 A and 15 A. To ensure in-depth understanding and skill development in the chosen domain, students must continue with the same domain electives in both the V and VI Semesters. This table doesn't include the subtopics. Scroll down for that.
Subject-wise:
Breakdown of of units, topics, and sub-topics.
Data Structures using C
Theory Credits: 3 [3 hrs/week]
Unit 1. Basic Concepts:
- Algorithm: Definition and characteristics
- Complexity analysis: Space Complexity, Time Complexity, Asymptotic Notations.
- Introduction to Data structures: Definition, Types of Data structures, Abstract Data Types (ADT),
- Introduction to Linked Lists, Representation of linked lists in Memory, Comparison between Linked List and Array.
Unit 2. Linked list
- Linked Lists: Types of Linked Lists
- Singly Linked list, Doubly Linked list, Circularly Singly Linked list, Circularly Doubly Linked list; Implementation of Single Linked List ADT: Creating a List, Traversing a linked list, Searching in linked list, Insertion and deletion into linked list (At first Node, Specified Position, Last node).
Unit 3. Stacks and Queues: Introduction to stack ADT, Implementation of stacks using array and Linked List, Application of stacks - Polish Notations - Converting Infix to Post Fix Notation - Evaluation of Post Fix Notation.
- Queues: Introduction to Queue ADT, Implementation of Queues using array and Linked List, Application of Queues Types of Queues- Circular Queues, De-queues, Priority Queue, Heaps.
Unit 4. Searching and Sorting:
- Linear or Sequential Search, Binary Search, Hashing and collision resolution.
- Sorting: Selection Sort, Bubble Sort, Insertion Sort, Quick Sort and Merge Sort
Unit 5. Trees and Graphs:
- Tree Terminology, Binary Tree Representation, Traversal techniques, Expression Tree, Binary
- Search Tree: Definition, Operations on a Binary Search Tree: Creation, Search, Insertion & deletion.
- Graphs: Introduction to Graphs, Terminology, Representation (Adjacency Matrix, Adjacency List), Traversal of Graphs (DFS, BFS), Applications of Graphs, Concept of Shortest Path Problems, Concept of Minimum Cost Spanning Tree
Textbooks:
- Data Structures Using C, Balagurusamy E. Tata MCGraw Hill
- Data Structures using C, Reema Thareja, Third Edition, Oxford University Press
Reference Books:
- Data Structures, Lipschutz, Schaum’s Outline Series, Tata Mcgraw-hill
- Data Structures Using C, Ch. Vijay Kumar, Pen Press International
Data Structures using C [Practical]
Credits: [1 2 hrs/week]
List of Experiments
- Write a program to read ‘N’ numbers of elements into an array and also perform the following operation on an array
- a. Add an element at the beginning of an array
- b. Insert an element at given index of array
- c. Update an element using a values and index
- d. Delete an existing element
- Write a program to implement Single Linked List with insertion, deletion and traversal operations
- Write a program to implement Doubly Linked List with insertion, deletion and traversal operations
- Write a program to implement the Stack operations using Arrays and Linked Lists.
- Write a program to convert a given infix expression to a postfix expression using stacks.
- Write a program to implement the Queue operations using Arrays and Linked Lists.
- Write a program to implement the Circular Queue operations using Arrays.
- Write a program for Binary Search Tree Traversals
- Write a program to search an item in a given list using the following Searching Algorithms
- a. Linear Search
- b. Binary Search.
- Write a program for implementation of the following Sorting Algorithms
- a. Bubble Sort
- b. Insertion Sort
- c. Quick Sort
- d. Merge Sort
Database Management Systems
Theory Credits: 3 [3 hrs/week]
Unit 1. Overview of Database Management System:
- Introduction to data, information, database, database management systems, file-based system,
- Drawbacks of file-Based System, database approach, Classification of Database Management
- Systems, advantages of database approach, Various Data Models, Components of Database
- Management System, three schema architecture of data base, costs and risks of database approach.
Unit 2. Entity-Relationship Model:
- Introduction, the building blocks of an entity relationship diagram, classification of entity sets, attribute classification, relationship degree, relationship classification, reducing ER diagram to tables, enhanced entity-relationship model (EER model), generalization and specialization, IS A
- Relationship and attribute inheritance, multiple inheritance, constraints on specialization and generalization, advantages of ER modeling.
Unit 3. Relational Model:
- Introduction, CODD Rules, relational data model, concept of key, relational integrity, relational
- algebra, relational algebra operations, advantages of relational algebra, limitations of relational
- algebra, Functional dependencies and normal forms.
Unit 4. Structured Query Language:
- Introduction, Commands in SQL, Data Types in SQL, Data Definition Language, Selection
- Operation, Projection Operation, Aggregate functions, Data Manipulation Language, Table
- Modification Commands, Join Operation, Set Operations, View, Sub Query.
Unit 5. PL/SQL:
- Introduction, Shortcomings of SQL, Structure of PL/SQL, PL/SQL Language Elements, Data
- Types, Operators Precedence, Control Structures, Steps to Create a PL/SQL, Program, Iterative Control, Procedures, Functions.
Textbooks:
- Database System Concepts, Avi Silberschatz, Henry F. Korth,S. Sudarshan, Seventh Edition, McGraw-Hill
- Database Management Systems by Raghu Ramakrishnan, McGrawhill
Reference Books:
- Fundamentals of Database Systems, Elmasri Navathe Pearson Education
- An Introduction to Database systems, C.J. Date, A.Kannan, S.Swami Nadhan, Pearson
Database mangement Systems [Practical]
Credits: 1 2 hrs/week Link
Sub: AI Source
Applications of Artificial Intelligence
Theory Credits: 3 [3 hrs/week]
Unit 1. Infrastructure and Platforms for Building Applications using AI
- Hardware used in building AI applications: Processors - CPU, GPU, TPU, NPU; Memory - RAM, VRAM; Storage - HDD, SSD
- Platforms for building applications using AI: Online platforms (Google AutoML, H2O.ai, Teachable Machine or similar platforms); Desktop (No-code/Low code) platforms (Orange Data Mining, KNIME, Weka, RapidMiner or similar tools)
- Edge AI: Concept; Applications in devices like Refrigerators, Led Bulbs, Surveillance Cameras, Micro Ovens, Smart Cars/Scooters; Edge AI in smart Appliances
Unit 2: Foundations of Data - Types, Ethics and Utility in Building Applications using AI
- Importance of data in building AI applications: Data as the fuel for AI; Role of big data in training AI models
- Conceptual Foundations of Data: Data vs. Information vs. Knowledge
- Structure of Data: Structured, Semi-Structured, and Unstructured Data
- Modalities of Data: Text, Image, Audio, Video, Tabular, Time-Series, and Spatial Data
- Formats of Data: Text Formats (CSV, JSON, XML); Image Formats (JPEG, GIF, PNG); Audio/Video (MP3, WAV, MP4, AVI)
- Data Repositories: Public Datasets, Private Datasets; Examples – Kaggle, Hugging Face Datasets, UCI Machine Learning Repository, Google Dataset Search
- Ethics, Privacy in Data Usage: Privacy concerns; Regulations - GDPR, HIPAA (Overview); Ethical use of data; Responsible AI practices
Unit 3. The AI Data Pipeline: From Collection to Model Readiness
- AI Data Pipeline stages: Data Collection, Annotation, Preprocessing, Splitting, Feeding into AI Models
- Core Components: Ingestion, Storage, Processing, Validation, Delivery
- Data Collection Methods: Manual input, Sensors & IoT Devices, System Logs & Transactions, Web Scraping, APIs
- Data Annotation and Labelling: Manual Annotation, Automated Annotation; Types – Classification, Bounding Boxes, Segmentation, Transcription, Named Entity Recognition (NER)
- Data Cleaning and Preprocessing: Missing Values, Duplicates, Incorrect Formats, Outliers, Noise; Cleaning steps – Identify Issues, Handle Errors, Validate Cleaned Data
- Data Splitting: Training set and test set
- Data Transformation Techniques: Normalization, Transformation, Feature Engineering
Unit 4. AI-Powered No-Code Development: Vibe Coding and Workflow Automation
- Vibe Coding: Concept & Workflow; Vibe Coding vs traditional programming; Tools – Google AI Studio, Firebase Studio, Replit, Cursor, Windsurf
- Benefits & Challenges: Advantages and limitations of Vibe Coding; Shift from code-centric to prompt-driven development; Prompt crafting
- Workflow Automation using AI: Fundamentals; Applications – Auto-email responses, Feedback summarization, Social media alerts & analytics
- Tools: Zapier, Power Automate, n8n, Lindy
Unit 5. AI in Networks, Cybersecurity, and Forensics
- AI in Networking: Network Management, Traffic Prediction, Intrusion Detection, Optimization, Fault Management, Routing
- AI in Cyber Security: Need of AI in Cyber Security, Threat detection, Fraud detection, Security automation; Challenges and considerations
- AI in Digital Forensics: AI in forensic investigations, Evidence acquisition and analysis, Overcoming limitations, Future outlook of AI-powered forensic tools
Aplications Of Artificial Inteligence [Practical]
Credits: 1 [2 hrs/week]
Suggested Lab Practicals (No Coding) # Lol
Lab 1 - Exploring Public Datasets (Orange Data Mining)
• Visit a public repository (Kaggle, UCI, data.gov.in) • Download a dataset (e.g., rainfall data, literacy rates, or traffic accident statistics)
Procedure:
- Open Orange → Add File widget → Load a CSV (e.g., Titanic dataset).
- Connect to Data Table → View rows/columns.
- Connect to Data Info → Check attributes, data types.
- View in Data Table and Distributions widget.
Observation: Note numeric, categorical, missing values.
Outcome: Students understand structured data format in CSV.
Lab 2 – Exploring Cybersecurity Datasets (Orange Data Mining)
Dataset: Kaggle Cybersecurity dataset.
Procedure:
- Load dataset into Orange (File widget).
- View using Data Table and Distributions widgets.
- Identify numerical (packet size, duration) and categorical (protocol type, attack type) attributes.
Observation: Note features that indicate “attack” vs. “normal traffic.”
Outcome: Students understand the type of features used in intrusion detection.
Lab 3 - Understanding Dataset Metadata and Formats
• Take two datasets in different formats (CSV, JSON) • View metadata (description, features, size, license) • Compare domain-specific datasets (e.g., medical vs. finance)
Lab 4 - Data Annotation Exercise
- Use MakeSense.ai or VGG Image Annotator (VIA)
- Annotate 10 sample images (traffic signs, fruits, or medical scans)
- Export annotations in XML or YOLO format
- Discuss annotation errors and challenges
Lab 5 - Data Cleaning and Visualization (Orange Data Mining) Aim: To clean dirty data and visualize categorical and numeric attributes.
Procedure:
- Load dataset.
- Connect File → Edit Domain (to change types) and Impute (to fill missing values).
- Compare cleaned vs. original in Data Table.
- Distributions widget.
- Check various features distribution.
(Optional: Create simple bar charts/line charts to visualize trends using Google Looker Studio)
Observation: Missing values filled with mean/median., Graphical representation of data.
Outcome: Learn importance of data cleaning., Students learn importance of visualization in preprocessing.
Lab 6: Train/Test Split in Orange
Aim: To split dataset for AI training/testing.
Procedure:
- Load Titanic dataset.
- Connect File → Data Sampler (70% train, 30% test).
- Connect outputs to Data Table widgets to view.
Observation: Students see two different subsets.
Outcome: Concept of model validation using split data.
Lab 7 – Writing a Detailed Prompt for a Simple Game App (Generative AI)
Objective: Understand prompt engineering by designing a game idea.
Activity:
- Open ChatGPT (or Gemini, Copilot).
- Write a detailed prompt like “Create a simple text-based treasure hunt game with levels, scoring, and random challenges.”
- Ask the AI to refine game rules, scoring, and characters.
- Document how prompt detail changes the AI’s response.
Outcome: Students learn how detailed prompts shape AI outputs.
Lab 8 – Create a Portfolio Website using Vibe Coding Tool
Objective: Learn how AI-assisted coding tools can automatically generate websites from simple instructions.
Activity:
- Open Vibe Coding Tool (Windsurf/Cursor/Firebase Studio/Any other vibe coding tool).
- Give a natural language instruction: “Create a personal portfolio website for a Computer Science student. It should have sections: About Me, Education, Skills, Projects, and Contact.”
- Experiment with different prompts to change layout, theme, or color scheme (e.g., “Make it a modern dark theme with blue highlights.”).
- Preview the generated site and customize content.
Outcome: Students experience how AI converts prompts into functional websites with minimal coding effort.
Lab 9 – Develop an Interactive Education Quiz App using Vibe Coding Tool
Objective: Understand AI’s role in creating educational applications.
Activity:
- Open Vibe Coding Tool.
- Give prompt: “Build an interactive quiz app for students with multiple-choice questions on AI basics. Include features: Start Quiz, Show Score, Retry.”
- Refine the app by asking AI to: ▪ Add timer for each question. ▪ Show correct/incorrect answers instantly. ▪ Add a Leaderboard page.
- Test the app by playing the quiz.
Outcome: Students see how AI-generated apps can support e-learning and assessments.
Lab 10 - Automating Feedback Summarization using n8n and AI
Objective: Automatically summarize student feedback responses using AI and email the summary to the teacher.
Steps:
- Trigger Node: Google Sheets (watch new row for feedback).
- AI Node: Send text to OpenAI/Gemini API for summarization.
- Action Node: Gmail → email summarized feedback to teacher.
- Test: Enter sample feedback in Google Sheet → receive AI summary via email.
- Discussion: How AI reduced manual effort in reading every response.
Outcome: Students see how automation + AI can transform data into insights instantly.
Lab 11 – Using AI Functions in Google Sheets
Objective: Enable students to experience Google Sheets’ built-in AI-powered features like summarizing, categorizing, sentiment analysis, and text generation through simple prompts within the spreadsheet environment.
Lab 12 - Dueep Fake Image Detection
Objective Enable students to critically assess image authenticity using multiple free AI tools, understanding the strengths and limitations of each.
Tools Deepfake-O-Meter Decopy AI Image Detector
Procedure
- Collect Images
- Run through DeepFake-o-Meter
- Use Decopy AI Image Detector
Observation: How AI tools help in Digital Forensics.
The Tools suggested above are tentative. Teacher/Student is free to choose any other similar tool to execute the said lab experiments. Like we have labs lol.
ENGLISH BRIDGE-II: COMMUNICATE AND CONNECT
Theory Credits: 4 [4 hrs/week]
Unit I
- Prose: “On Saying Please” – A.G. Gardiner
- Short Story: “Half a Rupee Worth” – R.K. Narayan
- Conversion of Words.
Unit II
- Poem: “If” – Rudyard Kipling
- Prose: “I Have a Dream” – Martin Luther King Jr.
- Skimming & Scanning
Unit III
- One-Act Play: “Never, Never Nest” – Cedric Mount
- Short Story: “The Gift of the Magi” – O. Henry
- Report Writing
Unit IV-
- Short Story: “How I Taught My Grandmother to Read” – Sudha Murty
- Information Transfer: Pie Charts , tree diagram and flow chart.
- Note-making
Unit V
- Prose: “The Secret of Work” – Swami Vivekananda
- Notices, Agendas, and Minutes
- One-Word Substitutes
Multidisciplinary Course Introduction to Social Work
Unit-I:(07Hrs). Introduction to social work and concepts related to social work
- Introduction to Social Work: Definition, Scope, objectives, Functions, social service, social welfare services, social reform, major social problems in India;
- Social work philosophy, values, objectives, principles, methods and fields of social work.
Unit-II:(09Hrs). Methods of Working with Individuals and Groups
- Social case work: Definition-scope and importance of social case work, principles and process of social case work
- Tools and techniques in social case work- Counselling skills.
- Social Group Work: Definition-scope- the need for social group work – Group work process
- Principles of Group Work - Stages of Group Work-Facilitation skills and techniques.
Unit-III: (09Hrs). Workingwith Communitiesand Field Work in social work
- Community – definition - characteristics- types- community organisation as a method of social work-definition-objectives-principles
- phases of community organization concepts of community development, community participation and community empowerment.
- Field work in social work – Nature, objectives and types of field work - Importance of field work supervision.
Suggested Co-curricular Activities:( 05 hours)
Divide the students into groups, each group containing not exceeding 10 students depending upon the total number of students in a class or section. Each group can search in internetabout any one of the institutions which work for the welfare of children or women or elderly or scheduled caste and scheduled tribe children or differently abled persons or Juvenile homes or Correctional homes or hospitals or Mahila Pragathipranganam or Swadhar project or any social welfare project or non governmental organizations (NGOs) to have an idea about welfare agencies working for the needy. Ask each group to exchange and discuss the information with other groups in the classroom with the information they collected on Internet.
Group Discussion with the students- what type of community problems they observe in their villages/towns/cities? Ask them to tell what are the line departments which will help to solve the problems of their communities and suggest them what type strategies help the communities to empower. Invited lectures/Training by local experts Visit to a community Assignments, Quiz etc.
References:
- Chowdhary, Paul. D. (1992). Introduction to Social Work. New Delhi: Atma Ram and Sons.
- Friedlander W.A. (1955). Introduction to social welfare, New York, Prentice Hall.
- Government of India, (1987). Encyclopedia of Social Work in India (Set of 4 Volumes).
- New Delhi, Publications Division, Ministry of Information and Broadcasting.
- Lal Das, D.K. (2017). Practice of Social Research – Social Work Perspective, Jaipur, Rawat Publications. Madan, G.R. (2009). Indian Social Problems (Volume 1 & 2). New Delhi: Allied publishers Private Limited.
- Siddiqui, H.Y.(2007). Social Group Work. Jaipur: Rawat Publications
- Pasty McCarthy &Carolin Hatcher, (2002). Presentation skills. The Essential Guide for Students. New Delhi, Sage Publications. Websites on Social work methods.
Chemistry
Unit I: Matter, Atoms, Molecules & Nuclear Chemistry Classification of matter, Dalton atomic theory, Thomson Model, Rutherford Model, Bohr’s model of atom, quantum numbers, electronic configuration, Aufbau Principle, Pauli’s exclusion principle, Hund’s rule. Isotopes-Isobars, Nuclear decay, Band of Stability, Nuclear Reaction types, Nuclear Applications.
Unit II: Elements, Classification and Chemical Bonding Classification of elements, Periodic Classification of elements based on electronic configuration, classification into types, classification into metals, non-metals and metalloids, periodic properties- atomic radii, ionisation enthalpy, electronegativity, Octet rule, ionic bond properties of Ionic compounds-covalent bond, properties of covalent molecule.
Unit III: Acids, Bases, Salts, Chemistry in Daily life Definition, types and properties of Acids, Bases, Salts, strength of acids and bases, pH, Importance of Chemistry in daily life. (food, drugs, textiles, preservatives, soaps and detergents.)
III. List of Reference Books:
- Inorganic Chemistry by Puri and Sharma
- Basic concepts of Inorganic Chemistry by D.N.Singh
Sanskrit POETRY, PROSE & GRAMMER-II
Unit-1. प्राचीन पद्य सावित्यम् (9h)
- पाणणग्रिणम्- रघुिम्शमिाकाव्ये 7 सगािः
- पत्राचाानम्- नानाग्रन्थेभ्यिः
Unit-2. आधुवनक पद्य सावित्यम् (9h)
- पन्नाधात्री – श्रीमत्प्रतापरणायनेमेिाडकाण्डे13 सगािः
- सुखिगािः – धम्मपदम् (Sanskrit Version of Prof. P.Sriramachandrudu)
Unit-3. गद्य सावित्यम् (9h)
- अमोघदशानम् - बाणस्य कादंबरीतिः
- चारुचेवितम् - कविकोपकलापत िः
Unit-4. व्याकरणम् (9h)
- अजन्त शब्ािः (नदी, तनु, िधू, मातृ, िन, फल, िारर, मधु)
- धातििः (इष्, णलख्, कृञ्, क्रीञ्, चुर, ् रमु, िन्द्, युध्)
Unit-5: व्याकरणम् (9h)
- सन्धयिः (िल्सन्न्धिः – विसगासन्न्धिः)
- समासािः (अव्यायीभाििः, बहुव्रीवििः)
Sub list: (lang-I) English, (Lang-II) Sanskrit, (Major-I) Data Structures, (Major-II) Database management systems, (Skill) AI, (Multidisciplinary) Introduction to Social Work. Semester exam is in no info as of 07/03/26 Maybe in Apr or June.