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This is a/social-work/ 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.

YearSemNo.Course TitleHours/WeekCredits
II1Computer Fundamentals and Office Automation33
Computer Fundamentals and Office Automation – Practical21
2Problem Solving Using C33
Problem Solving Using C – Practical21
III3Data Structures using C33
Data Structures using C – Practical21
4Database Management System33
Database Management System – Practical21
IIIII5OOPS Through JAVA33
OOPS Through JAVA – Practical21
6Computer Organisation33
Computer Organisation – Practical21
7Probability and Statistics44
IIIV8Operating Systems33
Operating Systems – Practical21
9Python Programming33
Python Programming – Practical21
10Software Engineering33
Software Engineering – Practical21
IIIV11Data Mining33
Data Mining – Practical21
12AWeb Interface Design Technologies33
Web Interface Design Technologies – Practical21
ORComputer Networks33
Computer Networks – Practical21
13AWeb Application Development using PHP & MySQL33
Web Application Development using PHP & MySQL – Practical21
ORCyber Security33
Cyber Security – Practical21
IIIVI14AMobile Application Development33
Mobile Application Development – Practical21
ORCloud Fundamentals and Security44
15AMERN Stack33
MERN Stack – Practical21
ORDigital Forensics33
Note

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:

Note

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:

  1. Data Structures Using C, Balagurusamy E. Tata MCGraw Hill
  2. Data Structures using C, Reema Thareja, Third Edition, Oxford University Press

Reference Books:

  1. Data Structures, Lipschutz, Schaum’s Outline Series, Tata Mcgraw-hill
  2. Data Structures Using C, Ch. Vijay Kumar, Pen Press International

Data Structures using C [Practical] ​

Credits: [1 2 hrs/week]

List of Experiments

  1. 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
  1. Write a program to implement Single Linked List with insertion, deletion and traversal operations
  2. Write a program to implement Doubly Linked List with insertion, deletion and traversal operations
  3. Write a program to implement the Stack operations using Arrays and Linked Lists.
  4. Write a program to convert a given infix expression to a postfix expression using stacks.
  5. Write a program to implement the Queue operations using Arrays and Linked Lists.
  6. Write a program to implement the Circular Queue operations using Arrays.
  7. Write a program for Binary Search Tree Traversals
  8. Write a program to search an item in a given list using the following Searching Algorithms
  • a. Linear Search
  • b. Binary Search.
  1. 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:

  1. Database System Concepts, Avi Silberschatz, Henry F. Korth,S. Sudarshan, Seventh Edition, McGraw-Hill
  2. Database Management Systems by Raghu Ramakrishnan, McGrawhill

Reference Books:

  1. Fundamentals of Database Systems, Elmasri Navathe Pearson Education
  2. 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 (Example - Google AutoML, H2O.ai, Teachable Machine or similar platforms - for practice only); Desktop (No-code/Lowcode) platforms (Orange Data Mining, KNIME, Weka, RapidMiner or similar tools - for practice only).
  • Edge AI: Concept; Applications in daily life 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: Definition of public Datasets; Definition of private Datasets; Importance of
  • Public Datasets, Popular Public Dataset Repositories (Example - Kaggle, Hugging Face Datasets,
  • UCI Machine Learning Repository, Google Dataset Search or similar ones - for demonstration only), Dataset licensing and usage rights.
  • Ethics, Privacy in Data Usage: Privacy concerns related to data usage; Regulations governing data usage - GDPR, HIPAA (Overview), Ethical use of data, Responsible AI data practices.

Unit 3. The AI Data Pipeline: From Collection to Model Readiness

  • The AI Data Pipeline: Stages and Components: Key Stages (Data Collection, Annotation, Preprocessing, Splitting, Feeding into AI Models Core Components: Ingestion, Storage, Processing, Validation, Delivery
  • Data Collection Methods for AI: Manual Input (Surveys, forms, human-curated entries), Sensors & IoT Devices (Real-time data from physical environments), System Logs & Transactions, Web Scraping (Automated extraction from websites), APIs (Structured data access from external platforms)
  • Data Annotation and Labelling: Definition & Importance; Annotation Methods: Manual Annotation, Automated Annotation; Types of Annotation: Classification, Bounding Boxes, Segmentation, Transcription, Named Entity Recognition (NER).
  • Data Cleaning and Preprocessing: Importance of data cleaning; Understanding “Dirty” Data:
  • Missing Values, Duplicates, Incorrect Formats, Outliers, Noise; Steps in Data Cleaning: Identify Issues, Handle Errors (Imputation, Removal), Validate Cleaned Data.
  • Data Splitting: Splitting data into training set and test set.
  • Data Transformation Techniques: Normalization, Transformation, Feature Engineering (Conceptual)

Unit 4: AI in Biological Sciences

  • AI in Botany & Agriculture: Plant disease detection via image recognition; Crop yield forecasting using climate and soil analytics; Precision agriculture: smart irrigation and fertilizer planning
  • AI in Zoology, Ecology & Environmental Sciences: Wildlife monitoring: species ID from camera trap data; Aquatic systems: fish recognition and water quality modeling; Livestock health and disease prediction; Environmental tracking: forest cover and pollution analysis

Unit 5: AI in BioTechnology and Bio-Chemistry

  • Application of AI in Genome sequencing & gene function prediction; Using AI in Protein structure modeling (e.g., AlphaFold); AI for Drug discovery: virtual compound screening; Application of
  • AI in Microbial classification & metagenomic profiling; Chemical reaction and material property prediction
Info

There is more but I think we need to unseen that rn.

APPLICATIONS 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:

  1. Open Orange → Add File widget → Load a CSV (e.g., Titanic dataset).
  2. Connect to Data Table → View rows/columns.
  3. Connect to Data Info → Check attributes, data types.
  4. View in Data Table and Distributions widget.
  • Observation: Note numeric, categorical, missing values.
  • Outcome: Students understand structured data format in CSV.

Lab 2 - 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 3 - 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 4 - Data Cleaning and Visualization (Orange Data Mining) Aim: To clean dirty data and visualize categorical and numeric attributes.

Procedure:

  1. Load dataset.
  2. Connect File → Edit Domain (to change types) and Impute (to fill missing values).
  3. Compare cleaned vs. original in Data Table.
  4. Distributions widget.
  5. 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 5: Train/Test Split in Orange Aim: To split a dataset for AI training/testing.

Procedure:

  1. Load Titanic dataset.
  2. Connect File → Data Sampler (70% train, 30% test).
  3. Connect outputs to Data Table widgets to view.
  • Observation: Students see two different subsets.
  • Outcome: Concept of model validation using split data.

Lab 6: Plant Leaf Disease Detection

  • Dataset: Plant leaf disease datasets (PlantVillage, Kaggle).
  • Tool: Google Teachable Machine / Plantix app.
  • Activity: Upload leaf images to classify healthy vs diseased leaves.

Lab 7: Crop Yield Prediction

  • Dataset: FAO crop yield datasets.
  • Tool: Orange Data Mining (drag-and-drop AI workflows).
  • Activity: Predict yield for different crops based on soil & climate features.

Lab 8: Species Recognization

  • Dataset: Camera trap image datasets (Snapshot Serengeti, LILA BC).
  • Tool: iNaturalist / Wildbook AI platform.
  • Activity: Upload wildlife images for species recognition & conservation mapping. Lab 9: Predict and visualize 3D protein structures:
  • Dataset: Genomic & protein sequence databases (NCBI, UniProt, AlphaFold DB).
  • Tool: AlphaFold Protein Structure Viewer (online).
  • Activity: Predict and visualize 3D protein structures.

Lab 10: Analyze chemical similarity and predict drug-likeness.

  • Dataset: Drug compound datasets (ChEMBL).
  • Tool: ChemMine Tools (web-based).
  • Activity: Analyze chemical similarity and predict drug-likeness.

Lab 11: Identify microbial species from sequencing datasets.

  • Dataset: Metagenomics datasets (MG-RAST).
  • Tool: MG-RAST online platform.

Activity: Identify microbial species from sequencing datasets.

Note

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.

Books/References

  1. Data Science for Beginners, Andrew Park (Introductory concepts of data types, collection, cleaning, and visualization without coding)
  2. AI Basics for Non-Programmers, Tom Taulli (Clear explanations of AI data lifecycle and real-world use cases)
  3. Data Preparation for Machine Learning, Jason Brownlee (Conceptual understanding of dataset quality, preprocessing, and pipelines)
  4. Hands-On Data Science for Non-Programmers, David Meerman Scott (Spreadsheet-based data exploration and visualization)
  5. You Look Like a Thing and I Love You – Janelle Shane
  6. Artificial Intelligence in Life Sciences – Elsevier.
  7. Artificial Intelligence in Agriculture – CRC Press (B. Prasad).
  8. AI for Ecology and Conservation – Springer.
  9. Bioinformatics and Drug Discovery using AI – Academic Press.
  10. Databases & Platforms: FAO, GBIF, PlantVillage, ChEMBL, UniProt, AlphaFold DB.

Online Resources

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.

Sanskrit POETRY, PROSE & GRAMMER-II ​

Unit-1. प्राचीन पद्य सावित्यम् (9h)

  1. पाणणग्रिणम्- रघुिम्शमिाकाव्ये 7 सगािः
  2. पत्राचाानम्- नानाग्रन्थेभ्यिः

Unit-2. आधुवनक पद्य सावित्यम् (9h)

  1. पन्नाधात्री – श्रीमत्प्रतापरणायनेमेिाडकाण्डे13 सगािः
  2. सुखिगािः – धम्मपदम् (Sanskrit Version of Prof. P.Sriramachandrudu)

Unit-3. गद्य सावित्यम् (9h)

  1. अमोघदशानम् - बाणस्य कादंबरीतिः
  2. चारुचेवितम् - कविकोपकलापत िः

Unit-4. व्याकरणम् (9h)

  1. अजन्त शब्ािः (नदी, तनु, िधू, मातृ, िन, फल, िारर, मधु)
  2. धातििः (इष्, णलख्, कृञ्, क्रीञ्, चुर, ् रमु, िन्द्, युध्)

Unit-5: व्याकरणम् (9h)

  1. सन्धयिः (िल्सन्न्धिः – विसगासन्न्धिः)
  2. समासािः (अव्यायीभाििः, बहुव्रीवििः)
Info

Sub list: (lang-I) English, (Lag-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.

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