CO OF COMPUTER APPLICATION - RAJAGIRI VISWAJYOTHI | Empowering Minds. Shaping Futures.

CO

Semester 1

Fundamentals of Programming Using C

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Demonstrate basic programming concepts.	U	1
CO2	Understand C Programming basics such as data types, variables and operators.	U	2
CO3	Devise C programs using decision and loop control statements.	An	2
CO4	Apply logic using arrays and functions in C.	Ap	3

Digital Fundamentals

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Demonstrate comprehension of number systems.	U,A	2
CO2	Analyse logic gates and Boolean algebra.	An,A	1,2
CO3	Illustrate combinational circuits.	U,An	1,3
CO4	Design sequential circuits.	An,A	1,2

Software Lab in C

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Develop programs using control structures, arrays and functions.	A	1

Discrete Mathematics

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Understand Set Theory.	U	1
CO2	Evaluate Set Theory problems.	E	2
CO3	Understand Propositional Logic.	U	2
CO4	Apply Propositional Logic.	A	3
CO5	Evaluate truth tables.	E	3
CO6	Analyse relations and functions.	An	2
CO7	Understand Matrix concepts.	U	2
CO8	Evaluate inverse matrices.	E	2

Cyber Laws and Security

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Describe cyber laws and cyber crimes.	U	1
CO2	Apply online security measures.	An	1
CO3	Illustrate cryptographic techniques.	U	2

Semester 2

1. Mathematics Foundations to Computer Science

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Apply concepts of Graph Theory to solve real-life problems.	A	1
CO2	Apply numerical methods to approximate solutions to mathematical problems.	A	1,2
CO3	Understand concepts of Linear Programming and Operations Research and apply using graphical and simplex methods.	A	1,2
CO4	Formulate and solve transportation problems.	C	1,2

2. Data Structures

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Understand concepts of Data Structures and array representations.	An	1
CO2	Analyse stacks and queues implementation and applications.	An	1,2
CO3	Implement singly, doubly and circular linked lists.	An	1,2
CO4	Implement Data Structures using C.	An	1,2

3. Operating Systems

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Describe structure, types and services of Operating System.	U	1
CO2	Analyse process scheduling algorithms.	An	2
CO3	Appraise process synchronization and deadlock handling.	An	2
CO4	Analyse memory management techniques.	An	2

4. Web Technologies

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Apply HTML and CSS to develop interactive web pages.	A	1
CO2	Implement JavaScript, DOM manipulation and AJAX.	A	2
CO3	Develop responsive and interactive web applications.	A	2

5. Indian Constitution: Legal and Ethical Perspectives for IT

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Understand fundamental principles of Indian Constitution.	U	6
CO2	Explain legal framework governing IT and cybersecurity.	An	1
CO3	Analyze ethical implications of emerging technologies.	An	7

Semester 3

1. Quantitative Techniques

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Describe the fundamental concepts of statistics, including data types, collection methods and representation techniques to analyse and interpret data effectively.	U	1
CO2	Compute and interpret central tendency and dispersion measures to summarize datasets and assess variability.	An	1,2
CO3	Evaluate relationships between variables using correlation coefficients and regression models.	A	1,2
CO4	Apply probability concepts to solve real-world problems involving uncertainty.	A	1,2

2. Database Management Systems

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Analyse the basic concepts of DBMS.	An	1
CO2	Develop proficiency in database design and SQL.	An	2
CO3	Understand normalization and transaction management.	An	2
CO4	Analyse MongoDB database operations.	An	2
CO5	Implement SQL queries and administer MongoDB databases.	A	2

3. Software Engineering

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Illustrate software development lifecycle and contemporary software engineering practices.	An	1
CO2	Analyse project management methodologies and strategic decision making.	An	1,2
CO3	Analyse software design, development and testing processes.	An	1,2

4. Design and Analysis of Algorithms

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Illustrate algorithm design paradigms and analyse algorithm performance.	An	1
CO2	Analyse divide and conquer and greedy methods and apply them to real-life problems.	An	2
CO3	Synthesize algorithms using dynamic programming and backtracking approaches.	An	2

5. Python Programming

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Analyse Python programming concepts.	An	1
CO2	Apply Python constructs and built-in data structures to solve problems.	An	2
CO3	Analyse data visualization and file handling in Python.	An	2
CO4	Solve problems using Python programming.	A	2

6. Feature Engineering

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Understand importance of features in machine learning and differentiate data types.	U	1
CO2	Apply preprocessing techniques including missing data handling, cleaning and normalization.	A	2
CO3	Implement feature engineering techniques including binning, polynomial features and transformations.	A	2
CO4	Utilize categorical data techniques and feature selection methods.	A	2
CO5	Perform feature transformation using PCA and understand its applications.	An	2

Semester 4

1. Artificial Intelligence

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Describe the characteristics of rational agents and gain insights about problem-solving agents.	An	1,2
CO2	Analyse uninformed and informed search techniques.	An	1,2
CO3	Apply knowledge representation using propositional logic and predicate calculus for inference and uncertainty handling.	An	1,2,3
CO4	Illustrate AI domains, applications and examine legal and ethical issues of AI.	An	2
CO5	Apply search strategies, solve constraint-based problems and use NLP techniques in intelligent systems.	E	1,2

2. Entrepreneurship and Startup Ecosystem

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Understand family businesses and identify business opportunities to create viable business models.	An	1,3
CO2	Understand venture creation building blocks and Indian entrepreneurship ecosystem benefits.	An	1,3

3. IT and Environmental Sustainability

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Describe environment components, natural resources and sustainable conservation practices.	U	1,3
CO2	Identify pollution types, SDGs and environmental laws and their impacts.	An	1,3,6
CO3	Explain social issues, environmental laws and population dynamics for sustainable development.	An	1,6,7,8

4. Object Oriented Programming using Java

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Understand fundamental concepts of object-oriented programming using Java.	U	2
CO2	Utilize arrays, strings, vectors, wrapper classes and inheritance in Java.	An	2
CO3	Utilize packages, exceptions and threads in Java programming.	An	2
CO4	Apply Java programming concepts, multithreading and exceptions for problem solving.	An	2
CO5	Understand GUI and JDBC architecture and develop Java GUI database applications.	A	2

5. Probability Distributions and Statistical Inference

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Analyse random variables, probability distributions and statistical moments.	An	1
CO2	Apply theoretical distributions to model real-world data and solve probability problems.	A	1,2
CO3	Describe sampling distributions and their interrelationships.	U	1,2
CO4	Illustrate hypothesis testing concepts including p-value, power and distribution tests.	A	1,2

6. Design Thinking and Innovation

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Propose innovative product designs and choose suitable prototype development frameworks.	An	1,3
CO2	Understand wicked problems and stakeholder consensus frameworks.	An	1
CO3	Analyse emotional experience and expressions for user-centred product design.	An	1,3

7. Introduction to Machine Learning

CO No.	Expected Course Outcome	Learning Domains	PO No.
CO1	Define and explain machine learning concepts, types and metrics.	An	1
CO2	Understand supervised and unsupervised learning techniques.	An	1
CO3	Implement supervised learning techniques and evaluate performance metrics.	A	1
CO4	Apply and visualize clustering algorithms including K-Means, hierarchical clustering and DBSCAN.	A	2,3
CO5	Perform dimensionality reduction using PCA and interpret results.	A	2,3
CO6	Develop and assess classification models using random forests, SVM and neural networks.	A	2,3
CO7	Demonstrate ensemble learning concepts using bagging and AdaBoost.	A	2,3