| III SEM | BS-CS-AIDS-201A :Mathematics for Big Data & Optimization | |||||||||||||
| CO1 | Demonstrate understanding of basic mathematical concepts in data science, relating to linear algebra, probability, and calculus | |||||||||||||
| CO2 | Employ methods related to these concepts in a variety of data science applications | |||||||||||||
| CO3 | Apply logical thinking to problem-solving in context. | |||||||||||||
| CO4 | Use appropriate technology to aid problem-solving and data analysis | |||||||||||||
| Mapping | BS-CS-AIDS-201A :Mathematics for Big Data & Optimization | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | – | 2 | 1 |
| CO2 | 3 | 3 | 1 | 2 | 1 | – | – | – | – | – | – | – | 1 | 2 |
| CO3 | 3 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 1 |
| CO4 | 2 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 3 | 2 |
| WT. AVG | 2.75 | 2.75 | 1.75 | 2.25 | 1.75 | – | – | – | – | – | – | – | 2 | 1.5 |
| Overall Mapping of Subject | 2.11 | |||||||||||||
| III SEM | PC-CS-AIDS- 203A: Object-Oriented Programming | |||||||||||||
| CO1 | To introduce the basic concepts of object oriented programming language and the its representation. | |||||||||||||
| CO2 | To allocate dynamic memory, access private members of class and the behavior of inheritance and its implementation. | |||||||||||||
| CO3 | To introduce polymorphism, interface design and overloading of operator. | |||||||||||||
| CO4 | To handle backup system using file, general purpose template and handling of raised exception during programming. | |||||||||||||
| Mapping | PC-CS-AIDS- 203A: Object-Oriented Programming | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 1 | – | 1 | – | – | – | – | – | – | 2 | 2 | 2 |
| CO2 | 3 | 2 | 1 | – | 2 | – | – | – | – | – | – | 2 | 2 | 1 |
| CO3 | 3 | 2 | 2 | 2 | 3 | – | – | – | – | – | – | 3 | 3 | 1 |
| CO4 | 3 | 2 | 1 | 1 | 2 | – | – | – | – | – | – | 2 | 2 | 1 |
| WT. AVG | 3 | 2 | 1.25 | 1.5 | 2 | – | – | – | – | – | – | 2.25 | 2.25 | 1.25 |
| Overall Mapping of Subject | 1.94 | |||||||||||||
| III SEM | PC-CS-AIDS- 205A: Data Structures & Algorithms | |||||||||||||
| CO1 | To introduce the basic concepts of Data structure , basic data types ,searching and sorting based on array data types. | |||||||||||||
| CO2 | To introduce the structured data types like Stacks and Queue and its basic operations’ implementation. | |||||||||||||
| CO3 | To introduce dynamic implementation of linked list. | |||||||||||||
| CO4 | To introduce the concepts of Tree and graph and implementation of traversal algorithms. | |||||||||||||
| Mapping | PC-CS-AIDS- 205A: Data Structures & Algorithms | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 3 | 1 | 2 | – | – | – | – | – | – | 3 | 2 | 2 |
| CO2 | 3 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | 3 | 3 | 2 |
| CO3 | 3 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | 3 | 3 | 3 |
| CO4 | 3 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | 3 | 3 | 2 |
| WT. AVG | 3 | 2.75 | 3 | 1.75 | 2 | – | – | – | – | – | – | 3 | 2.75 | 2.25 |
| Overall Mapping of Subject | 2.56 | |||||||||||||
| III SEM | PC-CS-AIDS- 207A: Introduction to Artificial Intelligence | |||||||||||||
| CO1 | Demonstrate fundamental understanding of Artificial Intelligence (AI) and its foundation | |||||||||||||
| CO2 | Apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning | |||||||||||||
| CO3 | Demonstrate proficiency in applying scientific method to models of machine learning | |||||||||||||
| CO4 | Demonstrate an ability to share in discussions of AI, its current scope and limitations, and societal implications | |||||||||||||
| Mapping | PC-CS-AIDS- 207A: Introduction to Artificial Intelligence | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 1 | 2 | 1 | – | – | – | – | – | – | 2 | 1 | 2 |
| CO2 | 2 | 2 | 1 | 1 | 1 | – | – | – | – | – | – | 2 | 2 | 1 |
| CO3 | 2 | 2 | 1 | 2 | 2 | – | – | – | – | – | – | 2 | 2 | 2 |
| CO4 | 2 | 2 | 1 | 2 | 1 | – | – | – | – | – | – | 2 | 2 | 1 |
| WT. AVG | 2 | 2 | 1 | 1.75 | 1.25 | – | – | – | – | – | – | 2 | 1.75 | 1.5 |
| Overall Mapping of Subject | 1.66 | |||||||||||||
| III SEM | PC-CS-AIDS- 209A: Programming Languages | |||||||||||||
| CO1 | To introduce the basic concepts of programming language, the general problems and methods related to syntax and semantics. | |||||||||||||
| CO2 | To introduce the structured data objects, subprograms and programmer defined data types. | |||||||||||||
| CO3 | To outline the sequence control and data control. | |||||||||||||
| CO4 | To introduce the concepts of storage management using programming languages. | |||||||||||||
| Mapping | PC-CS-AIDS- 209A: Programming Languages | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 1 | 2 | 1 | 1 | – | – | 1 | 1 | 1 | 1 | 1 | 3 | 2 |
| CO2 | 3 | 2 | 1 | 1 | 1 | – | – | 1 | 1 | 1 | 1 | 1 | 3 | 2 |
| CO3 | 3 | 1 | 1 | 1 | 1 | – | – | 1 | 1 | 1 | 1 | 2 | 3 | 2 |
| CO4 | 3 | 2 | 1 | 1 | 1 | – | – | 1 | 1 | 1 | 1 | 2 | 3 | 2 |
| WT. AVG | 3 | 1.5 | 1.25 | 1 | 1 | – | – | 1 | 1 | 1 | 1 | 1.5 | 3 | 2 |
| Overall Mapping of Subject | 1.52 | |||||||||||||
| III SEM | HM-902: Business Intelligence and Entrepreneurship | |||||||||||||
| CO1 | Students will be able understand who the entrepreneurs are and what competences needed to become an Entrepreneur. | |||||||||||||
| CO2 | Students will be able understand insights into the management, opportunity search, identification of a Product; market feasibility studies; project finalization etc. required for small business enterprises. | |||||||||||||
| CO3 | Students can be able to write a report and do oral presentation on the topics such as product identification, business idea, export marketing etc. | |||||||||||||
| CO4 | Students will be able to know the different financial and other assistance available for the small industrial units. | |||||||||||||
| Mapping | HM-902: Business Intelligence and Entrepreneurship | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 1 | 1 | 1 | 1 | 1 | – | – | – | – | – | – | – | 2 | 1 |
| CO2 | 1 | 1 | 2 | 1 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO3 | 1 | 2 | 1 | 1 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 2 | 1 | 1 | 1 | 1 | – | – | – | – | – | – | – | 2 | 1 |
| WT. AVG | 1.25 | 1.25 | 1.25 | 1 | 1.5 | – | – | – | – | – | – | – | 1.75 | 1.5 |
| Overall Mapping of Subject | 1.36 | |||||||||||||
| III SEM | PC-CS-AIDS213LA: Data Structure & Algorithms Lab | |||||||||||||
| CO1 | Implement linear and non linear data structures using linked list | |||||||||||||
| CO2 | Apply various data structures such as stack, queue and tree to solve the problems. | |||||||||||||
| CO3 | Implement various searching and sorting techniques | |||||||||||||
| CO4 | Choose appropriate data structure while designing the applications and analyze the complexity of the algorithms. | |||||||||||||
| Mapping | PC-CS-AIDS213LA: Data Structure & Algorithms Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 3 | 3 | 3 | – | – | – | – | – | – | – | 2 | 1 |
| CO2 | 2 | 3 | 2 | 3 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 2 | 2 | 1 | 2 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO4 | 2 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 2.25 | 2.25 | 2 | 2.75 | 2.25 | – | – | – | – | – | – | – | 1.75 | 1.75 |
| Overall Mapping of Subject | 2.14 | |||||||||||||
| III SEM | PC-CS-AIDS215LA: Artificial Intelligence Lab | |||||||||||||
| CO1 | To understand the basic concepts of Artificial Intelligence. | |||||||||||||
| CO2 | To apply various AI Search algorithms. | |||||||||||||
| CO3 | To understand the fundamentals of knowledge representation and theorem proving using AI tools. | |||||||||||||
| CO4 | Ability to apply knowledge representation and machine learning techniques to real life problems. | |||||||||||||
| Mapping | PC-CS-AIDS215LA: Artificial Intelligence Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 1 | 2 | 1 | – | – | – | – | – | – | 2 | 1 | 2 |
| CO2 | 2 | 2 | 1 | 1 | 1 | – | – | – | – | – | – | 2 | 2 | 1 |
| CO3 | 2 | 2 | 1 | 2 | 2 | – | – | – | – | – | – | 2 | 2 | 2 |
| CO4 | 2 | 2 | 1 | 2 | 1 | – | – | – | – | – | – | 2 | 2 | 1 |
| WT. AVG | 2 | 2 | 1 | 1.75 | 1.25 | – | – | – | – | – | – | 2 | 1.75 | 1.5 |
| Overall Mapping of Subject | 1.66 | |||||||||||||
| III SEM | PC-CS-AIDS217LA : Object Oriented Programming Lab | |||||||||||||
| CO1 | Implement object oriented concepts such as objects,classes abstraction and message passing. | |||||||||||||
| CO2 | Implement the friend function ,function overloading and virtual function | |||||||||||||
| CO3 | Implement Operator overloading, Inheritance and method overriding. | |||||||||||||
| CO4 | Implement the various functions on String and apply I/O operation to handle file system | |||||||||||||
| Mapping | PC-CS-AIDS217LA : Object Oriented Programming Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 1 | – | 1 | – | – | – | – | – | – | 2 | 2 | 2 |
| CO2 | 3 | 2 | 1 | – | 2 | – | – | – | – | – | – | 2 | 2 | 1 |
| CO3 | 3 | 2 | 2 | 2 | 3 | – | – | – | – | – | – | 3 | 3 | 1 |
| CO4 | 3 | 2 | 1 | 1 | 2 | – | – | – | – | – | – | 2 | 2 | 1 |
| WT. AVG | 3 | 2 | 1.25 | 1.5 | 2 | – | – | – | – | – | – | 2.25 | 2.25 | 1.25 |
| Overall Mapping of Subject | 1.94 | |||||||||||||
| IV SEM | BS-AIDS- 202A: Bayesian Data Analysis | |||||||||||||
| CO1 | Demonstrate fundamental understanding of Bayesian Inference and models | |||||||||||||
| CO2 | Understand and apply Bayesian statistics, posterior inference and decision analysis for making Bayesian models. | |||||||||||||
| CO3 | Demonstrate Computation, approximation and simulating from probability distributions in Bayesian analysis | |||||||||||||
| CO4 | Understand Bayesian forms of the standard statistical models | |||||||||||||
| Mapping | BS-AIDS- 202A: Bayesian Data Analysis | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 1 | 1 | 1 | 1 | – | – | – | – | – | – | – | 3 | 2 |
| CO2 | 2 | 2 | 1 | 1 | 1 | – | – | – | – | – | – | – | 3 | 1 |
| CO3 | 1 | 1 | 1 | 2 | 2 | – | – | – | – | – | – | – | 2 | 1 |
| CO4 | 1 | 1 | 1 | 1 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 1.75 | 1.25 | 1 | 1.25 | 1.5 | – | – | – | – | – | – | – | 2.5 | 1.5 |
| Overall Mapping of Subject | 1.54 | |||||||||||||
| IV SEM | PC-CS-AIDS- 204A: Data Science and R Programming | |||||||||||||
| CO1 | Basics of Data Science, Explain basic Statistics. Identify probability distributions commonly used as foundations for statistical modeling. Fit a model to data. | |||||||||||||
| CO2 | Using R to carry out basic statistical modeling and analysis. | |||||||||||||
| CO3 | Explain the significance of exploratory data analysis (EDA) in data science. Apply basic tools (plots, graphs, summary statistics) to carry out EDA. | |||||||||||||
| CO4 | Describe the Data Science Process and how its components interact via machine learning models. | |||||||||||||
| Mapping | PC-CS-AIDS- 204A: Data Science and R Programming | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 3 | 1 | 3 | – | 2 | 2 | 0 | 2 | – | 1 | 3 | 2 |
| CO2 | 3 | 2 | 3 | 2 | 3 | – | 1 | 2 | 0 | 2 | – | 1 | 2 | 2 |
| CO3 | 3 | 2 | 3 | 2 | 3 | – | 1 | 2 | 0 | 2 | – | 2 | 2 | 2 |
| CO4 | 3 | 2 | 3 | 2 | 2 | – | 1 | 2 | 0 | 2 | – | 2 | 3 | 2 |
| WT. AVG | 3 | 2 | 3 | 1.75 | 2.75 | – | 1.25 | 2 | 0 | 2 | – | 1.5 | 2.5 | 2 |
| Overall Mapping of Subject | 2 | |||||||||||||
| IV SEM | ES-CS-AIDS-206A: Intelligent Communication Systems | |||||||||||||
| CO1 | To be able to understand the theoretical basis of communication system process | |||||||||||||
| CO2 | Demonstrate the concept of Information theory and describe various sources available to transfer the information. | |||||||||||||
| CO3 | To have the in depth knowledge of the Communication Network Structure and study various protocols. | |||||||||||||
| CO4 | To deal with the practical aspect of intelligent communication system. | |||||||||||||
| Mapping | ES-CS-AIDS-206A: Intelligent Communication Systems | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 1 | 1 | 1 | – | – | – | – | – | – | – | – | 3 | 1 |
| CO2 | 2 | 1 | 1 | – | – | – | – | – | – | – | – | – | 2 | 1 |
| CO3 | 1 | 1 | 2 | 1 | – | – | – | – | – | – | – | – | 3 | 1 |
| CO4 | 1 | 3 | 3 | 2 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 1.75 | 1.5 | 1.75 | 1.33333 | 3 | – | – | – | – | – | – | – | 2.5 | 1.25 |
| Overall Mapping of Subject | 2 | |||||||||||||
| IV SEM | PC-CS-AIDS- 208A: Internet & Web technology | |||||||||||||
| CO1 | Learn the basic concepts of information and web architecture. | |||||||||||||
| CO2 | Learn about the skills that will enable to design and build high level web enabled applications. | |||||||||||||
| CO3 | Understand the applicability of Java Script as per current software industry standards. | |||||||||||||
| CO4 | Acquaint the latest programming language for the implementation of object based and procedure based applications using Python. | |||||||||||||
| Mapping | PC-CS-AIDS- 208A: Internet & Web technology | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 1 | 2 | 2 | – | – | – | – | – | – | – | – | 3 | 2 |
| CO2 | 1 | 1 | 3 | 1 | – | – | – | – | – | – | – | – | 2 | 1 |
| CO3 | 1 | 3 | 2 | 2 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 2 | 2 | 2 | – | 3 | – | – | – | – | – | – | – | 2 | 1 |
| WT. AVG | 1.75 | 1.75 | 2.25 | 1.66667 | 3 | – | – | – | – | – | – | – | 2.25 | 1.5 |
| Overall Mapping of Subject | 2.02 | |||||||||||||
| IV SEM | PC-CS-AIDS- 210A: Data Base Management Systems | |||||||||||||
| CO1 | To provide introduction to relational model. | |||||||||||||
| CO2 | To learn about ER diagrams and SQL. | |||||||||||||
| CO3 | To understand about the concept of functional dependencies. | |||||||||||||
| CO4 | To understand about Query Processing and Transaction Processing. | |||||||||||||
| Mapping | PC-CS-AIDS- 210A: Data Base Management Systems | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 1 | 2 | 1 | 2 | – | – | – | – | – | – | – | 3 | 2 |
| CO2 | 2 | 1 | 1 | 1 | 3 | – | – | – | – | – | – | – | 3 | 2 |
| CO3 | 1 | 2 | 2 | 1 | 2 | – | – | – | – | – | – | – | 2 | 1 |
| CO4 | 2 | 2 | 2 | 1 | 2 | – | – | – | – | – | – | – | 2 | 1 |
| WT. AVG | 1.75 | 1.5 | 1.75 | 1 | 2.25 | – | – | – | – | – | – | – | 2.5 | 1.5 |
| Overall Mapping of Subject | 1.75 | |||||||||||||
| IV SEM | PC-CS-AIDS- 212A: OPERATING SYSTEMS | |||||||||||||
| CO1 | To understand the structure and functions of Operating system. | |||||||||||||
| CO2 | To learn about processes, threads and scheduling algorithms. | |||||||||||||
| CO3 | To understand the principle of concurrency. | |||||||||||||
| CO4 | To understand the concept of deadlocks. | |||||||||||||
| CO5 | To learn various memory management schemes. | |||||||||||||
| CO6 | To study I/O management and file systems. | |||||||||||||
| CO7 | To study the concept of protection and security. | |||||||||||||
| Mapping | PC-CS-AIDS- 212A: OPERATING SYSTEMS | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 1 | 1 | – | – | – | – | – | – | – | – | 3 | 2 |
| CO2 | 1 | – | 1 | 1 | – | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 2 | 1 | 1 | 1 | – | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 2 | – | – | 1 | – | – | – | – | – | – | – | – | 3 | 1 |
| CO5 | 2 | 1 | 1 | 1 | – | – | – | – | – | – | – | – | 2 | 1 |
| CO6 | 2 | 2 | 1 | 1 | – | – | – | – | – | – | – | – | 2 | 2 |
| CO7 | 2 | 1 | 1 | 1 | – | – | – | – | – | – | – | – | 2 | 1 |
| WT. AVG | 2 | 1.4 | 1 | 1 | – | – | – | – | – | – | – | – | 2.29 | 1.57 |
| Overall Mapping of Subject | 1.54 | |||||||||||||
| IV SEM | PC-CS-AIDS-214LA : R Lab | |||||||||||||
| CO1 | Install and use R for simple programming tasks. Extend the functionality of R by using add-on packages. | |||||||||||||
| CO2 | Extract data from files and other sources and perform various data manipulation tasks on them. 4. Code statistical functions in R. | |||||||||||||
| CO3 | Use R Graphics and Tables to visualize results of various statistical operations on data . | |||||||||||||
| CO4 | Apply the knowledge of R gained to data Analytics for real life applications. | |||||||||||||
| Mapping | PC-CS-AIDS-214LA : R Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 1 | 2 | 2 | 1 | 2 | – | – | – | – | – | – | – | – | 1 |
| CO2 | 2 | 2 | 2 | 2 | 3 | – | – | – | – | – | – | – | 2 | 1 |
| CO3 | 2 | 3 | 1 | 1 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO4 | 2 | 2 | 2 | 2 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| WT. AVG | 1.75 | 2.25 | 1.75 | 1.5 | 2.25 | – | – | – | – | – | – | – | 1.33 | 1.5 |
| Overall Mapping of Subject | 1.76 | |||||||||||||
| IV SEM | ES-CS-AIDS-216LA :Internet & Web Technology Lab | |||||||||||||
| CO1 | Design webpages using HTML, JavaScript and CSS. | |||||||||||||
| CO2 | Design and test simple function/program to implement Searching and sorting techniques using Python. | |||||||||||||
| CO3 | Develop program in Java Script for pattern matching using regular expressions and errors in scripts | |||||||||||||
| CO4 | Design client-server based web applications. | |||||||||||||
| Mapping | ES-CS-AIDS-216LA :Internet & Web Technology Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 1 | 2 | 2 | – | – | – | – | – | – | – | – | 3 | 2 |
| CO2 | 1 | 1 | 3 | 1 | – | – | – | – | – | – | – | – | 2 | 1 |
| CO3 | 1 | 3 | 2 | 2 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 2 | 2 | 2 | – | 3 | – | – | – | – | – | – | – | 2 | 1 |
| WT. AVG | 1.75 | 1.75 | 2.25 | 1.67 | 3 | – | – | – | – | – | – | – | 2.25 | 1.5 |
| Overall Mapping of Subject | 2.02 | |||||||||||||
| IV SEM | PC-CS-AIDS-218LA :Database Management Systems Lab | |||||||||||||
| CO1 | To understand& Implement basic DDL commands. | |||||||||||||
| CO2 | To learn & Implement DML and DCL commands. | |||||||||||||
| CO3 | To understand the SQL queries using SQL operators. | |||||||||||||
| CO4 | To understand the concept of relational algebra and implement using examples. | |||||||||||||
| Mapping | PC-CS-AIDS218LA :Database Management Systems Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 1 | 2 | 1 | 2 | – | – | – | – | – | – | – | 3 | 2 |
| CO2 | 2 | 1 | 1 | 1 | 3 | – | – | – | – | – | – | – | 3 | 2 |
| CO3 | 1 | 2 | 2 | 1 | 2 | – | – | – | – | – | – | – | 2 | 1 |
| CO4 | 2 | 2 | 2 | 1 | 2 | – | – | – | – | – | – | – | 2 | 1 |
| WT. AVG | 1.75 | 1.5 | 1.75 | 1 | 2.25 | – | – | – | – | – | – | – | 2.5 | 1.5 |
| Overall Mapping of Subject | 1.75 | |||||||||||||
| IV SEM | MC-901A: Environmental Sciences | |||||||||||||
| CO1 | The students will be able to learn the importance of natural resources. | |||||||||||||
| CO2 | To learn the theoretical and practical aspects of eco system. | |||||||||||||
| CO3 | Will be able to learn the basic concepts of conservation of biodiversity. | |||||||||||||
| CO4 | The students will be able to understand the basic concept of sustainable development. | |||||||||||||
| Mapping | MC-901A: Environmental Sciences | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 1 | 1 | 1 | 1 | – | – | 2 | – | – | – | – | 1 | 1 | 1 |
| CO2 | 2 | 1 | 1 | 1 | – | – | 2 | – | – | – | – | 2 | 1 | 2 |
| CO3 | 2 | 2 | 1 | 1 | – | – | 2 | – | – | – | – | 1 | 1 | – |
| CO4 | 1 | 2 | 1 | 2 | 1 | – | 2 | – | – | – | – | – | 2 | 1 |
| WT. AVG | 1.5 | 1.5 | 1 | 1.25 | 1 | – | 2 | – | – | – | – | 1.33 | 1.25 | 1.33 |
| Overall Mapping of Subject | 1.35 | |||||||||||||
| V SEM | PC-CS-AIDS-301A:Theory of Computation | |||||||||||||
| CO1 | Students are able to explain and manipulate the different fundamental concepts in automata theory and formal languages. | |||||||||||||
| CO2 | Simplify automata and context-free grammars; Prove properties of languages, grammars and automata with rigorously formal mathematical methods, minimization. | |||||||||||||
| CO3 | Differentiate and manipulate formal descriptions of push down automata, its applications and transducer machines. | |||||||||||||
| CO4 | To understand basic properties of Turing machines and computing with Turing machine, the concepts of tractability and decidability. | |||||||||||||
| Mapping | PC-CS-AIDS-301A:Theory of Computation | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 3 | 3 | 1 | 2 | – | – | – | – | – | – | 1 | 3 | 3 |
| CO2 | 3 | 3 | 3 | 1 | 2 | – | – | – | – | – | – | 1 | 3 | 2 |
| CO3 | 3 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | 2 | 2 | 2 |
| CO4 | 3 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | 2 | 3 | 3 |
| CO5 | 3 | 2 | 3 | 2 | 2 | – | – | – | – | – | – | 2 | 3 | 2 |
| WT. AVG | 3 | 2.8 | 3 | 1.6 | 2 | – | – | – | – | – | – | 1.6 | 2.8 | 2.4 |
| Overall Mapping of Subject | 2.4 | |||||||||||||
| V SEM | PC-CS-AIDS-303A: Design and Analysis of Algorithms | |||||||||||||
| CO1 | To introduce the basic concepts of Data Structures and their analysis. | |||||||||||||
| CO2 | To study the concept of Dynamic Programming and various advanced Data Structures. | |||||||||||||
| CO3 | To introduce various Graph algorithms and concepts of Computational complexities. | |||||||||||||
| CO4 | To study various Flow and Sorting Networks | |||||||||||||
| Mapping | PC-CS-AIDS-303A: Design and Analysis of Algorithms | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 3 | 2 | 3 | – | – | – | – | – | – | – | – | 2 | 2 |
| CO2 | 3 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 3 | 3 | 3 | 3 | 1 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 3 | 3 | 3 | 3 | 1 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 3 | 3 | 2.5 | 2.75 | 1.33 | – | – | – | – | – | – | – | 2 | 2 |
| Overall Mapping of Subject | 2.37 | |||||||||||||
| V SEM | ES-CS-AIDS-305A: Computer Network | |||||||||||||
| CO1 | To understand the basic concept of networking, types, networking topologies and layered architecture. | |||||||||||||
| CO2 | To understand datalink layer and MAC sub-layer` | |||||||||||||
| CO3 | To understand the network Layer functioning | |||||||||||||
| CO4 | To understand the transport layer and application layer operation | |||||||||||||
| Mapping | ES-CS-AIDS-305A: Computer Network | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 2 | 1 | 2 | – | – | – | – | 2 | – | 3 | 2 | 2 |
| CO2 | 3 | 2 | 1 | 2 | 1 | – | – | – | – | – | – | – | 2 | – |
| CO3 | 3 | 1 | 2 | 1 | 1 | – | – | – | – | – | – | – | 2 | 1 |
| CO4 | 3 | 1 | 1 | 2 | 2 | – | – | – | – | – | – | – | 2 | – |
| WT. AVG | 3 | 1.5 | 1.5 | 1.5 | 1.5 | – | – | – | – | 2 | 3 | 2 | 1.5 | |
| Overall Mapping of Subject | 1.94 | |||||||||||||
| V SEM | PC-CS-AIDS-307A: Machine Learning with using Python | |||||||||||||
| CO1 | Understand basics of Python programming language. | |||||||||||||
| CO2 | Explain the operation of different supervised and unsupervised algorithms and their implementation in Python. | |||||||||||||
| CO3 | Implement several clustering, classification and regression algorithms, and apply a suitable learning algorithm to arrange of basic problems. | |||||||||||||
| CO4 | Work on Recommender Systems: Content-Based and Collaborative Filtering | |||||||||||||
| CO5 | Use and Analyze Popular models: Train/Test Split, Gradient Descent, and Mean Squared Error and perform custom analysis | |||||||||||||
| CO6 | Apply predictions and segmentation on real-world datasets. Interpret the output and validity of a learning algorithm. | |||||||||||||
| Mapping | PC-CS-AIDS-307A: Machine Learning with using Python | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 2 | 1 | – | – | – | – | – | – | 1 | 2 | 3 | 3 |
| CO2 | 1 | 1 | 2 | 2 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO3 | 1 | 3 | 2 | 1 | 2 | – | – | – | – | – | – | – | 2 | 3 |
| CO4 | 1 | 1 | 3 | 2 | 3 | – | – | – | – | – | – | – | 3 | 3 |
| CO5 | 2 | 3 | 3 | 3 | 3 | – | – | – | – | – | – | – | 3 | 3 |
| CO6 | 2 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | – | 3 | 3 |
| WT. AVG | 1.67 | 2.17 | 2.5 | 1.83 | 2.4 | – | – | – | – | – | 1 | 2 | 2.5 | 2.83 |
| Overall Mapping of Subject | 2.1 | |||||||||||||
| V SEM | ES-CS-AIDS-309A: Computer Architecture | |||||||||||||
| CO1 | Be familiar with the internal organization and operations of a computer. | |||||||||||||
| CO2 | Be familiar with the design trade‐offs in designing and constructing a computer processor. | |||||||||||||
| CO3 | Be aware with the CPU design including the RISC/CISC architectures. | |||||||||||||
| CO4 | Be acquainted with the basic knowledge of I/O devices and select the appropriate interfacing standards for I/O devices. | |||||||||||||
| Mapping | ES-CS-AIDS-309A: Computer Architecture | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 1 | – | – | – | – | – | – | – | – | – | 2 | – | – |
| CO2 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | – | 2 | – | – |
| CO3 | 3 | 3 | 3 | 2 | – | – | – | – | – | – | – | 2 | – | – |
| CO4 | 3 | – | 2 | 2 | – | – | – | – | – | – | – | 2 | – | – |
| CO5 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | – | – |
| CO6 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | – | – |
| WT. AVG | 2.83 | 2 | 2.4 | 2 | – | – | – | – | – | – | – | 2 | – | – |
| Overall Mapping of Subject | 2.25 | |||||||||||||
| V SEM | PC-CS-AIDS-313LA: Artificial Neural Networks Lab | |||||||||||||
| CO1 | Implement cognitive tasks and processing of sensorial data such as vision, image- And speech recognition, control, robotics, expert systems. |
|||||||||||||
| CO2 | Design single and multi-layer feed-forward neural networks | |||||||||||||
| CO3 | Understand and implement supervised and unsupervised learning concepts & Understand unsupervised learning using Kohonen networks |
|||||||||||||
| CO4 | Implement training of recurrent Hop field networks and associative memory concepts. | |||||||||||||
| Mapping | PC-CS-AIDS-311A: Artificial Neural Networks | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 1 | 2 | 2 | 2 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO2 | 2 | 3 | 1 | 1 | 1 | – | – | – | – | – | – | – | 2 | 3 |
| CO3 | 2 | 2 | 3 | 1 | 2 | – | – | – | – | – | – | 1 | 2 | |
| CO4 | 2 | 2 | 1 | 2 | 2 | – | – | – | – | – | – | – | – | – |
| CO5 | 2 | 2 | 1 | 1 | 2 | – | 3 | – | – | – | – | – | – | – |
| WT. AVG | 1.8 | 2.2 | 1.6 | 1.4 | 1.8 | – | 3 | – | – | – | – | – | 1.33 | 2.33 |
| Overall Mapping of Subject | 1.93 | |||||||||||||
| V SEM | PC-CS-AIDS-311A: Artificial Neural Networks | |||||||||||||
| CO1 | Understand basic principles of neuron structure. | |||||||||||||
| CO2 | Understand and explain the mathematical foundations of neural network models | |||||||||||||
| CO3 | Understand and apply the methods of training neural networks; | |||||||||||||
| CO4 | Implement and analyze different algorithms for learning. | |||||||||||||
| CO5 | Formalize the problem to solve it by using a neural network. Via implementation of these techniques in MATLAB. | |||||||||||||
| Mapping | PC-CS-AIDS-311A: Artificial Neural Networks | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 1 | 2 | 2 | 2 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO2 | 2 | 3 | 1 | 1 | 1 | – | – | – | – | – | – | – | 2 | 3 |
| CO3 | 2 | 2 | 3 | 1 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO4 | 2 | 2 | 1 | 2 | 2 | – | – | – | – | – | – | – | – | – |
| CO5 | 2 | 2 | 1 | 1 | 2 | – | 3 | – | – | – | – | – | – | – |
| WT. AVG | 1.8 | 2.2 | 1.6 | 1.4 | 1.8 | – | 3 | – | – | – | – | – | 1.33 | 2.33 |
| Overall Mapping of Subject | 1.93 | |||||||||||||
| V SEM | PC-CS- AIDS- 317LA: Design and Analysis of Algorithms Lab | |||||||||||||
| CO1 | The student should be able to Design algorithms for various computing problems. | |||||||||||||
| CO2 | The student should be able to Analyse the time and space complexity of algorithms. | |||||||||||||
| CO3 | The student should be able to Critically analyse the different algorithm design techniques for a given problem. | |||||||||||||
| CO4 | The student should be able to Modify existing algorithms to improve efficiency. | |||||||||||||
| Mapping | PC-CS- AIDS- 317LA: Design and Analysis of Algorithms Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 3 | 2 | 3 | – | – | – | – | – | – | – | – | 2 | 2 |
| CO2 | 3 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 3 | 3 | 3 | 3 | 1 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 3 | 3 | 3 | 3 | 1 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 3 | 3 | 2.5 | 2.75 | 1.33 | 2 | 2 | |||||||
| Overall Mapping of Subject | 2.37 | |||||||||||||
| V SEM | PC-CS-AIDS-315LA: Python Lab | |||||||||||||
| CO1 | Implement Python programming basics and paradigm. | |||||||||||||
| CO2 | Implement python looping, control statements, string manipulations and functions. | |||||||||||||
| CO3 | Implement Data Analysis & visualization–using NumPy, panda matplot lib etc. | |||||||||||||
| CO4 | Implement Object Oriented Skills in Python. | |||||||||||||
| Mapping | PC-CS-AIDS-315LA: Python Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 1 | 2 | 1 | – | – | – | – | – | – | – | 2 | 2 |
| CO2 | 2 | 2 | 2 | 1 | 1 | – | – | – | – | – | – | – | 2 | 1 |
| CO3 | 2 | 2 | 1 | 2 | 2 | – | – | – | – | – | – | – | 2 | 3 |
| CO4 | 2 | 1 | 1 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 2 | 1.75 | 1.25 | 1.75 | 1.5 | – | – | – | – | – | – | – | 2 | 2 |
| Overall Mapping of Subject | 1.75 | |||||||||||||
| V SEM | MC-904A: Energy Resources &Management | |||||||||||||
| CO1 | An overview about Energy Resources, Conventional and Non-conventional sources. | |||||||||||||
| CO2 | Understand the Layout and working of Conventional Power Plants. | |||||||||||||
| CO3 | Understand the Layout and working of Non-Conventional Power Plants. | |||||||||||||
| CO4 | To understand the Energy Management, Audit and tariffs, Role of Energy in Economic development and Energy Scenario in India. | |||||||||||||
| Mapping | MC-904A: Energy Resources &Management | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | – | 1 | 2 | 2 | 2 | 1 | – |
| CO2 | 1 | 1 | 2 | – | – | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 1 | – |
| CO3 | 1 | – | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 1 | – |
| CO4 | 1 | 1 | 1 | 1 | 1 | – | 2 | 2 | 1 | 2 | 2 | 2 | – | – |
| WT. AVG | 1 | 1.33 | 1.5 | 1 | 1 | 2 | 1.25 | 2 | 1 | 2 | 2 | 2 | 1 | – |
| Overall Mapping of Subject | 1.47 | |||||||||||||
| VI SEM | PC-CS-AIDS-302A : Compiler Design | |||||||||||||
| CO1 | To understand the role and designing of a lexical analyzer. | |||||||||||||
| CO2 | To analyze the role and designing of syntax analyzer or parser. | |||||||||||||
| CO3 | To identify the role of semantic analyzer and intermediate code generation. | |||||||||||||
| CO4 | To explore the design importance of optimization of codes and error detection. | |||||||||||||
| Mapping | PC-CS-AIDS-302A : Compiler Design | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 3 | 2 | 1 | – | – | – | – | – | – | – | 3 | 2 |
| CO2 | 3 | 2 | 3 | 2 | 1 | – | – | – | – | – | – | – | 1 | 1 |
| CO3 | 2 | 2 | 2 | 1 | 3 | – | – | – | – | – | – | – | 2 | 1 |
| CO4 | 1 | 2 | 3 | 3 | 1 | – | – | – | – | – | – | – | 1 | 1 |
| WT. AVG | 2.25 | 2 | 2.75 | 2 | 1.5 | – | – | – | – | – | – | – | 1.75 | 1.25 |
| Overall Mapping of Subject | 1.93 | |||||||||||||
| VI SEM | ES-CS-AIDS-304A: Applied Statistical Analysis for AI | |||||||||||||
| CO1 | Explore the Statistical Analysis concepts with the irrelationships and process. | |||||||||||||
| CO2 | Explain the concept of describing, transforming and summarizing data using various statistical methods and apply them to solve real world problems. | |||||||||||||
| CO3 | Understand and apply testing hypothesis with real life datasets. | |||||||||||||
| CO4 | Examine and analyze the relationships to find the correlation and regression and their applications in real life. | |||||||||||||
| CO5 | Explore the advanced techniques with applications of decision trees, neural networks. | |||||||||||||
| Mapping | ES-CS-AIDS-304A: Applied Statistical Analysis for AI | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 2 | 3 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| CO2 | 1 | 3 | 3 | 2 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 2 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | 2 | 2 | 1 |
| CO4 | 2 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | 2 | 2 | 2 |
| CO5 | 2 | 2 | 1 | 2 | 3 | – | – | – | – | – | – | – | 2 | 3 |
| WT. AVG | 1.8 | 2.4 | 2 | 2.4 | 2.6 | – | – | – | – | – | – | 2 | 2 | 2 |
| Overall Mapping of Subject | 2.15 | |||||||||||||
| VI SEM | PC-CS-AIDS-306A: Big Data Analytics | |||||||||||||
| CO1 | Understand Big Data and its analytics in the real world. | |||||||||||||
| CO2 | Analyze the Big Data framework like Hadoop and NOSQL to efficiently store and process Big Data to generate analytics. | |||||||||||||
| CO3 | Design of Algorithms to solve Data Intensive Problems using Map Reduce Paradigm 3 4 | |||||||||||||
| CO4 | Design and Implementation of Big Data Analytics using pig and spark to solve data intensive problems and to generate analytics. | |||||||||||||
| CO5 | Implement Big Data Activities using Hive. | |||||||||||||
| Mapping | PC-CS-AIDS-306A: Big Data Analytics | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 3 | 3 | 1 | 2 | – | – | – | – | – | – | 1 | 2 | 2 |
| CO2 | 1 | 2 | 3 | 1 | 2 | – | – | – | – | – | – | – | 3 | 3 |
| CO3 | 2 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 2 | 2 | 2 | 1 | 3 | – | – | – | – | – | – | – | 2 | 3 |
| WT. AVG | 2 | 2.5 | 2.5 | 1.25 | 2.25 | – | – | – | – | – | – | 1 | 2.25 | 2.5 |
| Overall Mapping of Subject | 2.03 | |||||||||||||
| VI SEM | PC-CS-AIDS-308A: Applied Machine Learning | |||||||||||||
| CO1 | Identify over fit regression models. | |||||||||||||
| CO2 | Compare different regularized regression algorithms and decision tree ensemble algorithms. | |||||||||||||
| CO3 | Explain the confusion matrix and its relation to the ROC curve. | |||||||||||||
| CO4 | Construct training datasets, testing datasets, and model pipelines. | |||||||||||||
| Mapping | PC-CS-AIDS-308A: Applied Machine Learning | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 3 | 3 | 3 | – | – | – | – | – | – | – | 1 | 1 |
| CO2 | 2 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 1 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | – | 1 | 1 |
| CO4 | 1 | – | 2 | 2 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 1.5 | 2.33 | 2.25 | 2.5 | 2.5 | – | – | – | – | – | – | – | 1.5 | 1.5 |
| Overall Mapping of Subject | 2.01 | |||||||||||||
| VI SEM | OE-CS-AIDS-302: Soft Skills and Interpersonal Communication | |||||||||||||
| CO1 | Develop effective communication skills (spoken and written). | |||||||||||||
| CO2 | Develop effective presentation skills. | |||||||||||||
| CO3 | Conduct effective business correspondence and prepare business reports which produce results. | |||||||||||||
| CO4 | Become self-confident individuals by mastering inter-personal skills, team management skills, and leadership skills. | |||||||||||||
| Mapping | OE-CS-AIDS-302: Soft Skills and Interpersonal Communication | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 1 | – | – | – | – | – | 1 | 1 | 1 | 2 | – | 2 | 1 | 1 |
| CO2 | – | 1 | – | – | 1 | 1 | – | – | 1 | 1 | 1 | 1 | 1 | 2 |
| CO3 | – | – | – | – | – | – | – | 1 | 1 | 1 | 2 | 1 | – | – |
| CO4 | – | – | – | – | – | 1 | 1 | 1 | 2 | 1 | 1 | 1 | – | – |
| WT. AVG | 1 | 1 | – | – | 1 | 1 | 1 | 1 | 1.25 | 1.25 | 1.33 | 1.25 | 1 | 1.5 |
| Overall Mapping of Subject | 1.13 | |||||||||||||
| VI SEM | PC-CS-AIDS-310A: Soft Computing | |||||||||||||
| CO1 | The main objective of the Soft Computing Techniques to Improve Data Analysis | |||||||||||||
| CO2 | To strengthen the dialogue between the statistics and soft computing research communities in order to cross-pollinate both fields | |||||||||||||
| CO3 | To develop Solutions and generate mutual improvement activities | |||||||||||||
| CO4 | To develop practical data analysis skills, which can be applied to practical problems | |||||||||||||
| Mapping | PC-CS-AIDS-310A: Soft Computing | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | – | – | – | – | – | – | – | – | – | – | – | 2 | 2 |
| CO2 | 1 | 1 | – | 3 | – | – | – | – | – | – | – | – | 1 | 1 |
| CO3 | 1 | – | 3 | – | – | – | – | – | – | – | – | – | 1 | 1 |
| CO4 | 1 | 1 | – | – | 3 | – | – | – | – | – | – | – | 3 | 3 |
| WT. AVG | 1.25 | 1 | 3 | 3 | 3 | – | – | – | – | – | – | – | 1.75 | 1.75 |
| Overall Mapping of Subject | 2.11 | |||||||||||||
| VI SEM | PC-CS-AIDS-312LA: Applied Machine Learning Lab | |||||||||||||
| CO1 | Perform advanced data cleaning, exploration, and visualization | |||||||||||||
| CO2 | Engineer features based on conditional relationships between existing features | |||||||||||||
| CO3 | Build and finalize a machine learning classifier | |||||||||||||
| CO4 | Build machine learning applications in different domains | |||||||||||||
| Mapping | PC-CS-AIDS-312LA: Applied Machine Learning Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 3 | 3 | 3 | – | – | – | – | – | – | – | 1 | 1 |
| CO2 | 2 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 1 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | – | 1 | 1 |
| CO4 | 1 | – | 2 | 2 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 1.5 | 2.33 | 2.25 | 2.5 | 2.5 | – | – | – | – | – | – | – | 1.5 | 1.5 |
| Overall Mapping of Subject | 2.01 | |||||||||||||
| VI SEM | PC-CS-AIDS-314LA: Big Data Analytics Lab | |||||||||||||
| CO1 | Demonstrate the knowledge of big data analytics and implement different file management task in Hadoop. | |||||||||||||
| CO2 | Understand Map Reduce Paradigm and develop data applications using variety of systems. | |||||||||||||
| CO3 | Analyze and perform different operations on data using Pig Latin scripts. | |||||||||||||
| CO4 | Illustrate and apply different operations on relations and databases using Hive. | |||||||||||||
| Mapping | PC-CS-AIDS-314LA: Big Data Analytics Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 3 | 3 | 1 | 2 | – | – | – | – | – | – | 1 | 2 | 2 |
| CO2 | 1 | 2 | 3 | 1 | 2 | – | – | – | – | – | – | – | 3 | 3 |
| CO3 | 2 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 2 | 2 | 2 | 1 | 3 | – | – | – | – | – | – | – | 2 | 3 |
| WT. AVG | 2 | 2.5 | 2.5 | 1.25 | 2.25 | – | – | – | – | – | – | 1 | 2.25 | 2.5 |
| Overall Mapping of Subject | 2.03 | |||||||||||||
| VI SEM | ES-CS-AIDS-316LA: Applied Statistical Analysis for AI Lab | |||||||||||||
| CO1 | Implement basic Statistical operations in R language. | |||||||||||||
| CO2 | Implement regression techniques. | |||||||||||||
| CO3 | Implement hypothesis testing with real time applications. | |||||||||||||
| CO4 | Implement and evaluate various probability distributions for real world problems. | |||||||||||||
| Mapping | ES-CS-AIDS-316LA: Applied Statistical Analysis for AI Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 2 | 3 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| CO2 | 1 | 3 | 3 | 2 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 2 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | 2 | 2 | 1 |
| CO4 | 2 | 3 | 2 | 2 | 2 | – | – | – | – | – | – | 2 | 2 | 2 |
| CO5 | 2 | 2 | 1 | 2 | 3 | – | – | – | – | – | – | – | 2 | 3 |
| WT. AVG | 1.8 | 2.4 | 2 | 2.4 | 2.6 | – | – | – | – | – | – | 2 | 2 | 2 |
| Overall Mapping of Subject | 2.15 | |||||||||||||
| VII SEM | HM-CS-AIDS-401A: Business Intelligence and Data Visualization | |||||||||||||
| CO1 | Students will learn the principles and best practices for how to use data in order to support fact-based decision making. | |||||||||||||
| CO2 | Emphasis will be given to applications in marketing, where BI helps in the Businesses. | |||||||||||||
| CO3 | BI helps performing for sales analysis and in application domains | |||||||||||||
| CO4 | Practical experience will be gained by developing a BI project (case-study) with leading BI software. | |||||||||||||
| Mapping | HM-CS-AIDS-401A: Business Intelligence and Data Visualization | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 1 | 1 | 3 | – | – | – | – | – | – | – | 2 | 3 |
| CO2 | 2 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO3 | 3 | 3 | 3 | 2 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 2 | 2 | 3 | 2 | 3 | – | – | – | – | – | – | – | 2 | 1 |
| WT. AVG | 2.25 | 2.5 | 2.5 | 1.75 | 2.75 | – | – | – | – | – | – | – | 1.75 | 2 |
| Overall Mapping of Subject | 2.21 | |||||||||||||
| VII SEM | HSS-403A :Universal Human Values II: Understanding Harmony | |||||||||||||
| CO1 | Development of a holistic perspective based on self-exploration about themselves(humanbeing),family,societyandnature/existence. | |||||||||||||
| CO2 | Understanding (or developing clarity) of the harmony in the human being, family, society and nature/existence. | |||||||||||||
| CO3 | Strengthening of self-reflection. | |||||||||||||
| CO4 | Development of commitment and courage to act. | |||||||||||||
| Mapping | HSS-403A :Universal Human Values II: Understanding Harmony | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | – | – | – | – | – | – | – | 3 | 3 | – | – | 3 | – | – |
| CO2 | – | – | – | – | – | 2 | – | 3 | 3 | – | – | 3 | – | – |
| CO3 | – | – | – | – | – | – | – | 3 | 3 | 2 | – | 3 | – | – |
| CO4 | – | – | – | – | – | 3 | 3 | 3 | 3 | 2 | – | 3 | – | – |
| WT. AVG | – | – | – | – | – | 2.5 | 3 | 3 | 3 | 2 | – | 3 | – | – |
| Overall Mapping of Subject | 2.75 | |||||||||||||
| VII SEM | OE-CS-AIDS-401: Cyber Law and Ethics | |||||||||||||
| CO1 | To facilitate the basic knowledge of cyber Law. | |||||||||||||
| CO2 | To learn about how to maintain the Confidentiality, Integrity and Availability of information technology act. | |||||||||||||
| CO3 | To get enable to fix the various Cyber Law and Related Legislation. | |||||||||||||
| CO4 | To deal with the Cyber Ethics. | |||||||||||||
| Mapping | OE-CS-AIDS-401: Cyber Law and Ethics | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | – | – | – | – | – | – | 2 | – | 1 | – | – | 1 | – | – |
| CO2 | – | – | – | 1 | – | 2 | 1 | 3 | 1 | 2 | 1 | – | 2 | 1 |
| CO3 | – | – | – | – | – | 1 | 1 | – | 1 | – | – | 1 | 1 | – |
| CO4 | – | – | – | – | – | 2 | 2 | 2 | – | 1 | 1 | 1 | – | 1 |
| CO5 | – | – | – | – | – | – | – | – | 2 | – | 1 | – | 1 | – |
| WT. AVG | – | – | – | 1 | – | 1.67 | 1.5 | 2.5 | 1.25 | 1.5 | 1 | 1 | 1.33 | 1 |
| Overall Mapping of Subject | 1.38 | |||||||||||||
| VII SEM | PE-CS-AIDS-417A:Data Mining & Predictive Modelling | |||||||||||||
| CO1 | Understand the fundamental concept of Data Mining. | |||||||||||||
| CO2 | Learn Data Mining techniques for Prediction and Forecasting. | |||||||||||||
| CO3 | Compare the underlying Predictive Modelling techniques. | |||||||||||||
| CO4 | Select appropriate Predictive Modelling approaches to identify cases and apply using a suitable package such as SPSS modeler . | |||||||||||||
| Mapping | PE-CS-AIDS-417A:Data Mining & Predictive Modelling | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | – | – | – | – | – | – | – | – | – | – | – | 3 | 2 |
| CO2 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | – | – | 3 | 2 |
| CO3 | 3 | 3 | 3 | – | – | – | – | – | – | – | – | – | 3 | 2 |
| CO4 | 3 | 3 | 3 | 2 | 3 | – | – | – | – | – | – | – | 1 | 3 |
| WT. AVG | 3 | 3 | 2.67 | 2 | 3 | – | – | – | – | – | – | – | 2.5 | 2.25 |
| Overall Mapping of Subject | 2.63 | |||||||||||||
| VII SEM | PE-CS-AIDS-425A: Human AI Interaction | |||||||||||||
| CO1 | To have a broad foundational understanding of types and techniques in AI/ML | |||||||||||||
| CO2 | To be able to demonstrate good understanding of the potential use cases and benefits of artificial intelligence (AI) technologies | |||||||||||||
| CO3 | To have a critical understanding of the ethical, social and legal implications of AI applications on human life and work | |||||||||||||
| CO4 | To be able to understand appropriate design, development and research methods for human-AI interaction | |||||||||||||
| Mapping | PE-CS-AIDS-425A: Human AI Interaction | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | – | – | – | – | 3 | 1 | 1 |
| CO2 | 3 | 2 | 3 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | – | 2 | 1 | 1 |
| CO3 | 3 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | – | – | – | 2 | 1 | 1 |
| CO4 | 3 | 2 | 3 | 2 | 3 | 3 | 2 | 1 | – | – | 2 | 3 | 2 | 2 |
| WT. AVG | 3 | 1.5 | 2 | 2 | 1.75 | 1.75 | 1.75 | 1 | 1 | 2 | 2 | 2.5 | 1.25 | 1.25 |
| Overall Mapping of Subject | 1.77 | |||||||||||||
| VII SEM | HM-CS-AIDS-405A: Data Visualization Lab | |||||||||||||
| CO1 | Understand and describe the main concepts of data visualization | |||||||||||||
| CO2 | Create ad-hoc reports, data visualizations, and dashboards using Tableau Desktop | |||||||||||||
| CO3 | Publish the created visualizations to Tableau Server and Tableau Public | |||||||||||||
| CO4 | Create Dashboard for real problems in Industry | |||||||||||||
| Mapping | HM-CS-AIDS-405A: Data Visualization Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 1 | 1 | 3 | – | – | – | – | – | – | – | 2 | 3 |
| CO2 | 2 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO3 | 3 | 3 | 3 | 2 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 2 | 2 | 3 | 2 | 3 | – | – | – | – | – | – | – | 2 | 1 |
| WT. AVG | 2.25 | 2.5 | 2.5 | 1.75 | 2.75 | – | – | – | – | – | – | – | 1.75 | 2 |
| Overall Mapping of Subject | 2.21 | |||||||||||||
| VII SEM | PC-CS-AIDS- 417LA:Data Mining & Predictive Modelling LAB | |||||||||||||
| CO1 | Understand the fundamental concept of Data Mining. | |||||||||||||
| CO2 | Learn Data Mining techniques for Prediction and Forecasting. | |||||||||||||
| CO3 | Compare the underlying Predictive Modelling techniques. | |||||||||||||
| CO4 | Select appropriate Predictive Modelling approaches to identify cases and apply using a suitable package such as SPSS modeller. | |||||||||||||
| Mapping | PC-CS-AIDS- 417LA:Data Mining & Predictive Modelling LAB | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | – | – | – | – | – | – | – | – | – | – | – | 3 | 2 |
| CO2 | 3 | 3 | 2 | 2 | – | – | – | – | – | – | – | – | 3 | 2 |
| CO3 | 3 | 3 | 3 | – | – | – | – | – | – | – | – | – | 3 | 2 |
| CO4 | 3 | 3 | 3 | 2 | 3 | – | – | – | – | – | – | – | 1 | 3 |
| WT. AVG | 3 | 3 | 2.67 | 2 | 3 | – | – | – | – | – | – | – | 2.5 | 2.25 |
| Overall Mapping of Subject | 2.63 | |||||||||||||
| VIII SEM | PC-CS-AIDS- 402A: Reinforcement Learning | |||||||||||||
| CO1 | To learn the basics of Reinforcement Learning concepts, various Reinforcement Learning architecture | |||||||||||||
| CO2 | To explore knowledge of various process of Reinforcement Learning | |||||||||||||
| CO3 | To understand the basics of Reinforcement Learning models | |||||||||||||
| CO4 | To implies about the different Reinforcement Learning algorithms and their applications to solve real world problems. | |||||||||||||
| Mapping | PC-CS-AIDS- 402A: Reinforcement Learning | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | – | 3 | 1 |
| CO2 | 2 | 2 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 2 | – | – | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 1 | 2 | 2 | – | 2 | – | – | – | – | – | – | 2 | 1 | 2 |
| WT. AVG | 2 | 2 | 2 | 2.33 | 2 | 2 | 2 | 1.75 | ||||||
| Overall Mapping of Subject | 2.01 | |||||||||||||
| VIII SEM | HSS-404A: Entrepreneurship and Start-ups | |||||||||||||
| CO1 | To understand the basics of Entrepreneurship . | |||||||||||||
| CO2 | To learn the basics of Creative and Design Thinking . | |||||||||||||
| CO3 | To apply the Business Enterprises . | |||||||||||||
| CO4 | To know about business models . | |||||||||||||
| Mapping | HSS-404A: Entrepreneurship and Start-ups | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | – | – | – | – | – | – | – | – | 2 | 2 | – | 2 | – | – |
| CO2 | – | 3 | 3 | – | – | – | – | – | – | – | – | 3 | – | – |
| CO3 | – | – | – | – | – | 1 | – | 2 | 2 | – | 3 | – | – | – |
| CO4 | – | – | – | – | – | – | – | 2 | 2 | 3 | – | 3 | – | – |
| WT. AVG | – | 3 | 3 | – | – | 1 | – | 2 | 2 | 2.5 | 3 | 2.67 | – | – |
| Overall Mapping of Subject | 2.40 | |||||||||||||
| VIII SEM | OE-CS-AIDS-408: Agile Software Engineering | |||||||||||||
| CO1 | Analyze existing problems with the team, development process and wider organization | |||||||||||||
| CO2 | Apply a thorough understanding of Agile principles and specific practices | |||||||||||||
| CO3 | Select the most appropriate way to improve results for a specific circumstance or need | |||||||||||||
| CO4 | Judge and craft appropriate adaptations to existing practices or processes depending upon analysis of typical problems and risk analysis. | |||||||||||||
| Mapping | OE-CS-AIDS-408: Agile Software Engineering | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 1 |
| CO2 | 2 | 2 | 1 | 3 | 3 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 2 | 1 | 2 | 2 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO4 | 2 | 1 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 2.25 | 1.5 | 1.75 | 2.25 | 2.25 | – | – | – | – | – | – | – | 1.75 | 1.75 |
| Overall Mapping of Subject | 1.93 | |||||||||||||
| VIII SEM | PE-CS-AIDS-416A: Application of Data Science in Industry | |||||||||||||
| CO1 | Describe a flow process for data science problems | |||||||||||||
| CO2 | Classify data science problems into standard typology | |||||||||||||
| CO3 | Develop R codes for data science solutions | |||||||||||||
| CO4 | Correlate results to the solution approach followed and Construct use cases to validate approach and identify modifications required | |||||||||||||
| Mapping | PE-CS-AIDS-416A: Application of Data Science in Industry | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 1 | 2 | 1 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO2 | 2 | 1 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 2 | 2 | 1 | 2 | 1 | – | – | – | – | – | – | – | 2 | 1 |
| CO4 | 3 | 2 | 2 | 1 | 1 | – | – | – | – | – | – | – | 1 | 1 |
| WT. AVG | 2.25 | 1.5 | 1.75 | 1.5 | 1.5 | – | – | – | – | – | – | – | 1.5 | 1.5 |
| Overall Mapping of Subject | 1.64 | |||||||||||||
| VIII SEM | PE-CS-AIDS426A : Next Generation Databases | |||||||||||||
| CO1 | Implement and evaluate complex, scalable database systems, with emphasis on providing experimental evidence for design decisions. | |||||||||||||
| CO2 | Demonstrate the management of structured and unstructured data management with recent tools and technologies. | |||||||||||||
| CO3 | Demonstrate competency in designing No SQL database management systems | |||||||||||||
| CO4 | Demonstrate competency in designing XML Databases | |||||||||||||
| Mapping | PE-CS-AIDS426A : Next Generation Databases | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO2 | 2 | 3 | 2 | 2 | 1 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 3 | 2 | 2 | 3 | 1 | – | – | – | – | – | – | – | 2 | 1 |
| CO4 | 2 | 1 | 1 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| WT. AVG | 2.25 | 2 | 1.75 | 2.5 | 1.5 | – | – | – | – | – | – | – | 1.75 | 1.75 |
| Overall Mapping of Subject | 1.93 | |||||||||||||
| VIII SEM | PC-CS-AIDS- 404LA: Reinforcement Learning Lab | |||||||||||||
| CO1 | Implement Python programming advance and paradigm. | |||||||||||||
| CO2 | Implement various process of Reinforcement Learning | |||||||||||||
| CO3 | Implement various Reinforcement Learning models | |||||||||||||
| CO4 | Implement various Reinforcement Learning algorithms. | |||||||||||||
| Mapping | PC-CS-AIDS- 404LA: Reinforcement Learning Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 3 | 2 | 2 | 3 | 2 | – | – | – | – | – | – | – | 3 | 1 |
| CO2 | 2 | 2 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 2 | – | – | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO4 | 1 | 2 | 2 | – | 2 | – | – | – | – | – | – | 2 | 1 | 2 |
| WT. AVG | 2 | 2 | 2 | 2.33 | 2 | – | – | – | – | – | – | 2 | 2 | 1.75 |
| Overall Mapping of Subject | 2.01 | |||||||||||||
| VIII SEM | PE-CS-AIDS-416LA: Application of Data Science in Industry Lab | |||||||||||||
| CO1 | Describe a flow process for data science problems | |||||||||||||
| CO2 | Classify data science problems into standard typology | |||||||||||||
| CO3 | Develop R codes for data science solutions | |||||||||||||
| CO4 | Correlate results to the solution approach followed and Construct use cases to validate approach and identify modifications required | |||||||||||||
| Mapping | PE-CS-AIDS-416LA: Application of Data Science in Industry Lab | |||||||||||||
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | |
| CO1 | 2 | 1 | 2 | 1 | 2 | – | – | – | – | – | – | – | 1 | 2 |
| CO2 | 2 | 1 | 2 | 2 | 2 | – | – | – | – | – | – | – | 2 | 2 |
| CO3 | 2 | 2 | 1 | 2 | 1 | – | – | – | – | – | – | – | 2 | 1 |
| CO4 | 3 | 2 | 2 | 1 | 1 | – | – | – | – | – | – | – | 1 | 1 |
| WT. AVG | 2.25 | 1.5 | 1.75 | 1.5 | 1.5 | – | – | – | – | – | – | – | 1.5 | 1.5 |
| Overall Mapping of Subject | 1.64 | |||||||||||||
| Industrial Training/Internship (Semester: 3rd, 5th and 7th) | ||||||||||||||
| CO1 | Identify the problem in the relevant engineering field and gather information through independent or collaborative study | |||||||||||||
| CO2 | Develop and maintain a product based on the learning with the ability to work as an individual or in group with the capacity to be a team member or leader or manager. | |||||||||||||
| CO3 | Apply the acquired skills in communication and document writing. | |||||||||||||
| CO4 | Demonstrate the professional, societal and ethical responsibilities of an engineer. | |||||||||||||
| Mapping: Industrial Training/Internship (Semester: 3rd, 5th and 7th) | ||||||||||||||
| CO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 |
| CO1 | 2 | 1 | – | – | – | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 |
| CO2 | 3 | – | 3 | 2 | 3 | 1 | 1 | 1 | – | 1 | 1 | 1 | 3 | 2 |
| CO3 | – | – | – | – | – | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | – |
| CO4 | – | – | – | – | – | 2 | – | 2 | 1 | 3 | – | – | – | 1 |
| AVG | 2.5 | 1 | 3 | 2 | 3 | 1.25 | 1 | 1.25 | 1 | 1.5 | – | 1.33 | 1.67 | 1.67 |
| Project – I, II (Semester: 7th and 8th) | ||||||||||||||
| CO1 | Independently carry out literature survey in identified domain, and consolidate it to formulate a problem statement | |||||||||||||
| CO2 | Apply identified knowledge to solve a complex engineering problem and design a solution, implement and test the proposed solution | |||||||||||||
| CO3 | Use synthesis/modeling to simulate and solve a problem or apply appropriate method of analysis to draw valid conclusions and present, demonstrate, execute final version of project | |||||||||||||
| CO4 | Incorporate the social, environmental and ethical issues effectively into solution of an engineering problem | |||||||||||||
| CO5 | Contribute effectively as a team member or leader to manage the project timeline | |||||||||||||
| CO6 | Write pertinent project reports and make effective Project Presentations | |||||||||||||
| Mapping: Project – I, II (Semester: 7th and 8th) | ||||||||||||||
| CO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 |
| CO1 | – | – | – | – | – | 2 | 1 | 2 | 1 | 1 | 1 | 3 | 3 | 3 |
| CO2 | – | – | – | – | – | 1 | 1 | 1 | – | – | – | – | 3 | 3 |
| CO3 | – | – | – | – | – | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 3 | 3 |
| CO4 | – | – | – | – | – | 3 | 3 | 3 | 2 | 2 | 1 | 2 | – | – |
| CO5 | – | – | – | – | – | – | – | – | 3 | – | 3 | – | – | – |
| CO6 | – | – | – | – | – | – | – | – | – | 3 | – | – | – | – |
| AVG | – | – | – | – | – | 1.75 | 1.5 | 2 | 2 | 2 | 1.75 | 2.33 | 3 | 3 |
(a) PROJECT EVALUATION
(i) Internal assessment of project work
| Review | Assessment | AssessmentTool (Rubric) | AssessmentWeightage | CO(s)Covered |
| 1 | Project Synopsis Evaluation | PR1 | 20% | 1 |
| 2 | 1st Mid-Term Project Evaluation | PR2 & PR6 | 10% & 5% | 2, 5 |
| 3 | 2nd Mid-Term Project Evaluation | PR3 & PR6 | 10% & 5% | 2, 5 |
| 4 | End Term Project Evaluation | PR4, PR5 & PR6 | 20%, 10% & 5% | 3, 4, 5 |
| 5 | Project Report Evaluation | PR7 | 15% | 6 |
(ii) Rubric-PR1: Project Synopsis Evaluation (Maximum Marks: 20)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Topic selection | Complete innovative and useful for society | Somewhat innovative and useful for society | Useful for society but not innovative | Useful for limited group and not innovative |
| b | Problem Definition | Exceeds expectation. | Extends beyond expectation in some manner. | Meets expectation. | Nearly meet expectations |
| The social, ethical and environmental issues of the project problem also identified. | Problem and its implications well understood and described. | Problem and its implications understood but not well described. | Steps to be followed to solve the defined problem are not specified properly. | ||
| c | Literature Survey Purpose and need of the project | Outstanding investigation in all aspects. | Well-researched project, good depth and thoroughness, sensible planning of research and well referenced throughout. | Research is clear and structured. | Minimal research or cursory coverage |
| Detailed and extensive explanation of the purpose and need of the project. | Collects a great deal of information and good study of the existing systems. | Appropriate coverage is present and referenced. | Minimal referencing, | ||
| . | . | Moderate study of the existing systems. | Minimal explanation of the purpose and need of the project. | ||
| d | Justification of Project Objectives and Planning | All objectives of the proposed work are well defined. | Good justification to the objectives. | Incomplete justification to the objectives proposed. | Limited information |
| Steps to be followed to solve the defined problem are clearly specified. | Methodology to be followed is specified but detailing is not done. | Steps are mentioned but unclear and without justification to objectives. | Only some objectives of the proposed work are defined. | ||
| e | Project Scheduling & Distribution of Work among Team members | Detailed and extensive scheduling with timelines provided for each phase of project. | Good Scheduling of project. | Moderate scheduling of project. | Poor / No project scheduling done |
| Work breakdown structure well defined. | Work breakdown structure properly defined. | Work breakdown insufficient. | No Work breakdown structure provided. | ||
| TOTAL MARKS = (a + b + c + d + e)/2.5 | |||||
(iii) Rubric-PR2:1st Mid-term Project Evaluation (Maximum Marks: 10)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Quality of Software Requirements/ Specifications | Outstanding clarity of thought and documentation in the development of design from the specification using and adapting models appropriately. | Focus is on specification and the design follows from it, using most appropriate elements of chosen design technique | Design techniques used minimally though correctly on specification | Very minimal analysis |
| Excellent incisive analysis leading to well defined model/ requirements specification of high quality that is fully accurate. | Analysis is well presented and leads to a sound well documented model/ requirements specification. | Minimal model/ requirements specification is created | Very Minimal model/ requirements specification is created | ||
| b | Quality Appropriateness and Accuracy of Design | Excellent design covering all aspects of the specification, fully appropriate to the project, shoeing clear thinking | Appropriate design, clear and accurate, satisfactory for the implementation of the project. | Limited design, or design not well related to specification or model | Very minimal design |
| TOTAL MARKS = (a + b)/2 | |||||
(iv) Rubric-PR3: 2nd mid Term Project Evaluation (Maximum Marks: 10)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Quality, appropriateness and accuracy of Project Implementation | Excellent use of software engineering principles and models both at higher and lower levels in implementation from design cycle. | Very well engineered solution, with evidence that the student has used proven method in transforming design into implementation. | Appropriately engineered implementation which follows from design Language/package facilities exploited to suggest a functional implementation. | In sufficient implementation to show competent use of any problem solving methods. |
| Documented use of complex features in the language /package which show quantitatively and qualitatively the improvements gained. | Appropriate use of facilities to make implementation more efficient or effective. | Project with some limitations, mostly technically sound. | Minimal implementation. | ||
| An excellent fully operating technically outstanding project. | Effective and efficient implementation technically with only minor limitations. | Project essentially works but with some severe functional limitations. | Poor technical quality with little use of development skills or knowledge in evidence. | ||
| Project fulfils functional requirements specification exactly with no limitations or failures of any type. | Project works well with only some minor functional limitations. | Project does not work in most parts to requirements/ specification. | |||
| b | Quality, appropriateness and accuracy of Testing | A quality piece of work giving full coverage of the solution and full program of testing/ evaluation undertaken. | Extensive and well organized implementation and testing/evaluation documentation. | Sufficient implementation documentation and testing/ evaluation documentation. | Minimal implementation documentation or testing/evaluation documentation. |
| TOTAL MARKS = (a + b)/2 | |||||
(v) Rubric–PR4: End Semester Internal Project Evaluation (Maximum Marks: 20)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Quality and accuracy of Software System/Model | Excellent design covering all aspects of the specification, fully appropriate to the project, shoeing clear thinking. | Appropriate design, clear and accurate, satisfactory for the implementation of the project. | Design not well related to specification or model | Very minimal design |
| An excellent fully operating technically outstanding project | Very well engineered solution, with evidence that the student has used proven method in transforming design into implementation | Language/package facilities exploited to suggest a functional implementation | In sufficient implementation to show competent use of any problem solving methods | ||
| Outstanding clarity of thought and documentation in the development of design from the specification using and adapting models appropriately | Effective and efficient implementation with only minor limitations | Project with some limitations, mostly technically sound | Poor technical quality with little use of development skills or knowledge in evidence | ||
| A quality piece of work giving full coverage of the solution and full programme of testing/ evaluation undertaken | Extensive and well organized implementation and testing/ evaluation documentation | Project essentially works but with some severe limitations | Project does not work in most parts to requirements specification | ||
| Sufficient implementation documentation and testing/ evaluation documentation | Minimal implementation documentation or testing/ evaluation documentation | ||||
| b | Demonstration of software system /Module working and Functioning | All defined objectives are achieved | All defined objectives are achieved | All defined objectives are achieved | Only some of the defined objectives are achieved |
| Each module working well and properly demonstrated | Each module working well and properly demonstrated | Modules are working well in isolation and properly demonstrated | Modules are not in proper working form that further leads to failure of integrated system | ||
| All modules of project are well integrated and system working is accurate | Integration of all modules not done and system working is not Very satisfactory | Modules of project are not properly integrated | |||
| TOTAL MARKS = (a + b) | |||||
(vi) Rubric–PR5: Identification of the social, environmental and ethical issues (Max. Marks: 10)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Identification of the social, environmental and ethical issues of the project problem | Identifying and solving social, environmental and ethical issues | Identifying and solving social, environmental or ethical issues | Identifying social, environmental or ethical issues | Not able to Identify any issues |
(vii) Rubric– PR6: Individual Contribution Evaluation (Maximum Marks: 05)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Individual Presentation | Excellently planned and executed presentation and demo leaving the listeners in no doubt of the value of the product | Quality presentation and demo. Clear and concise description leaving listeners with sound understanding of project and its problems | Timed and prepared presentation, demo with student describing what has been learnt | No presentation or no demo or student unable to articulate project development |
| Contents of presentations are appropriate and well delivered | Contents of presentations are appropriate and well delivered | Contents of presentations are appropriate but not well delivered | Contents of presentations are not appropriate and not well delivered | ||
| Proper eye contact with audience and clear voice with good spoken language | Clear voice with good spoken language but | Eye contact with only few people and unclear voice | Poor eye contact with audience and unclear voice | ||
| Less eye contact with audience | |||||
| b | Individual Contribution | Excellent Contribution showing his/her dependency in project | Good contribution as reflected in overall work | Some contribution as reflected in overall work. | No Contribution |
| c | To observe the completion of work referring to the original set plan | Ahead of the proposed plan | In pace with the plan | Delayed but can cope up with the lag at their own | Interventional help is needed |
| TOTAL MARKS = (a + b+c)/6 | |||||
(viii) Rubric– PR7: Project Report Evaluation (Maximum Marks: 15)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Style, structure, form and the perceived clarity with readability of report | Outstanding, comprehensive and clear report, Fully referenced | Effective report using academic language accurately referenced. | Acceptable report structure, some referencing, no missing parts, clarity of language | Report is unbalanced or unclear, or it is difficult to follow ideas |
| Major sections missing, or no referencing | |||||
| b | Effectiveness of the project report | Accurately referenced, very high standard of presentation aimed at the right level throughout. | Effective technical/business report fully structured, accurately referenced | Adequate report presentation references included. | Referencing is poor or inconsistent, or lack of illustrative content. |
| Fully referenced Complete explanation of the key concepts and strong description of the technical requirements of the project | Incomplete explanation of the key concepts and in- sufficient description of the technical requirements of the project | Report is unreadable as an English report | |||
| Complete explanation of the key concepts but in-sufficient description of the technical requirements of the project | Inappropriate explanation of the key concepts and poor description of the technical requirements of the project. | ||||
| c | Results, Conclusion and Discussions | Results are presented in very appropriate manner | Results are presented in good manner | Results presented are not much satisfactory | Results are not presented properly |
| Project work is well summarized and concluded | Project work summary and conclusion not very appropriate Future extensions in the project are specified | Project work summary and conclusion not very appropriate Future extensions in the project are not specified | Project work is not summarized and concluded | ||
| Future extensions in the project are well specified | Future extensions in the project are not specified | ||||
| TOTAL MARKS = (a+b+c)/2 | |||||
(ix) Evaluation weightages of each CO through the rubrics
| CO Number | CO1 | CO2 | CO3 | CO4 | CO5 | CO6 |
| Marks allotted to the COs through the rubrics (Max. 100) | 20 | 20 | 20 | 10 | 15 | 15 |
(b) INTERNSHIP EVALUATION
(i) Internal assessment of internships
| Rubric | Parameters | Weightage (Assessment Marks) |
| R1 | Objective of Training | 20% (10) |
| R2 | Domain Knowledge | 20% (10) |
| R3 | Practical Implementation | 20% (10) |
| R4 | Q and A during Presentation | 20% (10) |
| R5 | Training Report | 20% (10) |
| Total | 100% (50) | |
(ii) The rubrics for assessing internships:
| Rubric | Parameter | Level of Achievement | |||
| Excellent (10) | Good (8) | Average (6) | Poor (4) | ||
| R1 | Objective of Training | Objective of training is clearly and well defined. | Objective of training is defined with good justifications. | Objective of training is defined with little justifications. | Objective of training is unclear. |
| R2 | Domain Knowledge | Extensive knowledge of technology implemented | Fair knowledge of technology implemented | Lacks sufficient knowledge of technology implemented | No knowledge of technology implemented |
| R3 | Practical Implementation | Practical Implementation is completed in very systematic manner. | Practical Implementation is completed in appropriate manner. | Practical Implementation is completed but not systematically. | Practical Implementation is not completed. |
| R4 | Q and A during Presentation | Answers effectively in a satisfied manner to queries by the examiner | Answers appropriately to queries by the examiner | Non satisfactory answers to the queries by the examiner | Does not answer to queries by the examiner |
| R5 | Training Report | Report as per specified format and completed. | Report completed with very few contents not as per format. | Report completed but formatting not done properly | Report not prepared as per format. |
(iii) Evaluation weightages of each CO through the rubrics
| CO Number | CO1 | CO2 | CO3 | CO4 |
| Marks allotted to the COs through the rubrics (Max. 50) | 10 | 20 | 10 | 10 |
