Sr.No.Program Outcomes (POs)
PO1: Adopt relevant methods and procedures for solving problems using computers.
PO2: Compare statistical information and use their intelligence and identify with a professional approach.
PO3: Use the relevant equipment, technology and software to collect, compare and assess scientific data.
PO4: Develop and demonstrate ideas in innovative manner.
PO5: Design and deliver effective presentations and showcase their various IT skills.
PO6: Use research-based learning and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
PO7: Interpret the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the information of, and need for sustainable development.
PO8: Communicate effectively and professionally both in writing and by means of presentations to both specialist and a general audience.
Sr.No.Program Specific Outcomes (PSOs)
PS01: Communicate computer science concepts, designs, and solutions effectively and professionally
PS02: Apply knowledge of computing to produce effective designs and solutions for specific problems.
PS03: Use software development tools, software systems, and modern computing platforms.
Semester I - 2019 Pattern Course Name: Paradigm of Programming Language
Sr.No.Course Outcomes (COs)
CO1: Think about programming languages analytically.
CO2: Separate syntax from semantics.
CO3: Compare programming language designs.
CO4: Interpret their strengths and weaknesses.
CO5: Learn new languages more quickly.
CO6: Infer basic language implementation techniques.
CO7: Learn small programs in different programming Languages.
Course Name: Design and Analysis of Algorithms
Sr.No.Course Outcomes (COs)
CO1: Design the algorithms.
CO2: Select the appropriate algorithm by doing necessary analysis of algorithms
CO3: Learn basic Algorithm Analysis techniques and identify the use of asymptotic notation
CO4: Interpret the use of data structures in improving algorithm Performance
CO5: Differentiate classical problem and solutions
CO6: Identify classification of problems
CO7: Provide foundation in algorithm design and analysis
CO8: Describe and design algorithms in the context of space and time complexity.
Course Name: Database Technologies
Sr.No.Course Outcomes (COs)
CO1: Summarize database technologies
CO2: Describe the concept of NoSQL databases
CO3: Design database using MongoDB.
CO4: Implement the information of making a choice of what database technologies to use, based on their application needs.
CO5: Handle large volumes of structured, semi-structured, and unstructured data using database technologies.
CO6: Identify detailed architecture, define objects, load data, query data and performance tune NoSQL databases.
Course Name: Cloud computing
Sr.No.Course Outcomes (COs)
CO1: Explain the principles and paradigm of Cloud Computing.
CO2: Appreciate the role of Virtualization Technologies
CO3: Design and deploy Cloud Infrastructure.
CO4: Predict cloud security issues and solutions.
CO5: Design& develop backup strategies for cloud data based on features.
CO6: Display new ideas and innovations in cloud computing.
2013 Pattern Course Name: CS-101 Principles of Programming Languages
Sr.No.Course Outcomes (COs)
CO1: Think about programming languages analytically:
CO2: Separate syntax from semantics
CO3: Compare programming language designs
CO4: Identify their strengths and weaknesses
CO5: Learn new languages more quickly
CO6: Interpret basic language implementation techniques
CO7: Learn small programs in different programming Languages.
Course Name: CS-102 Advanced Networking
Sr.No.Course Outcomes (COs)
CO1: Define different types of routing protocols.
CO2: Illustrate different types of multimedia.
CO3: Recall the concept of networking models, protocols, functionality of each layer.
CO4: Explain methods of user authentication.
CO5: Explain the importance of network security and cryptography and its applications
CO6: Identify different network security protocols and learn them.
Course Name: CS-103 Distributed Database Concepts
Sr.No.Course Outcomes (COs)
CO1: Relate the concepts of distributed database
CO2: Describe what is Distributed DBMS
CO3: Explain various architectures of DDBMS
CO4: Apply various fragmentation techniques given a problem. Demonstrate the basic fundamentals of distributed database
CO5: Illustrate information of architecture, design issues, integrity control, query processing and optimization, transactions, and concurrency control & distributed transaction reliability.
CO6: Identify the steps of query processing.
Course Name: CS-104 Design and Analysis of Algorithms
Sr.No.Course Outcomes (COs)
CO1: Basic Algorithm Analysis techniques and express the use of asymptotic notation
CO2: Discuss different design strategies
CO3: Express the use of data structures in improving algorithm performance.
CO4: Explain classical problem and solutions
CO5: Learn a variety of useful algorithms
CO6: Identify classification of problems.
Course Name: CS-105 Network Programming
Sr.No.Course Outcomes (COs)
CO1: Demonstrating the working mechanism and implementation of data communication protocols on Unix operating system.
CO2: Implementation using TCP sockets
CO3: Implementation using UDP sockets.
CO4: Construct Client-Server application program
CO5: Construct Client-Server application program
CO6: Show the use of different socket options.
CO7: Plan to create different kind projects which can make use of TCP and UDP client server technology
Semester II - 2013 Pattern Course Name: CS-201 Digital Image Processing
Sr.No.Course Outcomes (COs)
CO1: What are the applications of Digital Image Processing How images are formed, sampled, quantized and represented digitally.
CO2: How images are enhanced using Spatial Filtering
CO3: How images are enhanced using Frequency Filtering.
CO4: How images are restored using different filters.
CO5: Role of Morphological Operations in Image Processing.
CO6: Segmentation and how it can be achieved using different methods.
CO7: How object can be described. (Feature Extraction methods)
Course Name: CS-202 Advanced Operating Systems
Sr.No.Course Outcomes (COs)
CO1: Describe the programming interface to the Unix/Linux-system call.
CO2: Demonstrate C program that runs under Unix/Linux.
CO3: Define the insights into functional modules for OS.
CO4: Summarize the concepts underlying in the design and implementation of OS.
CO5: Summarize the concept of system call implementation.
CO6: Design and implementation of Operating Systems.
Course Name: CS-203 Data Mining and Data Warehousing
Sr.No.Course Outcomes (COs)
CO1: Process raw data to make it suitable for various data mining algorithms
CO2: Discover and measure interesting patterns from different kinds of databases.
CO3: Interpret the techniques of clustering, classification, association finding, feature selection and visualization to real world data.
CO4: Design a data mart or data warehouse for any organization.
CO5: Asses raw input data and preprocess it to provide suitable input for range of data mining algorithms.
CO6: Extract association rules and classification model
CO7: Identify the similar objects using clustering techniques.
Course Name: CS-205 Programming with DOT NET
Sr.No.Course Outcomes (COs)
CO1: Gained information of DOT Net framework
CO2: Learned C# language features for development of Web and Stand Alone Application
CO3: Implementing Web Services using DOTNET framework
CO4: Apply validations to ASP and C# pages
CO5: use different controls of c# and ASP.Net
CO6: Develop different kinds of web application
CO7: Host different kind of web application on the internet.
Course Name: CS-206 Artificial Intelligence
Sr.No.Course Outcomes (COs)
CO1: Define What is AI?and Early work in AI, AI and related fields and AI problems and Techniques
CO2: Solve AI problems
CO3: Express the Representations and Mappings, Approaches to Knowledge representation, Knowledge representation method,- Propositional Logic,, Predicate logic, Representing Simple facts in Logic, Representing Instances and Isa relationships,- Computable Functions and Predicates, Resolution, Forward and backward chaining
CO4: Write a Script and design a frame for given problem
CO5: EstimateMinimax Search Procedures, Adding alpha-beta cutoffs
CO6: Get the information about what is learning and types of learning.
SY MSc(CS) Semester III - 2014 Pattern Course Name: CS-301 Software Metrics & Project Management
Sr.No.Course Outcomes (COs)
CO1: Demonstrate the skill to ensure successful medium and large scale projects
CO2: Relate requirement elicitation, project management, verification and validation of software engineering projects with real life projects
CO3: Select and use project management techniques for process modeling, planning,estimation,risk management, process and product metrics for successful quality project
CO4: Apply project management concepts and techniques to an IT project.
CO5: Identify issues that could lead to IT project success or failure.
CO6: Explain project management in terms of the software development process.
CO7: Describe the responsibilities of IT project managers.
CO8: Apply project management concepts through working in a group as team leader or active team member on an IT project.
Course Name: CS-302 Mobile Computing
Sr.No.Course Outcomes (COs)
CO1: Explain the concepts of android.
CO2: Explain the principles and theories of mobile computing technologies.
CO3: Describe infrastructures and technologies of mobile computing technologies.
CO4: List applications in different domains that mobile computing offers to the public, employees, and businesses.
CO5: Describe the possible future of mobile computing technologies and applications.
CO6: Effectively communicate course work through written and oral presentation
Course Name: CS-303 Soft Computing
Sr.No.Course Outcomes (COs)
CO1: Explain the concepts of how an intelligent system work and its brief development process.
CO2: Solve fuzzy set problems by recognizing fuzzy rules, approximate reasoning, fuzzy inference systems, and fuzzy logic
CO3: Explain the theory and concepts of neural networks
CO4: Use Soft computing techniques the solve character recognition, pattern classification, regression and similar problems.
CO5: List the facts and outline the different process carried out in fuzzy logic, ANN and Genetic Algorithms.
CO6: Outline facts to optimization. identify process/procedures to handle real world problems using soft computing.
Course Name: CS-304 Core Project
Sr.No.Course Outcomes (COs)
CO1: Identify, define and justify the scope of the proposed problem
CO2: Gather and analyze system requirements
CO3: Propose an optimized solution among the existing solutions
CO4: Practice software analysis and design techniques
CO5: Develop a functional application based on the software design
CO6: Apply coding, debugging and testing tools to enhance the quality of the software
CO7: Construct new software system based on the theory and practice gained through this exercise
CO8: Prepare the proper documentation of software projects following the standard guidelines
CO9: Learn technical report and oral presentation skills
Course Name: CS-307 Functional Programming
Sr.No.Course Outcomes (COs)
CO1: Discuss the basics of lambda calculus and combinator, how they are used to implement functional programming languages.
CO2: Summarise the main features of reduction strategies
CO3: Problem Solving using Python Programming
CO4: Programming Capabilities using Python Programing
CO5: Construct reduce code using functional features of Python Programming
CO6: Construct Object Oriented Programming features using Python Programming.
Course Name: CS-308 Business Intelligence
Sr.No.Course Outcomes (COs)
CO1: Explain the role of BI in enterprise performance management and decision support.
CO2: Describe the applications of data mining and intelligent systems in managerial work.
CO3: Distinguish between data warehousing and online analytical processing (OLAP) concepts, including dimensional modeling, star and snowflake schemas, attribute hierarchies, metrics, and cubes.
CO4: Reframe data analysis and reporting using available BI software.
CO5: Explain the foundations, definitions, and capabilities of DSS, data analytics and BI.
CO6: List the definitions, concepts, and architectures of data warehousing.
CO7: Demonstrate the impact of business reporting, information visualization, and dashboards.
CO8: Explain data mining, neural networks, support vector machines, text analytics, text mining, sentiment analysis, web mining, web analytics, social analytics, social network analysis