I Year

M.Sc.(Data –Science)-Semester-I

Course Code : MDS-101

Course Name :  Mathematical Foundations For Data Science

Course Outcomes: 

CO1 ;Demonstrate understanding of vector spaces, matrices, and transformations relevant to data science.

CO2 :Apply matrix operations, decompositions, and transformations to solve practical problems.

CO3 :Use combinatorial principles to count, arrange, and analyze discrete structures in problem-solving.

CO4 : Apply graph theory concepts to solve problems in networking, data modeling, and optimization. Implement graph algorithms      like spanning tree construction and graph coloring in real-time contexts.

Course Code: MDS-102

Course Name: Design And Analysis Of Algorithms

Course Outcomes

CO1 : Analyze algorithm performance and apply complexity analysis techniques. Apply divide-and-conquer methods to solve searching, sorting, and matrix problems

CO2 : Implement greedy algorithms for optimization problems such as MST and shortest path. Apply dynamic programming to solve 

CO3 : Classify problems into P, NP, NP-Hard, and NP-Complete categories and understand their implications

CO4 : Design solutions for combinatorial problems using backtracking and branch-and-bound methods

Course Code: MDS-103

Course Name: Java Programming

Course Outcomes

CO1: Apply the core concepts of Object-Oriented Programming (OOP) including encapsulation, inheritance, and polymorphism to design and implement modular, reusable, and maintainable Java applications.

CO2: Develop interactive Graphical User Interface (GUI) applications using AWT and Swing components, incorporating event handling mechanisms to manage user interactions effectively.

CO3: Implement Java Database Connectivity (JDBC) for establishing database connections, executing SQL queries, and processing result sets to develop data driven applications.

CO4: Design and deploy dynamic web applications using Servlets and Java Server Pages (JSP) with effective client server communication, session handling, and content generation.

Course Code: MDS-104

Course Name: Statistical Inference

Course Outcomes

CO1 : Understand basic concepts of estimation theory and properties of estimators.

CO2 : Apply estimation methods and resampling techniques to solve statistical problems.

CO3 : Analyze hypotheses using parametric and non-parametric statistical tests.

CO4 : Apply non-parametric density estimation and random number generation techniques.

   M.Sc.(Data –Science)-Semester-II

              Course Code: MDS-201

              Course Name: Advanced Machine Learning Techniques

              Course Outcomes:

               CO1 :Explain the principles and working of major classification and ensemble learning algorithms.

            CO2:Apply classification techniques and multivariate analysis methods to real-world datasets.

            CO3 :Analyze high-dimensional data using feature extraction, distance measures, clustering, and association analysis techniques.

            CO4 :Design and evaluate appropriate machine learning models for pattern discovery and decision-making problems 

             Course Code:MDS-202

             Course Name: Artificial Intelligence

             Course Outcomes

              CO1: Understand the fundamentals of Artificial Intelligence, including its history, problem-solving methods, and state-space representation.
            CO2: Apply various search techniques and game-playing strategies such as heuristic search, A* algorithm, and alpha-beta pruning.
            CO3: Analyze logic-based systems and knowledge representation methods using propositional logic, predicate logic, and semantic structures.
            CO4: Evaluate expert systems and uncertainty models, including Bayesian networks and other probabilistic reasoning techniques.

             Course Code: MDS-203

            Course Name: Statistical Pattern Recognition

            Course Outcomes

              CO1: Understand fundamental concepts of Statistical Pattern Recognition, including pattern recognition systems, linear classifiers, and Bayesian         

                     decision theory
            CO2: Analyze and evaluate classification techniques such as Nearest Neighbor, Bayes classifier, and error estimation methods for different probability                       distributions.
            CO3: Apply advanced models like Hidden Markov Models, Neural Networks, and Support Vector Machines for solving pattern recognition problems.
CO4: Perform feature selection and extraction techniques and implement real-world applications such as handwritten digit recognition.

         Course Code: MDS-204

         Course Name: Software Engineering

         Course Outcomes

          CO1: Understand software engineering principles, process models, and agile methodologies for developing modern software systems.
        CO2: Apply requirements engineering and analysis modeling techniques using UML to design effective software architectures.

        CO3: Design software systems using architectural, component-level, and user interface design principles.
        CO4: Evaluate software quality through testing, quality assurance practices, and security engineering techniques.