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SYLLABUS
M.SC. (DATA SCIENCE) I-YEAR, II-SEMESTER
MDS-201: PAPER- I: ADVANCED MACHINE LEARNING TECHNIQUES
UNIT – I
Introduction to Classification: Definitions, concepts, and goals, Classifier algorithms and their applications, Logistic Regression, Linear Discriminant Function for binary outputs: Minimum Mean Squared Error approach; Likelihood Ratios and MVN Populations, Bayes Misclassification and Naïve Bayes Classifier, Support Vector Machines (SVMs) , Evaluation measures for classification performance
UNIT – II
UNIT – II
Decision Tree Algorithms and Random Forest Algorithm, Bootstrap Algorithms: Bagging, boosting (Gradient, Ada-, and XG-Boosting), K-Nearest Neighbor (KNN) Algorithm, Market Basket Analysis: Concepts and applications, Practical considerations in classifier selection and implementation
UNIT – III
UNIT – III
Multivariate Analysis Techniques: Definitions, goals, and applications of multivariate analysis Principal Component Analysis (PCA), Factor Analysis, Multidimensional Scaling (MDS), Canonical Correlations and Canonical Variables, Conjoint Analysis, Path Analysis, and Correspondence Analysis, Feature Extraction and Feature Selection Techniques, Inter- and Intra-class distance measures; Probabilistic distance measures
Multivariate Analysis Techniques: Definitions, goals, and applications of multivariate analysis Principal Component Analysis (PCA), Factor Analysis, Multidimensional Scaling (MDS), Canonical Correlations and Canonical Variables, Conjoint Analysis, Path Analysis, and Correspondence Analysis, Feature Extraction and Feature Selection Techniques, Inter- and Intra-class distance measures; Probabilistic distance measures
UNIT – IV
UNIT – IV
Cluster Analysis: Cluster Analysis: Introduction similarities and Dissimilarities, Hierarchical and Non-hierarchical Clustering Methods, Single, Complete and Average Linkages; K- Means, DBSCAN, CART, and C4.5 Methods
Cluster Analysis: Cluster Analysis: Introduction similarities and Dissimilarities, Hierarchical and Non-hierarchical Clustering Methods, Single, Complete and Average Linkages; K- Means, DBSCAN, CART, and C4.5 Methods
Association Analysis: Problem Definition; Frequent Itemset Generation and Rule Generation; Compact Representation of Frequent Itemsets; Methods for Generating Frequent Itemsets; Apriori and FP-Tree Growth Algorithms
Association Analysis: Problem Definition; Frequent Itemset Generation and Rule Generation; Compact Representation of Frequent Itemsets; Methods for Generating Frequent Itemsets; Apriori and FP-Tree Growth Algorithms
Suggested Readings
1. Johnson, R.A, and Dean W. Wichern: Applied Multivariate Statistical Analysis.
2. Morrison, D: An Introduction to Multivariate Analysis.
3. Seber: Multivariate Observations
4. Anderson: An Introduction to Multivariate Analysis.
5. Bishop: Analysis of Categorical data.
SYLLABUS
M.SC. (DATA SCIENCE) I-YEAR, II-SEMESTER
MDS-202-T: PAPER- II: ARTIFICIAL INTELLIGENCE
UNIT – I
Introduction: History of Intelligent Systems, Foundations of Artificial Intelligence, Sub-areas of AI, Applications of AI.
Problem Solving and State-Space Search: Introduction to Problem Solving, State-Space Search, Characteristics of Problems, Exhaustive Search Techniques (DFS, BFS).
Heuristic Search Techniques: Concept of Heuristics, Iterative Deepening A*, Constraint Satisfaction Problems (CSPs).
UNIT – II
Game Playing: Bounded Look-ahead Strategy, Evaluation Functions, Alpha-Beta Pruning, Minimax Algorithm.
Logic Concepts and Logic Programming: Introduction to Logic, Propositional Calculus, Propositional Logic, Natural Deduction System, Axiomatic System, Semantic Tableaux, Resolution, and Refutation in Propositional Logic.
UNIT – III
Predicate Logic: Syntax and Semantics, Inference in Predicate Logic, Unification, Logic Programming Concepts.
Knowledge Representation (KR): Introduction, Approaches to Knowledge Representation, Semantic Networks, Extended Semantic Networks, Frames and Frame-based Systems.
UNIT – IV
Expert Systems: Introduction, Phases in Building Expert Systems, Expert System Architecture, Expert Systems vs. Traditional Systems, Truth Maintenance Systems, Applications of Expert Systems, Shells and Tools.
Uncertainty Measures: Introduction to Uncertainty, Probability Theory, Bayesian Belief Networks, Certainty Factor Theory, Dempster–Shafer Theory.
Suggested Readings
Suggested Readings
1. Saroj Kaushik, Artificial Intelligence, Cengage Learning India, First Edition, 2011.
2. Russell, Norvig, Artificial Intelligence: A Modern Approach, Pearson Education, 2nd Edition, 2004.
3. Rich, Knight, Nair, Artificial Intelligence, Tata McGraw Hill, 3rd Edition 2009
SYLLABUS
M.SC. (DATA SCIENCE) I-YEAR, II-SEMESTER
MDS-203: PAPER-III: OPTIMIZATION TECHNIQUES
UNIT – I
UNIT – I
Introduction to Optimization and Linear Programming: Meaning and scope of Optimization Techniques. Convex sets and their properties. General Linear Programming Problem (LPP). Formulation of LPP. Statements of Fundamental Theorem of LPP and related theorems. Optimal solution of LPP by Graphical Method Simplex Method Charner’s and Two-Phase Methods Concept of degeneracy and resolving it.
Introduction to Optimization and Linear Programming: Meaning and scope of Optimization Techniques. Convex sets and their properties. General Linear Programming Problem (LPP). Formulation of LPP. Statements of Fundamental Theorem of LPP and related theorems. Optimal solution of LPP by Graphical Method Simplex Method Charner’s and Two-Phase Methods Concept of degeneracy and resolving it.
UNIT – II
UNIT – II
Duality and Transportation & Assignment Problems: Concept of duality in LPP. Dual–Primal relationship. Complementary Slackness Theorems. Fundamental Theorem of Duality. Dual Simplex Algorithm.
Duality and Transportation & Assignment Problems: Concept of duality in LPP. Dual–Primal relationship. Complementary Slackness Theorems. Fundamental Theorem of Duality. Dual Simplex Algorithm.
Transportation Problem (TPP): Concept and formulation as a special case of LPP. Methods to find initial basic feasible solution, North-West Corner Rule, Matrix Minimum Method, Vogel’s Approximation Method (VAM) , Optimal solution through MODI method for balanced and unbalanced TPP.
Transportation Problem (TPP): Concept and formulation as a special case of LPP. Methods to find initial basic feasible solution, North-West Corner Rule, Matrix Minimum Method, Vogel’s Approximation Method (VAM) , Optimal solution through MODI method for balanced and unbalanced TPP.
Assignment Problem (AP): Concept and formulation as a special case of TPP and LPP. Unbalanced Assignment Problem. Optimal solution using Hungarian Method. Travelling Salesman Problem (TSP) – concept and solution approach.
Assignment Problem (AP): Concept and formulation as a special case of TPP and LPP. Unbalanced Assignment Problem. Optimal solution using Hungarian Method. Travelling Salesman Problem (TSP) – concept and solution approach.
UNIT – III
UNIT – III
Sequencing, Integer Programming and Network Models: Problem of Sequencing, Optimal sequence of N jobs on two and three machines without passing.
Sequencing, Integer Programming and Network Models: Problem of Sequencing, Optimal sequence of N jobs on two and three machines without passing.
Integer Programming Problem (IPP): Concept and formulation. Gomory’s Cutting Plane Algorithm for pure and mixed IPP. Branch and Bound Technique.
Network Models: Basic concepts of networks and constraints. Construction of network and critical path. PERT and CPM techniques. Network flow problems. Time–Cost Analysis.
UNIT – IV
UNIT – IV
Queuing Theory: Introduction and essential features of a queuing system. Operating characteristics (transient and steady states). Queue length and general relationships among characteristics.
Queuing Theory: Introduction and essential features of a queuing system. Operating characteristics (transient and steady states). Queue length and general relationships among characteristics.
Probability distributions in queuing systems: Arrival and interarrival time distributions. Service time and departure (death) process distributions.
Classification of Queuing Models: Solutions of Queuing Models, M/M/1: ∞ / FIFO model
M/M/1: N / FIFO model
Suggested Readings
Suggested Readings
1. Kantiswarup; Gupta P.K. and Singh, M.N. (1985): Operations Research; Sultan Chand
2. Taha, H.A. (1982): Operations Research: An Introduction; MacMillan
3. Sharma, S.D.: Operations Research.
4. Hillier F.S. and Leiberman, G.J. (1962): Introduction to Operations Research; Holdon Day
SYLLABUS
M.SC. (DATA SCIENCE) I-YEAR, II-SEMESTER
MDS-204-T: PAPER- IV: SOFTWARE ENGINEERING
Unit- I
Fundamentals Of Software Engineering: Introduction to Software Engineering: The nature of Software, Software Application Domains, The Software Process, Software Engineering Practice.
Software Process Structure: A Generic Process Model, Process Framework, CMMI, Prescriptive Process Models: Waterfall Model, Incremental Process Models, Evolutionary Process Models – Prototyping, The Spiral Model, Concurrent Models; An agile view of Process: Agility, Agile Process and Agile Process Models –Extreme Programming (XP).
Unit- II
Requirement Engineering: – Understanding Requirements: Establishing the Groundwork, Requirement Engineering tasks, Initiating the Requirements Engineering Process, Eliciting Requirements, Feasibility Study, Software Requirements Analysis and Specification: Software Requirements, Problem Analysis, Requirements Specification.
Case Studies: Planning and managing the project: Managing Software Project, Project Personnel, Effort Estimation, Risk Management, the project plan, Software project estimation – Empirical estimation models.
Unit- III
Design Engineering: Design Principles, Design Notation and Specification, Design concepts, Flow oriented modelling; The function-oriented design for the case studies; OO Design Concepts; Architectural Design: Software Architecture, Data Design, Modelling Component-Level Design. A brief Taxonomy of Architectural Styles. Implementation: Coding Principles and Standards, Coding Process, Code Verification.
Unit- IV
Software Testing Strategies: Unit, Integration, System, and Acceptance Testing, Black-box & White-box Testing, Equivalence Partitioning, Boundary Value Testing Test Automation, Continuous Integration (CI), Test-driven Development (TDD) Software Quality Assurance: Quality Planning, Reviews, Reliability, ISO Standards
Software Configuration Management (SCM): Version Control, Change Management, Configuration Audits.
Software Evolution: Categories of Maintenance, Legacy System Management, Reengineering, Software Reuse
Suggested Readings:
1. Roger S. Pressman, “Software Engineering: A practitioner’s approach”, 8th Edition McGraw Hill,
2. Sommerville “Software Engineering”, 10th Edition, Pearson,2015
3. Shari Lawrence Pfleeger, “Software Engineering Theory and Practices”, 4th Edition, Pearson Education, India, 2011.
4. Pankaj Jalote” An integrated approach to Software Engineering”, Springer/ Narosa, 2014
SYLLABUS
M.SC. (DATA SCIENCE) I-YEAR, II-SEMESTER
MDS-205-P: PAPER- V (PRACTICAL-1):
ADVANCED MACHINE LEARNING USING PYTHON LAB
List of Practical’s using Python
Note: Preferably write Python code based on the procedure rather than usage of packages.
1. Implementation of classification techniques for the data sets and evaluation of its analysis using
(i) Logistic regression
(ii) Naïve Bayes classifier
(iii) Support Vector Machines,
(iv) Decision tree (ID-3) algorithms
(v) Random forest algorithm
(vi) Bagging and Boosting algorithms
(vii) KNN
2. Implementation of
(i) Principal component analysis
(ii) Factor analysis,
(iii) Multidimensional Scaling,
(iv) Path analysis,
3. Implementation of clustering using
(i) K-means,
(ii) DBSCAN,
(iii) CART,
(iv) C4.5 methods
4. Implementation of Association algorithms
(i) Apriori algorithm
(ii) FP-tree growth algorithm.
Case Studies : Students shall perform the following exercises for any two Case Studies selected from the following list.
List Of Projects :
SYLLABUS
M.SC. (DATA SCIENCE) I-YEAR, II-SEMESTER
MDS-206-P: PAPER-VI (PRACTICAL-2):
ARTIFICIAL INTELLIGENCE USING R / PYTHON LAB
Programs List using R and Python
1. Implementation of A* and AO* algorithms.
2. Implementation of Alpha-beta pruning.
3. Implementation of search algorithms (BFS & DFS).
4. Implementation of Hill Climbing algorithm
5. Implementation of Gaming problems:
(i) Tower of Hanoi problem
(ii) Tic-Tac-Toe problem
(iii) Water-Jug problem.
(iv) 4-Queens problem.
(v) 8 Puzzle problems.
(vi) Monkey banana problem
SYLLABUS
M.SC. (DATA SCIENCE) I-YEAR, II-SEMESTER
MDS-207-P: PAPER- VII (PRACTICAL-3)
Optimization Techniques Using TORA & R
Programs List using R and TORA
1. Optimal Solution to a L.P.P. by
(i) Graphical method
(ii) Simplex method
(iii) Charners Method
(iv) Two-phase simplex method
(v) Dual Simplex Method
(vi) Duality
2. Transportation problem by MODI & stepping stone methods. (Balanced and unbalanced)
3. Assignment problem by Hungarian method (Balanced and unbalanced)
4. Traveling salesman problem by Hungarian method
5. Job sequencing problem for N jobs on 2 and 3 machines
6. Integer Programming Problem (Gomery’s cutting plane and Branch & Bound methods)
7. Construction of Network diagram and finding Critical path using CPM and PERT.
8. Evaluation of Time cost analysis through CPM and PERT
M.SC. (DATA SCIENCE) I-YEAR, II-SEMESTER
MDS-208-P: PAPER- VIII (PRACTICAL-4):
DATA HANDLING USING SPSS
List of Practical’s
1. Basic operations of Data entry, Data import and export, I/O files handling etc.
2. Data Visualization: Pie diagram, Bar diagram, Histogram, Line plot, frequency curves & polygons, Scatter Plot, Gantt Chart, Box Plot.
3. Descriptive Statistics: Measures of Central Tendencies, Dispersions, Relative measures of Dispersions, Moments, Skewness, Kurtosis.
4. Parametric Tests: Testing for Mean(s), Variance(s), Proportion(s), ANOVA for one-way two-way and two-way with one and m-observations per cell
and with & without interactions,
5. Non–Parametric tests: Sign test, Wilcoxon Sign Rank test, Mann-Whitney U-test, Run test, Kolmogorov Smirnov test, Chi-square test for goodness
of fit and Chi-square test independence.
6. Design and Analysis of Experiments: Analysis of Variances for Completely Randomized, Randomized Block, and Latin Square Designs.
7. Regression Analysis: Analysis of Simple and Multiple Linear Regression models, Selection of Best Linear Regression Model (All possible, forward, backward, stepwise, and stage-wise methods). Binary and multinomial Logistic regression models, Probit analysis.
8. Multivariate Data Analysis: Linear Discriminant Analysis, Principal Component Analysis, Factor analysis, multi-dimensional scaling, Cluster analysis.
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