Semester II

SYLLABUS

M.SC. (DATA SCIENCE) I-YEAR, II-SEMESTER

MDS-201: PAPER- I: ADVANCED MACHINE LEARNING TECHNIQUES

UNIT – I

Classification Techniques: Definitions, derivations, methods, properties, and applications of classifier algorithms: Logistic regression, Linear Discriminant Function (for binary outputs) with minimum mean squared error and using likelihood ratios (MVN populations), Bayes Mis-classification; Naïve Bayes classifier, Support Vector Machines, Decision Tree algorithms, Random Forest algorithm, Bootstrap algorithms (Bagging, Gradient, Ada- and XG-Boosting), KNN algorithm, Market-Basket Analysis.

UNIT – II

Multivariate Analysis Techniques: Definitions, goals, derivations, methods, properties, and applications of: Principal component analysis, Factor analysis, Multidimensional Scaling, Canonical Correlations and Canonical Variables, Conjoint Analysis, Path analysis, Correspondence analysis; Feature extraction and Feature selection techniques, Inter and intra class distance measures, Probabilistic distance measures.

UNIT - III

Cluster Analysis: Introduction, similarities, and dissimilarities, Hierarchical and non-hierarchical clustering methods, Single, complete, and average linkages, K-means, DBSCAN, CART, C4.5 methods.

Association Analysis: Problem definition, Frequent item set generation, Rule generation, Compact representation of frequent item sets, methods for generating frequent item sets, Aprori and FP-tree growth Algorithm.

REFERENCE BOOKS

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 - 1

Introduction: History Intelligent Systems, Foundations of Artificial Intelligence, Sub areas of Al, Applications. Problem-Solving - State - Space Search and Control Strategies: Introduction, General Problem-Solving Characteristics of problem, Exhaustive Searches, Heuristic Search Techniques, Iterative - Deepening A*, Constraint Satisfaction. Game Playing, Bounded Look-ahead Strategy and use of Evaluation Functions, Alpha Beta Pruning.

UNIT – II

Logic Concepts and Logic Programming: Introduction, Propositional Calculus Propositional Logic, Natural Deduction System, Axiomatic System, Semantic Table, A System in Propositional Logic, Resolution, Refutation in Propositional Logic, Predicate Logic, Logic Programming. Knowledge Representation: Introduction, Approaches to knowledge Representation, Knowledge Representation using Semantic Network, Extended Semantic Networks for KR, Knowledge Representation using Frames.

UNIT - III

Expert System and Applications: Introduction, Phases in Building Expert Systems Expert System Architecture, Expert Systems Vs Traditional Systems, Truth Maintenance Systems, Application of Expert Systems, List of Shells and Tools. Uncertainty Measure - Probability Theory: Introduction, Probability Theory, Bayesian Belief Networks, Certainty Factor Theory, Dempster - Shafer Theory.

REFERENCE BOOKS

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

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 other related theorems. Optimal Solution of LPP by graphical, Simplex and Charner’s & two-phase methods. Concept of degeneracy and resolving it Concept of duality of LPP. Dual Primal relationship, Complementary slackness theorems, Fundamental Theorem of Duality. Dual simplex Algorithm.

UNIT-II

Transportation problem: Concept of transportation problem, TPP as a special case of LPP, Initial basic feasible solutions by North-West Corner Rule, Matrix minimum method and VAM. Optimal solution of TPP through MODI method for balanced and unbalanced transportation problems.

Assignment problem: Concept of Assignment problem, Assignment problem as a special case of TP and LPP. Unbalanced assignment problem, optimal solution using Hungarian method and traveling salesman problem and its solution.

Problem of Sequencing: Optimal sequence of N jobs on two and three machines without passing.

UNIT-III

Integer Programming Problem: Gomory’s cutting plane Algorithm for pure and mixed IPP Branch and bound Technique.

Networks: Basic concepts of Networks constraints; Construction of Network and critical path; PERT and CPM; Network flow problems. Time Cost Analysis.

Queuing Theory: Introduction, essential features of the Queuing system, and the Operating characteristics of the Queuing system (transient and steady states). Queue length, General relationships among characteristics. Probability distribution in queuing systems, distribution of Arrival and interarrival. Distribution of death (departure) process, service time. Classification of Queuing models and solution of Queuing models; M/M/1:∞/FIFO and M/M/1:N/FIFO

REFERENCES

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

Software Engineering: The Nature of Software, Changing Nature of Software, Defining the Discipline, Software Process, Software Engineering Practice. The Software Process: A Generic Process Model, Defining a Framework Activity, Process Assessment and Improvement, Prescriptive Process Models, Specialized Process Models, Unified Process, Personal and Team Process Models. Defining Agility, Agile Process, Extreme Programming, Psychology of Software Engineering, Software Team Structures, Software Engineering Using the Cloud, Global Teams.

UNIT – II

Requirements: Core Principles of Modeling, Requirements Engineering, Establishing the Groundwork, Eliciting Requirements, Developing Use Cases, Building the Analysis Model, Requirements Analysis, UML Models That Supplement the Use Case, Identifying Analysis Classes, Specifying Attributes, Defining Operations, Class Responsibility-Collaborator Modeling, Associations and Dependencies, Analysis Packages. Design Concepts: Design within the Context of SE, Design Process, Design Concepts, Design Model, Software Architecture, Architectural Styles, Architectural Considerations, Architectural Design, Component, Designing Class-Based Components, Conducting Component-Level Design, Component-Based Development, User Interface Design Rules.

UNIT – III

Quality Management: Quality, Software Quality, Software Quality Dilemma, Achieving Software Quality, Defect Amplification and Removal, Reviews, Informal Reviews, Formal Technical Reviews, Elements of Software Quality Assurance, SQA Tasks, Goals, and Metrics, Software Reliability, A Strategic Approach to Software Testing, Test Validation Testing, System Testing, Debugging, Software Testing Fundamentals, White-Box Testing, Black-Box Testing, Path Testing, Control Structure Testing, Object-Oriented Testing Strategies & Methods, Security Engineering Analysis, Security Assurance, Security Risk Analysis.

REFERENCE BOOKS

1.      Roger S Pressman, B R Maxim, Software Engineering – A Practitioner’s Approach (8e)

2.      Ian Sommerville: “Software Engineering”.

3.      Hans Van Vliet, Software Engineering.

4.      D. Bell, Software Engineering for Students.

5.      K.K. Aggarwal, Y. Singh, Software Engineering.

6.      R. Mall, Fundamentals of Software Engineering

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 writing Python code based on the procedure rather than the 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.

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.