VTU PhD Entrance Test Computer Science/ Information Science Syllabus 2021

Research Methodology

PART - I

UNIT - 1

Research Methodology:

Introduction, Meaning of Research, Objectives of Research, Types of Research, Research Approaches, Significance of Research, Research Methods versus Methodology, Research and Scientific Method, Research Process, Criteria of Good Research, Problems Encountered by Researchers in India. Defining the Research Problem: Research Problem, Selecting the Problem, Necessity of Defining the Problem, Technique Involved in Defining a Problem.

 

UNIT - 2

Reviewing the literature:

Place of the literature review in research, Bringing clarity and focus to research problem, Improving research methodology, Broadening knowledge base in research area, Enabling contextual findings, Review of the literature, searching the existing literature, reviewing the selected literature, Developing a theoretical framework, Developing a conceptual framework, Writing about the literature reviewed. Research Design: Meaning of Research Design, Need for Research Design, Features of a Good Design, Important Concepts Relating to Research Design, Different Research Designs, Basic Principles of Experimental Designs, Important Experimental Designs.

 

UNIT - 3

Design of Sample Surveys:

Design of Sampling: Introduction, Sample Design, Sampling and Non-sampling Errors, Sample Survey versus Census Survey, Types of Sampling Designs. Measurement and Scaling: Qualitative and Quantitative Data, Classifications of Measurement Scales, Goodness of Measurement Scales, Sources of Error in Measurement, Techniques of Developing Measurement Tools, Scaling, Scale Classification Bases, Scaling Technics, Multidimensional Scaling, Deciding the Scale. Data Collection: Introduction, Experimental and Surveys, Collection of Primary Data, Collection of Secondary Data, Selection of Appropriate Method for Data Collection, Case Study Method.

 

UNIT – 4

Data Preparation:

Data Preparation Process, Some Problems in Preparation Process, Missing Values and Outliers, Types of Analysis, Statistics in Research. Descriptive Statistics: Measures of Central Tendency, Measures of Dispersion, Measures of Skegness, Kurtosis, Measures of Relationship, Association in case of Attributes, Other Measures.

 

UNIT - 5

Sampling and Statistical Inference:

Parameters and Statistic, Sampling and Non- Sampling Errors, Sampling Distribution, Degree of Freedom, Standard Error, Central Limit Theorem, Finite Population Correction, Statistical Inference.

 

UNIT - 6

Testing of Hypotheses:

Hypothesis, Basic Concepts Concerning Testing of Hypotheses, Testing of Hypothesis, Test Statistics and Critical Region, Critical Value and Decision Rule, Procedure for Hypothesis Testing, Hypothesis Testing for Mean, Proportion, Variance, for Difference of Two Mean, for Difference of Two Proportions, for Difference of Two Variances, P-Value approach, Power of Test, Limitations of the Tests of Hypothesis. Chi-square Test: Test of Difference of more than Two Proportions, Test of Independence of Attributes, Test of Goodness of Fit, Caution in Using Chi Square Tests.

 

UNIT – 7

Analysis of Variance (ANOVA):

The ANOVA Technique, Basic Principle of ANOVA, One way ANOVA, Two way ANOVA, Latin – square Design, Analysis of Co- Variance, Assumptions in Co-Variance.

 

UNIT – 8

Linear Regression Analysis:

Dependent and Independent Variables, Simple Linear Regression Model, Multiple Linear Regression Model, Problem of Multicolinearity, Qualitative Explanatory Variables. Factor Analysis: The Mathematical Basis, Important Methods of Factor Analysis, Rotation in Factor Analysis, R – Type and Q – Type Factor Analysis, Merits and Demerits of Factor Analysis.

 

UNIT – 9

Probability:

Random Experiments, Sample Spaces, Events, The Concept of Probability, The Axiomsof Probability, Theorems on Probability, Assignment of Probabilities, Conditional Probability, Theorems on Conditional Probability, Independent Events, Bayes’Theorem. Random Variables, Discrete Probability Distributions, Distribution Functions for RandomVariables, Distribution Functions for Discrete Random Variables, Continuous Random Variables, Graphical Interpretations, Joint Distributions Independent Random Variables, Change of Variables, Probability Distributions of Functions of Random Variables, Convolutions, Conditional Distributions.

 

UNIT - 10

The Binomial Distribution, Properties of the Binomial Distribution, The Normal Distribution, Properties of the Normal Distribution, Relation Between Binomial and Normal Distributions, The Poisson Distribution, Properties of the Poisson Distribution, Relation Between the Binomial andPoisson Distributions, Relation Between the Poisson and Normal Distributions, The Hypergeometric Distribution.

 

Reference Books:

(1) Research Methodology Methods and Techniques, C.R. Kothari, Gaurav Garg, New Age International Publishers,4th Edition, 2019.

(2) Research Methodology a step-by-step guide for beginners, Ranjit Kumar, SAGE Publications Ltd, 3rd Edition, 2011. [For Unit -2, Reviewing the Literature]

(3)Probability and Statistics, Murray R. Spiegel, Schaum’s Outline Series, McGraw-Hill, 4th Edition, 2013.[For Unit -9 and Unit – 10]

Computer Science/ Information Science

Part II

Unit 1

Data Structures and its applications:

introductions, primitive, arrays, strings, stacks recursion, queues, linked lists, trees, sorting and searching

Reference:

1.Erns Horowitz and Sartaj Salmi, Fundamentals of Data Structures in C, Universities Press,

2.Seymour Lipschutz, Data Structures Schaum's Outlines, McGraw 11111

 

Unit 2

Discrete Mathematical Structures:

Fundamentals of logics, properties of Integers, principles of counting, relations and functions, inclusion and exclusion, graph theory

Reference:

Ralph P, Grimaldi: Discrete and Combinatorial Mathematics, Pearson Education.

 

Unit 3

Software Engineering:

introduction, requirement engineering„ RUP, UML, software testing, project planning, agile software development.

Reference:

Ian Sommerville: Software Engineering„ Pearson Education

 

Unit 4

Computer Organization:

Machine instructions and programs, input/output organization, memory, Arithmetic, and basic processing unit

Reference:

Carl Hamacher, honk° Vranesic, Safwat Zaky, Computer Organization, 5th Edition, Tata McGraw Hill, 2002

 

Unit 5

Design and Analysis of Algorithms:

introduction to algorithms, performance, divide and conquer, greedy, dynamic programming, backtracking

Reference:

1. Introduction to the Design and Analysis of Algorithms, Anany Levitin. Pearson.

2. Computer Algorilhms/C4—I., Ellis Horowitz, Shirai Salmi and Rajasekaran,Universiuts Press

 

Unit 6

Operating system:

introduction, multi threaded programming, Deadlocks, virtual memory management, secondary storage structures and protections,

Reference:

Abraham Silberschatz, Peter Baer Galvin, Greg Gagne, Operating System Principles , Wiley-India.

 

Unit 7

Data Communication and Computer Network; Introduction, Digital transmission, bandwidth utilization, Data link control, media access control, wired LAN and Ethernet. Application layer, Transport layer, Network layer, Network security

Reference:

Behr= A, Forouzan, Data Communications and Networking

James F Kurose and Keith W Ross, Computer Networking, A Top•Down Approach, Sixth edition, Pearson

 

Unit 8

Object Oriented Modelling and Design(c++/Java):

Introduction, use case modelling, Process overview, use case realization, Design patterns

Reference:

1. Michael Olaha, James Rumbaugh: Object Oriented Modelling and Design with UML, Pearson Education

2.Erich Gamma. Richard I lelm. Ralph Johnson and john Vlissides: Design Patterns —Elements of Reusable Object-Oriented SoRwarc, Pearson Education

 

Unit 9

Data Base Management System:

Introduction, relational model, Relational algebra, SQL, Normalization, transaction processing, External Sorting.

Reference:

Fundamentals of Database Systems, Ram/. Elmasri and Shamkant II, Navathe„ Pearson,

 

Unit 10

System modelling and simulation:

introduction, statistical modelling, queuing models, random number generation, input modelling, estimation, verification, calibration and validation.

Reference:

Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol: Discrete-Event System Simulation,