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Old January 18th, 2013, 02:00 PM
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Default Prepare For M.Phil Computer Science Entrance Exam

Please give some information relate to the Bharathidasan University M.Phil Computer Science Entrance Exam?

I want to take admission in the Govt. Holkar Autonomous Science College, Indore – M.Phil. (Computer
Science)? So I am searching for the syllabus of the Govt. Holkar Autonomous Science College M.Phil Computer Science Entrance Exam?

The Govt. Holkar Science College, Indore (M.P.) was established in 10th of June ,1891. The college is affiliated to the University of Calcutta.

Eligibility for M.Phil. (Computer Science ) :

The applicant must have Master's Degree in relevant subject with minimum 55 % marks from a recognized University.

Age limit-
No student above 22 in the first year or above 27 in the P.G. classes will be admitted. Age will be calculated as on 1 July. SC, ST, OBC, Disabled and Women students will have relaxation of 3 more years.

Important dates:

M.Phil Entrance Exam Date was on(01-07-2014)

M.Phil Entrance Exam notice

You are searching for the Govt. Holkar Autonomous Science College M.Phil Computer Science Entrance Exam syllabus. Here I am uploading a file that contains the syllabus. This will helps you in preparation of your exam. This is as follows:

Govt. Holkar Autonomous Science College M.Phil Computer Science Entrance Exam syllabus

UNIT- I: Introduction to research
Definition, objectives, motivation, types of research, approaches, utility of research. Qualities
of a good researcher, Problems encountered by researchers in India, Hypothesis development.
Measurement of variables – scales and measurements of variables. Developing scales: rating
scale and attitudinal scales. Validity testing of scales developed, Reliability concept in the
scales being developed. Stability measures.

UNIT- II: Data collection methods
Types of data – Collection and presentation of data (Table, Graphs, Diagrams), Schedule
(purpose, essentials, procedure, design), Interviews questionnaires, Guidelines for
Questionnaire design – electronic questionnaire design and surveys. Observation, inferences,
Special date sources: focus groups, static and dynamic panels, sampling techniques.
Probabilistic and non –probabilistic samples, Issues of precision and confidence in
determining sample size, Hypothesis testing, Determination of optimal sample size.

UNIT- III: Statistical Techniques and quantitative method
Use of quantitative method in research, Data analysis for specific type of data, Tabulation and
graphical representation, Sampling techniques, measures of sampling tendencies, Standard
Deviation and Standard error, Correlation and Regression, testing significance, level of
significance. Students T test, chi-square test, F test and Analysis of variance, Basic
knowledge of computer Statistical Programs-Prism, Sigma plot, SPSS. Non parametric or
free distribution tests, testing of hypothesis for non-parametric data.
Data Analysis: Mathematical and statistical analysis using software tools like MAT Lab,
SPSS or free wares tools.

UNIT- IV: Computer applications
Using Computers: Importing and exporting of computer data – a knowledge of .PDF and
.html formats, using notepad/word pad, MS Access and Adobe PageMaker, basic knowledge
of programming and data processing, Two dimensional and three dimensional plots,

Excel and Origin for graphical representations and computation, using SPSS and Mat lab,
using internet and search engines, using power-point / flash / video for making deliberations.
Internet and Intranet: HTML, Web pages, creating a web page using MS Front page, adding
graphics and images, Current web technologies. Hosting a web site. Advance search
techniques, case studies: Google & Yahoo and Google Scholar. Building an Intranet. Word
Processing advance features helpful in preparing thesis in MS-Word
Data Analysis and Display: Facilities in MS Excel for Data analysis and display, What-if-
analysis/ data analysis in worksheet using MS-Excel, Other data analysis and display
software's, case study: Origin . Software for, Scientific and Statistical Analysis: Case studies:
SPSS Database: Creating a Database and simple Querying, Graphics and Drawing: Adobe
Photoshop: Basics (only Introductory, level), Image compression (GIF, JPEG, PNG formats),
Multimedia, Digital Arts, Audio and Video formats, Multimedia Projections.

UNIT- V: The Research Reporting & Review of Literature
Preparation of synopsis and report, Significance of Report Writing – Different Steps in
writing Report – Layout of the Research Report – Types of Reports – Oral Presentation –
Mechanics of Writing a research Report – Precautions for Writing Research Reports, Writing
of a research paper, Literature collection ( textual and digital resources), citation styles (
Journals, book and reports), Manuscript preparation ( data presentation, editing and proof
correction), abstract preparation and abstracting services
Review of published research in the relevant field.

Books recommended
1. Donald R. Cooper and remela S. Schindler, Business Research Methods, Tata
McGraw Hill publishing company limited, New Delhi, 2000.
2. C.R. Kothari, Research Methodology, Wishva Prakashan, New Delhi,
3. Donald H. McBurney, research methods, Thomson Asia Pvt. Ltd. Singapore, 2002
4. G.W. Ticehurst and A.J. Veal, Business research methods, Longman, 1999.
5. Ranjit Kumar, Research methodology, Sage Publications, London, New Delhi, 1999
6. Information Communication Technology by Tim Shortis.
7. Handbook of Communication and Social Interaction Skills By John Page 5

M.Phil. - Computer Science

Introduction : Data Mining: Definitions, KDD v/s Data Mining, DBMS v/s Data Mining ,
DM techniques, Mining problems, Issues and Challenges in DM, DM Application areas.
Association Rules & Clustering Techniques: Introduction, Various association algorithms
like A Priori, Partition, Pincer search etc., Generalized association rules.

Clustering paradigms; Partitioning algorithms like K-Medioid, CLARA, CLARANS;
Hierarchical clustering, DBSCAN, BIRCH, CURE; categorical clustering algorithms,
STIRR, ROCK, CACTUS. Other DM techniques & Web Mining: Application of Neural
Network, AI, Fuzzy logic and Genetic algorithm, Decision tree in DM. Web Mining, Web
content mining, Web structure Mining, Web Usage Mining.

Temporal and spatial DM: Temporal association rules, Sequence Mining, GSP, SPADE,
SPIRIT, and WUM algorithms, Episode Discovery, Event prediction, Time series analysis.
Spatial Mining, Spatial Mining tasks, Spatial clustering, Spatial Trends.

Data Mining of Image and Video : A case study. Image and Video representation techniques,
feature extraction, motion analysis, content based image and video retrieval, clustering and
association paradigm, knowledge discovery.

The vicious cycle of Data mining, data mining methodology, measuring the effectiveness of
data mining data mining techniques. Market baskets analysis, memory based reasoning,
automatic cluster detection, link analysis, artificial neural networks, generic algorithms, data
mining and corporate data warehouse, OLA
Reference Books:
1. Data Mining Techniques ; Arun K.Pujari ; University Press.
2. Data Mining; Adriaans & Zantinge; Pearson education.
3. Mastering Data Mining; Berry Linoff; Wiley.
4. Data Mining; Dunham; Pearson education. Page 6

M.Phil. - Computer Science

Object Oriented Concepts and Modeling Techniques Modeling, objects and classes,
Relationships, Inheritance, Association, aggregation, Containers, Delegation, Metadata,
Abstract methods and Classes.

Object modeling, Dynamic modeling, Events, Status, Scenarios, Event hate diagrams,
Operations, State diagrams, Functional Models, Dataflow diagrams, Constraints
specification, Relation of object, Functional and Dynamic models.

Design Methodology: OMT methodology, Analysis, Overview of system design, Subsystem,
concurrency, Common architectural frameworks designing algorithm, Design optimization,
Implementation of control, Design of Associations, Object design, Class design, Comparison
of design methodology with SASD, JSD and others.

Implementation Programming style, Reusability, Extensibility, Programming in the large,
Translating a design into an Implementation class definition, Object oriented Language
features, Survey of object-oriented languages, Object storage and relation with database.

Advanced Topics Distributed objects, Components development, Introduction to Distributed
object system like CORBA, EJB, COM+, DCOM, and other design architectures.
G. Booch, Object-Oriented Analysis and Design, Pearson Education.
J. Rumbaugh, Object-Oriented Modeling and Design, Pearson Education

Government Holkar Science College
(An Autonomus Institute and Centre of Excellence)
A.B. Road, Indore (M.P.) 452017 INDIA
Ph. 0731-464074

The Bharathidasan University M.Phil Computer Science Entrance Exam information is as follows:

M.Phil Computer Science Entrance Exam

Duration: 1 Year

The applicants should have a Masters degree in science majoring Computer Science, IT or Computer Application or its equivalent with 60% or Any Masters degree with PGDCA.

Admission procedure:

The admission to the M.Phil course is based on the marks get in an entrance examination, the qualifying examination marks and the performance in the viva voce.

The Rank list will publish accordingly.

Entrance exam pattern:

The entrance examination is an objective type one.
The written examination will be for 75 marks.
The interview is of 25 marks for preparing the final score.

Admission dates:
Here I am giving the tentative dates for M.Phil admission:

The Last date for receipt of completed application forms:
Last week of July 2013

Application form:
Here I am attachment of the BU M.Phil admission application form:
Attached Files Available for Download
File Type: pdf BU M.Phil Application form.pdf (566.5 KB, 304 views)

Last edited by Aakashd; December 22nd, 2019 at 03:52 PM.
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Old February 3rd, 2015, 04:59 PM
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Join Date: Feb 2015
Default MPhil computer science entrance exam

I need MPhil computer science entrance model question paper
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Old February 26th, 2015, 03:00 PM
Default mphil entrance exam

Mphil entrance exam sample question paper in computer science please attach the question
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Old July 18th, 2015, 09:46 PM
poongodi chml
Default Re: Prepare For M.Phil Computer Science Entrance Exam

Please give some information relate to the Bharathidasan University M.Phil Computer Science Entrance Exam?
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Old August 21st, 2015, 05:32 AM

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Old August 21st, 2015, 05:35 AM

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Old August 22nd, 2015, 09:20 AM

I want mphil entrance questions paper for computer science
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Old October 28th, 2015, 12:17 AM

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Old August 8th, 2019, 04:40 PM
Default Re: Prepare For M.Phil Computer Science Entrance Exam

Hi buddy I have applied for Dravidian University, [DU] Kuppam M.Phil Computer Science Entrance Exam and for its exam preparation here I am looking for its exam syllabus for this exam preparation so will you plz provide me same here :
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Old August 8th, 2019, 04:42 PM
Super Moderator
Join Date: Jun 2013
Default Re: Prepare For M.Phil Computer Science Entrance Exam

As you are asking for Dravidian University, [DU] Kuppam M.Phil Computer Science Entrance Exam syllabus so here I am providing same here :

Dravidian University, [DU] Kuppam M.Phil Computer Science Entrance Exam syllabus
Research Methodology

Thesis Writing, Analysis of Algorithms, Formal Languages and Automata, Probability and Statistical Analysis, Logics, Relations and Functions

To impart the basic concepts on algorithms, formal languages and Automata, probability and statistics, logic, relations and functions which are required for research and to give knowledge on thesis writing.

Concepts In Computer Science

Computer Architecture, Distributed Databases, Communication Protocols, Computer Graphics and Multimedia, Web Technology

To impart knowledge on the some of the advance topics in Computer Science such as computer architecture, distributed databases, communication protocols, Computer graphics and Web Technology.

Artificial Intelligence & Expert Systems

Artificial Intelligence, Knowledge Representation, Natural language processing, Expert Systems, Knowledge Base and chaining functions

The objective of this course is to educate students about different types of expert systems in computers.

Simulation And Modeling

Introduction to Simulation, Statistical Models in Simulation, Random - Number Generation, Input Modeling : Data Collection, Comparison of Two System Designs

Students are taught about the simulation and modelling techniques.

Grid Computing

Grid Computing: Early Grid Activities, Merging the Grid Services Architecture with the Web Services Architecture, Open Grid Services Infrastructure (OGSI), The Grid Computing Tool Kits

Students learn grid computing and its background and detailed knowledge.

Data Mining

Introduction to Data Mining, Data Processing: Cleaning, Concept description, Classification and prediction, Multidimensional analysis and descriptive mining of complex data objects

The objective of this course is to provide students a detailed knowledge about concepts of data mining.

Wireless Networks And Security

Overview of Wireless Networks, Network Planning, CDMA Technology, IEEE 802.11 WLANs, Case studies

The subject covers various topics of wireless networks and its mechanism.

Object Oriented Database Systems

Object Oriented System, Object Modeling, Object -Oriented- Databases, Transactions, Parallel Databases

The main objective of the course is to make students familiar with object oriented database systems.

Artificial Neural Networks

Characteristics of biological Neuron, Back Propagation network, Statistical methods

The subject covers the concepts of artificial neural networks along with its principles and elements.
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