VALUE ADDED COURSES (R15-R20)

Career Oriented Course (COC)

Course Title: Advanced MS Excel & R-Programming         

Course Objective: The objective of this course is to provide the student with a practical knowledge of MS Excel, VBA, and R-programming, which are very widely used in IT industry- Data Analysis and Data Mining. 

Course Outcomes: 

At the end of this course, a student is expected to develop and process spread sheets in MS Excel, develop micro programming-Visual Basic Applications, use the required Statistical/Mathematical/Financial and other functions effectively.

The student is also expected to use R-programming effectively to do the required statistical analysis. 

CO1: demonstrating the basic mechanics and navigation of an MS Excel spreadsheet, understand and create simple data tables and PivotTables and creating lookups using VBA.

CO2: demonstrating overview, environment setup, basic syntax and data types, understanding basic syntax and data interfaces of R programming, 

CO3: construct appropriate charts using MS Excel and R Programming.

SYLLABUS

 List of References:

1. Mark Gardener: Beginning R: The Statistical Programming Language
2. Garrett Grolemund  : Hands-On Programming with R: Write Your Own Functions and Simulations  by  Levin, Stephan, Krehbiel, Berenson
3. Statisitcs for Managers using Microsoft Excel. 4^th edition. Pearson publication
4. Gerald Keller:Appled Statistics with MS-Excel. Duxbury, Thomson Learning
5. Norman Matloff : The Art of R Programming 
6. P.G.Dixit, V.R.Pawgi & P.S.Kapre: Statistical Methods and Use of R-Software, Nirali Prakasan Publishers
7. Vishwas R.Pawgi: Statistical Computing Using R-Software

VALUE ADDED COURSES (R15 – R20)

Inter Disciplinary Course (IDC)

Course Title: Basic Statistics & Data Analysis (IDC)

Course Code: IDC 501

Course Objective: The objective of this interdisciplinary course is to give statistical knowledge to non-statistics students, which helps them to perform basic data analysis reqired for project works, research paper presentations/publications.

Course Outcomes

CO1: acquire knowledge of the importance of statistics in various domains, list various sources and types of data, identify scales of measurements, organize data and describe summary measures. 

CO 2: construct appropriate diagrams and graphs and acquire knowledge of the importance of normal distribution in tests of significance.

CO3: Identify a suitable test of significance to test a given hypothesis -large sample test/small sample test for testing. 

SYLLABUS

UNIT I

Descriptive Statistics – Types of Data, Scales of Measurements, Likert scale, Measures of Central Tendency, Partition Measures- Quartiles, Deciles, Percentiles, Measures of Dispersion, Moments, Skewness and Kurtosis.

Diagrammatic representation of data (Bar and Pie charts)

Graphical representation of data (Histogram, frequency polygon, Ogives)

Normal distributions- its importance in tests of significance.

UNIT II

Correlation and Regression Analysis (simple linear).

Inferential statistics – Concepts of confidence intervals, hypothesis testing, Large sample tests-Means and proportions, Small sample tests -t test, F test, χ² test.

List of References:

1. S.C.Gupta:  Fundamentals of Statistics Himalaya Publishing House.
2.  Jerrold H.Zar: BioStatistical Analysis.
3. Hugh Coolican: Research Methods and Statistics in Psychology.
4. R.S.N.Pillai, Bagavathi:Practical Statistics S.C.Chand& Company Ltd.
5. Maracello Pagano, Kimberlee Gauvreau:Principles of BioStatistics.
6. B.L .Agarwal :Basic Statistics New Age international publishers.

                                          VALUE ADDED COURSES (R21 onwards)

Skill Enhancement Course (SEC) – 2

Course Title: Data Scaling Techniques and Report Writing

Course Objectives

At the end of the course, a student will be able to

1.  Acquire knowledge in the use and application of the methods of data collection and analysis.
2.  Analyze various scaling techniques and evaluate questionnaires.

Course Outcomes

          CO1: Explain scales of measurement, questionnaire and schedule, classification bases and scale construction techniques.

           CO2: Design and carryout a project with statistical analysis and present the report.

SYLLABUS

UNIT –I

Review: Qualitative and quantitative data, Measurement scales: nominal, ordinal, interval and ratio scales.

Scale classification bases, important scaling techniques, Scale construction techniques, developing likert – type scales, Factor scales and cumulative scales their advantages and limitations.

UNIT – II

Review: Questionnaire and schedule.

Interpretation and report writing: Meaning and technique of interpretation, precautions in interpretation, significance of report writing, different steps in report writing, types of reports, layout of the research report, mechanics of writing a research report and oral presentation.

Practical: Group project of 3 students

Students should submit a research report based on empirical study using primary/ secondary data.

Reference Books

1.     Kothari, C.R.(2009): Research Methodology: Methods and Techniques, 2^nd Revised Edition reprint, New Age International Publishers.
2.     Kumar.R(2011): Research Methodology: A Step-by-Step Guide for Beginners, SAGE Publications
3.     SC Gupta and VK Kapoor: Fundamentals of Applied Statistics, Sultan Chand & Sons
4.     Goon AM, Gupta MK, Das Gupta B: Fundamentals of Statistics, Vol – I, The World Press Pvt Ltd, Kolkata.

Skill Enhancement Course (SEC) – 4

Course Title: R-Programming

Course Objectives

1. Acquire knowledge in essential programming skills in R.
2. Visualize a given data, perform descriptive analysis and carry out a project using R Programming. 

Course Outcomes

After successful completion of the course, a student will be able to perform the following using R Programming

CO1: Demonstrate basic programming skills in R

CO2: Describe and illustrate the data 

CO3:  Design and carryout a project with statistical analysis using R Programming and present the report.

SYLLABUS

UNIT – I

Introduction to R: – Overview, Environment Setup, Basic Syntax, Data types.

R Basics: – Variables, Operators, Decision Making, Loops, Functions, Strings, Vectors, Lists, Matrices, Arrays, Factors, Data Frames, Data Reshaping, Packages.

UNIT – II

Data Interfaces: – CSV files, Excel, etc

Data visualisation: – Pie Charts, Bar Charts, Area Charts, Bubble Charts, Surface charts, , Waterfall charts, Box Plots, Violin plots, Histograms, Line Graphs, Scatter plots, Matrix plots.

Statistical Analysis in R: – Descriptive statistics: measures of central tendency, measures of dispersion, partition values, moments, skewness and kurtosis, Regression.

Practical: Group project of 3 students each

Students should carry out a project using R programming and submit at the end of the course for evaluation.

List of References:

1. Mark Gardener: Beginning R: The Statistical Programming Language
2. Garrett Grolemund  : Hands-On Programming with R: Write Your Own Functions and Simulations  by  
3. Levin, Stephan, Krehbiel, Berenson:Statisitcs for Managers using Microsoft Excel. 4th edition. Pearson publication
4. Gerald Keller:Appled Statistics with MS-Excel. Duxbury, Thomson Learning
5. Norman Matloff : The Art of R Programming 
6. P.G.Dixit, V.R.Pawgi & P.S.Kapre: Statistical Methods and Use of R-Software, Nirali Prakasan Publishers
7. Vishwas R.Pawgi: Statistical Computing Using R-Software