Value Added Courses
Department of Statistics
Value Added Courses
Generic Elective (GE) – R21 onwards
Subject | Statistics – Theory |
Course Code | IDC501 |
Course Title | Essential Statistics |
Course Objectives | Familiarize the learners with basics of statistics in data analysis – visualization, descriptive analysis, predictive analysis and inference using MS-Excel & R Programming |
No. of Credits | 4 |
Course Outcomes At the end of this course a student will be able to | |
CO1 | Understand the scope and basic concepts of statistics, summary statistics, bivariate data and inferential statistics |
CO2 | Identify the scales of measurement, Analyze the data using summary statistics and regression analysis |
CO3 | Illustrate the data with appropriate graphs and diagrams and analyze the data using MS-Excel and R Programming |
CO4 | Set up hypotheses and evaluate the various parameters using statistical inference |
Syllabus
Graduate Attributes – Relevance, course contents/ titles are highlighted as indicated below | ||||
Employability | Entrepreneurship | Skill Development | Value enrichment | Women empowerment |
Note: Relevance – L- Local, R- Regional, N-National, G- Global
UNIT | Course Contents/Title | Course Outcome | Cognitive Levels | Relevance L/R/N/G |
I | Introduction: Definition and scope of Statistics, concepts of statistical population and sample. Data: Primary and secondary data, quantitative and qualitative, attributes, variables, scales of measurement – nominal, ordinal, interval and ratio, Likert scale. Presentation: tabular and graphic, including histogram, ogives, Box plot, Stem-Leaf plot.
Lab Work : Charts, Histogram, Ogives, Box plot, Stem-Leaf plot using MS-Excel and R. | CO1, CO2 & CO3 | Remember, Understand, and Analyze | G |
II | Descriptive Statistics: Measures of Central Tendency: mathematical and positional. Measures of Dispersion: range, quartile deviation, mean deviation, standard deviation, coefficient of variation, moments, skewness and kurtosis. Normal distributions- its importance in tests of significance.
Lab Work : Summary statistics using excel and R | CO2 & CO3 | Remember, Apply &Analyze | G |
III | Linear Regression Analysis: Definition, scatter diagram, simple, partial and multiple correlation (3 variables only), rank correlation. Simple linear regression, principle of least squares and fitting of polynomials and exponential curves.
Lab Work :Correlation,Simple linear regression using excel and R | CO2 & CO3 | Remember, Apply &Analyze | G |
IV | Inferential statistics: Basic concepts of statistical hypothesis testing, Large sample tests-Means and proportions, Small sample tests -t test, F test, χ2 test. Lab Work : Large sample tests-Means and proportions, Small sample tests -t test, F test, χ2 test using excel and R. | CO3 & CO4 | Create & Evaluate | G |
Lab work in all the units cater to the Graduate Attributes of Skill Development and Employability | ||||
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
MS –Excel | |
Topic | Details |
Excel Basics | Insert/Select/Delete/Move Data Rows/Columns, Copy ,Paste, Find and Replace Spell Check, Zoom-In-Out, Special Symbols Insert Comments, Add Text Box, Undo Changes Setting Cell, Setting Fonts, Text Decoration Rotate Cells, Setting Colours, Text Alignments, Merge and wrap, Split Cell, Borders and Shades, Apply Formatting, Worksheet – Options, Margins, page orientation, Header and Footer, Page breaks, Set Background, Freeze Panes, Conditional Format Hide and Protect Worksheets, merge worksheets. |
Tables | Inserting tables. Table Formatting Inserting/Creating formulae, Data Filtering, Sorting, Using Ranges, Data Validation, Using templates, Using Macros, Adding graphics. |
Look Ups | Vlookup and Hlookup |
Functions | Text Functions, Date & Time functions, Mathematical Functions, Statistical Functions. |
Worksheets | Worksheet – Cross referencing, Printing worksheets, Email Workbooks, Translate Worksheet (includes more exercises for functions to get hands on) |
Tables | Data Tables and Pivot Tables |
Charts | Charts |
VBA | Intro to VBA, Excel Macros, Excel Terms, Macro Comments, Message Box, Input Box, Variables, Constants, Operators, Decisions, Loops, Strings, Date & Time Arrays, Functions, Sub procedure, Events, Error Handling, Excel objects, Text files. |
Project | |
R-Programming | |
Topic | Details |
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, Packages, Data Reshaping |
R Basics | Loops, Functions, Strings |
R Basics | Vectors, Lists, Matrices |
R Basics | Arrays, Factors |
R Basics | Data Frames, Packages, Data Reshaping |
Data Interfaces | CSV files, Excel, etc |
Charts | Pie Charts, Bar Charts, Box Plots |
Charts | Histograms, Line Graphs, Scatter plots |
R Stats | Mean, Median, Mode, Regression |
List of References:
- Mark Gardener: Beginning R: The Statistical Programming Language
- Garrett Grolemund : Hands-On Programming with R: Write Your Own Functions and Simulations by Levin, Stephan, Krehbiel, Berenson
- Statisitcs for Managers using Microsoft Excel. 4th edition. Pearson publication
- Gerald Keller:Appled Statistics with MS-Excel. Duxbury, Thomson Learning
- Norman Matloff : The Art of R Programming
- P.G.Dixit, V.R.Pawgi & P.S.Kapre: Statistical Methods and Use of R-Software, Nirali Prakasan Publishers
- 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:
- S.C.Gupta: Fundamentals of Statistics Himalaya Publishing House.
- Jerrold H.Zar: BioStatistical Analysis.
- Hugh Coolican: Research Methods and Statistics in Psychology.
- R.S.N.Pillai, Bagavathi:Practical Statistics S.C.Chand& Company Ltd.
- Maracello Pagano, Kimberlee Gauvreau:Principles of BioStatistics.
- 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
- Acquire knowledge in the use and application of the methods of data collection and analysis.
- 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
- Kothari, C.R.(2009): Research Methodology: Methods and Techniques, 2nd Revised Edition reprint, New Age International Publishers.
- Kumar.R(2011): Research Methodology: A Step-by-Step Guide for Beginners, SAGE Publications
- SC Gupta and VK Kapoor: Fundamentals of Applied Statistics, Sultan Chand & Sons
- 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
- Acquire knowledge in essential programming skills in R.
- 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:
- Mark Gardener: Beginning R: The Statistical Programming Language
- Garrett Grolemund : Hands-On Programming with R: Write Your Own Functions and Simulations by
- Levin, Stephan, Krehbiel, Berenson:Statisitcs for Managers using Microsoft Excel. 4th edition. Pearson publication
- Gerald Keller:Appled Statistics with MS-Excel. Duxbury, Thomson Learning
- Norman Matloff : The Art of R Programming
- P.G.Dixit, V.R.Pawgi & P.S.Kapre: Statistical Methods and Use of R-Software, Nirali Prakasan Publishers
- Vishwas R.Pawgi: Statistical Computing Using R-Software