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VALUE ADDED COURSES

SEMESTER III

VAC1 – VALUE ADDED COURSE
KU3VACSTA261: DATA VISUALIZATION AND INTERPRETATION
Semester Course Type Course Level Course Code Credits Total Hours
III VALUE ADDED 200 – 299 KU3VACSTA261 3 60

Learning Approach (Hours/Week) / Marks Distribution
Lecture Practical/Internship Tutorial CE ESE Total Duration of ESE (Hours)
2 2 - 25 50 75 1½

Course Description

This course introduces students to fundamental concepts in data analysis including data types, scaling techniques, census and sampling methodologies, measures of central tendency and dispersion, and bivariate data analysis, emphasizing practical applications through various statistical tools and techniques.

Course Prerequisite

Foundation Courses (Level 100 – 199)

Course Outcomes
CO No. Expected Outcome Learning Domains
1 Understand and distinguish between different data types (quantitative, qualitative, time-series, and cross-sectional) and their appropriate scaling techniques. U
2 Apply census and sampling methodologies effectively, including the collection of primary and secondary data, and utilize various graphical representations for data presentation. A
3 Calculate and interpret measures of central tendency (mean, median, and mode) and dispersion (range, mean deviation, standard deviation) along with the coefficient of variation. R
4 Analyse bivariate data through correlation techniques, including the understanding of different types of correlation and the application of scatter diagrams and Karl Pearson’s correlation coefficient. An
5 Perform simple linear regression analysis, interpret regression coefficients, and understand the properties of regression models for predictive modelling. E

COURSE CONTENTS
Contents for Classroom Transaction
Module Unit / Description Hours Contents
1 Data types and Scaling techniques 7
  1. Concepts of population and sample
  2. Quantitative, qualitative, time-series and cross-sectional data
  3. Discrete and continuous data
  4. Different types of scales: Nominal, ordinal, interval and ratio
2 Census and Sampling 8
  1. Census and Sampling – meaning and comparison
  2. Primary data. Secondary data – its major sources
  3. Diagrammatic presentation- line diagram, bar diagrams and pie diagrams, pictograms, cartograms and box-plot
  4. Frequency tables, frequency polygon, frequency curve, ogives and histogram
3 Measures of Central Tendency and Dispersion 8
  1. Central tendency- Mean, median and mode (concept and application only)
  2. Range, mean deviation, standard deviation (concept and application only)
  3. Coefficient of variation
4 Bivariate Data Analysis 7
  1. Correlation (concept and application only), types of correlation
  2. Scatter diagram, Karl Pearson’s correlation coefficient (simple examples)
  3. Simple linear regression
  4. Regression coefficients and its properties
5 Practical using MS Excel 30 Practical based on Module 2 to 4 using MS Excel. Data analysis: presentation of data – Charts and Diagrams, Frequency table, Histogram, calculation of descriptive statistics and bivariate data analysis

Essential Readings
  1. Gupta, S. C. and Kapoor, V. K. (2014). Fundamentals of Mathematical Statistics, Sultan Chand & Sons.
  2. Agarwal, B. L. (2006). Basic Statistics. 4th Edition, New Age international (P) Ltd., New Delhi.
  3. Salkind, N. J. (2010). Excel Statistics: A Quick Guide, SAGE Publication Inc. New Delhi.
  4. Gupta, V. (2002). Statistical Analysis with Excel, VJ Books Inc. Canada.
Suggested Readings
  1. Gupta, S. P. (2004). Statistical Methods, Sultan Chand & Sons, New Delhi.
  2. Remenyi, D., Onofrei, G. and English, J. (2010). An Introduction to Statistics Using Microsoft Excel, Academic Publishing Ltd., UK.

Assessment Rubrics
Evaluation Type Marks Evaluation Type Marks Total
Lecture 50 Practical 25 75
End Semester Evaluation 35 End Semester Evaluation 15
Continuous Evaluation 15 Continuous Evaluation 10
a) Test Paper- 1 5 a) Punctuality 2
b) Test Paper-2 5 b) Skill 3
c) Assignment/ Viva-Voce 5 c) Assignment/ Field Report 5
Total 50 Total 25 75