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MULTI-DISCIPLINARY COURSES

SEMESTER I

MDC1 – MULTI-DISCIPLINARY COURSE
KU1MDCSTA141: BASICS OF STATISTICS
Semester Course Type Course Level Course Code Credits Total Hours
I MULTI-DISCIPLINARY 100 – 199 KU1MDCSTA141 3 45

Learning Approach (Hours/Week) / Marks Distribution
Lecture Practical/Internship Tutorial CE ESE Total Duration of ESE (Hours)
3 - - 25 50 75 1½

Course Description

This course covers fundamental mathematical concepts such as number systems, equations, and progressions, along with an introduction to statistics including data types, measurement scales, and methods of data collection and presentation.

Course Prerequisite

HSE level Mathematics/Statistics Courses

Course Outcomes
CO No. Expected Outcome Learning Domains
1 Understand the concepts and properties of numbers including integers, rational and irrational numbers. U
2 Apply ratio and proportion concepts in solving real-world problems. A
3 Demonstrate proficiency in using laws of indices and logarithms in mathematical calculations. R
4 Solve linear and quadratic equations and apply arithmetic and geometric progressions to practical situations. E
5 Analyse and interpret statistical data, including differentiating between quantitative and qualitative data types and understanding various measurement scales. An

COURSE CONTENTS
Contents for Classroom Transaction
Module Unit / Description Hours Contents
1 Elementary Mathematics – I 8
  1. Number system – Integers, rational and irrational numbers
  2. Ratio and proportion
  3. Laws of indices
  4. Logarithm
2 Elementary Mathematics – II 7
  1. Equations – Solution of linear and quadratic equations
  2. Arithmetic and geometric progression
  3. Simple and compound growth rate
  4. Profit and loss, Market equilibrium
3 Introduction to Statistics 8
  1. Statistics: Definition, nature and scope of statistics in various streams
  2. Different types of data: quantitative, qualitative, geographical and chronological
  3. Scales of measurement of data: nominal, ordinal, interval and ratio scale
  4. Time series, cross sectional and longitudinal data
4 Statistical Methods 7
  1. Collection of data: Primary and Secondary and their sources
  2. Presentation of data: classification and tabulation of data
  3. Diagrammatic Representation: Line diagram, bar diagrams and pie diagrams
  4. Graphical presentation: Histogram, frequency polygon, frequency curve and ogives
5 Open End 15 Practical using MS Excel

History of Statistics, Data entry using MS Excel, Understanding the usage of various statistical and mathematical functions in Excel, Preparation of diagrams explained in Module 4 by Excel, Preparation and submission of a report.

Essential Readings
  1. Gupta S. C. and Kapoor, V. K. (2002): Fundamentals of Mathematical Statistics, Sultan Chand & Co.
  2. Gupta S. C. (2018): Fundamentals of Statistics, Himalaya Publishing House.
  3. B L Agrawal (2013): Basic Statistics, New Age International Publishers.
  4. Yule and Kendall (1984): An Introduction to the Theory of Statistics, Charles Gtiffin & Co, London.
  5. Spiegel, M.R (2000): Theory and Problem of Statistics, McGraw Hill, London.
Suggested Readings
  1. Mood A. M., Gray bill F. A., Bose D. C. (2007): Introduction to the theory of statistics - Tata Magraw Hill.
  2. Goon A. M., Gupta M. K., Das Gupta. B. (1999): Fundamentals of Statistics, Vol. I, World Press, Calcutta.
  3. Croxton. F. E and Cowden. D. J (1973): Applied General Statistics, Printice Hall of India.

Assessment Rubrics

SEMESTER II

MDC2 – MULTI-DISCIPLINARY COURSE
KU2MDCSTA151: INTRODUCTION TO DATA ANALYSIS
Semester Course Type Course Level Course Code Credits Total Hours
II MULTI-DISCIPLINARY 100 – 199 KU2MDCSTA151 3 45

Learning Approach (Hours/Week) / Marks Distribution
Lecture Practical/Internship Tutorial CE ESE Total Duration of ESE (Hours)
3 - - 25 50 75 1½

Evaluation Type
Evaluation Type Marks
End Semester Evaluation 50
Continuous Evaluation 25
a) Test Paper- 1 5
b) Test Paper-2 5
c) Assignment 5
d) Seminar -
e) Book/ Article Review -
f) Viva-Voce -
g) Field Report/Practical 10