Working with Numbers

Semester and Year Offered: Third Semester

Course Coordinator and Team: Partha Saha

Email of course coordinator: partha[at]aud[dot]ac[dot]in

Pre-requisites: None

Course Objectives/Description:

This is an introductory course which will equip students with elementary knowledge of data analysis. This course will help students to develop basic understanding of various tools and techniques of quantitative data analysis. They will also be familiarized with working with data in worksheets and classroom teaching will be supplemented by group activities and empirical exercises. In a nutshell, at the end of this course, students are expected to have confidence in carrying out preliminary quantitative studies and meaningful evidence based discussions on issues of Sustainable Urbanism.

Course Outcomes:

On successful completion of this course students will be able to:

1. Work with data using practical skills of data exploration and visualization.
2. Develop acumen of evidence based learning and empirically verify happenings related to Sustainable Urbanism.
3. Use empirics in order to enhance understanding of various topics discussed in this programme.
4. Analyse quantitative data using software

Brief description of modules/ Main modules:

1. Introduction to Basic Concepts in Statistics: this module will introduce important concepts and definitions.
2. Univariate Frequency Distribution: this module will try and understanding basic statistical and mathematical methods used in empirical analysis in case of univariate data.
3. Tabulation and graphical representation of data: Importance of this module lies in the fact that intelligent graphical presentation of data can often provide guidance towards adopting appropriate research questions and also provide crucial background knowledge about characteristics of various data points vis-à-vis each other.
4. Summation & Other Statistical Procedures: this module will introduce basic calculations using mathematical and statistical formulae which are used in preliminary data analysis.
5. Introduction to Sample & Census: this module will provide an introduction to sampling techniques, and advantages and disadvantages of census enumeration.
6. Measures of Central Tendency: this module will focus on measures of central tendency like mean, median, and mode.
7. Measures of Dispersion: this module will focus on measures of dispersion like standard deviation, coefficient of variation, range and other measures.
8. Dealing with Bivariate Data & Correlation: this module will introduce students to statistical analysis of bivariate data and introduce the concept of correlation.
9. Index Numbers: this module will equip students with techniques of constructing index.
10. Discussion on Sustainability Indices: this module will focus on applications of statistical concepts and techniques on real life data which will be obtained from different secondary sources.

Assessment Details with weights:

1. Assignment 1: Based on whatever is discussed in modules 1 – 5 (30% weight)
2. Written Examination: Based on modules 6 – 9 (40% weight)
3. Assignment 2: Based on modules 10 – 12 (30% weight)

Reading List:

• [MMC] Moore, D.S., McCabe, G.P. and Craig, B.A. (2009), Introduction to the Practice of Statistics, 6th edition, W.H. Freeman and Co
• Statistics in Social Sciences: Current Methodological Developments – S Kolenikov, D Steinley, L Thombs (eds)
• Elementary Statistics – Neil A Weiss
• Reports of UNDP and other international organizations while working on modules 9 to 12.