डॉ. बी. आर. अम्बेडकर विश्‍वविद्यालय दिल्ली
Dr. B. R. Ambedkar University Delhi
Established by The Government of NCT of Delhi

Course Coordinator and Team: Saranika Sarkar  & Krishna Ram

1.       Does the course connect to, build on or overlap with any other courses offered in AUD? No.

2.       Specific requirements on the part of students who can be admitted to this course:

(Pre-requisites; prior knowledge level; any others – please specify)  No.

3.       No. of students to be admitted (with justification if lower than usual cohort size is proposed): As per SLS norms.

4.       Course scheduling (semester; semester-long/half-semester course; workshop mode; seminar mode; any other – please specify):

It is a semester long course to be offered in the first semester.

5.       How does the course link with the vision of AUD?

AUD envisions a postgraduate with a certain minimum understanding of how to work and infer from using some basic statistical techniques. This course contributes the basic core knowledge and understanding required towards development of students in the two year postgraduate programme in economics. 

6.    How does the course link with the specific programme(s) where it is being offered?

The course tries to give students a comprehensive and rigorous understanding of how to work and infer from the data using various statistical techniques. It is expected that students who take this course will gain an understanding of the methodology used for data analysis. 

Course Details:

a.     Summary: This course is designed to make students familiar with data, especially the survey  and big data sets. The  course equips students for analysing data related to economics in particular and social science in general, with the help of statistical knowledge and computer software R. The main thrust of the course will be providing valuable skills in data analysis particularly relevant to economics and social sciences, and helping them to become proficient in handling big data in these fields.  

b.     Objectives:

1.    To make students familiar with the data, especially the survey and big data sets.

2.   To equip students with the tools that help them to analyse and infer from the data with the help of statistical packages such as Stata/R.

3.   To provide valuable skills in data analysis particularly relevant to economics and social sciences and help them to become proficient in handling big data in these fields. 

4.   To estimate causal relationship between variables.

5.  To apply matrix algebra and determinants to solve system of linear equations

Expected learning outcomes:

Upon successful completion of this course,  students will be able to:

Perform data management tasks such as data cleaning, merging, and sub-setting, including big data.

Visualise data and make multiway tables

Derive statistical inferences from data

Matrix manipulation, calculate eigenvalue and eigenvector

Able to solve system of linear equation using concept of matrix algebra and determinants.

Overall structure (course organisation, rationale of organisation; outline of each module):

The course consists six main modules:

1.     Data Management & Exploration

This module includes Data types, Import-Export, Cleaning, Filtering, Joining and Visualisation of data

2.     Data Summarisation and Tables

This module mainly deals with the calculation of descriptive statistics such as mean, median, mode, range, variance, standard deviation and correlation coefficients. It includes how to make one-way, two-way, and multiway tables.

3.     Dealing with Complex Survey and Big Data Sets

This section delves into dealing with survey and big data sets. Using the Arrow, Duckdb packages of R programming language, this section covers working with big data remotely without necessity of loading the data to the computer memory. Additionally, this section also covers the integration of virtual machine technology, showcasing its role in augmented data analysis capabilities.  

4.    Review of Basic Probability, Probability Distribution, Expectation, Estimation and Hypothesis Testing

This module emphasises reviewing the basic probability theory and probability distribution, including

binomial probability distribution, uniform probability distribution, and normal probability distribution.

Additionally, the section covers expectation,  estimation and hypothesis testing.

5.     Convergence of  Random Variables:  Law of Large Number (LLN) and  Central Limit Theorem (CLT)

      This section mainly covers the type of convergence, low of large numbers and the central limit theorem.

6.     Review of Linear Algebra

This section delves into the review of linear algebra that mainly includes matrix algebra,  determinants,

eigenvalue and eigenvector, and application of linear independence.

Contents (week-wise plan with readings):

The main textbooks for the course will be: 

[WG] Wickham, H.& Grolemund, G. (2021). R for data science. " O'Reilly Media, Inc."

Wasserman, L. (2004). All of statistics: a concise course in statistical inference (Vol. 26, p. 86). New York: Springer, ch-5

Hansen, B. (2022). Probability and Statistics for Economists. Princeton University Press. Ch 1-2, 7 and 9.

 [MM] Miller, I & Miller, M. (2004). John E. Freund's Mathematical Statistics: With Applications. Pearson Education India.

Mitchell, M.N (2020): Data Management Using Stata: A Practical Handbook, Stata Press

Simon, C. P., & Blume, L. (1994). Mathematics for economists(Vol. 7). New York: Norton

Week*	Plan/ Theme/ Topic	Core Reading (with chapter no.)	Additional Suggested Readings	Assessment (weights, modes, scheduling)
1-4	Data Management & Exploration	WG: Ch- 1-9, 12-19, 20	Mitchell, Ch 2-7,9
5	Data Summarisation and Tables	WG:  Ch 10	Mitchell, Ch 8
6-7	Dealing with Complex Survey and Big Data Sets	WG: Ch 21-23		Mid- Term (50 %)
7-9	Review of basic Probability, Probability Distributions, Densities, and Expectation, Conditional expectation,	MM: Ch-2-6	Hansen, 1-5 Wasserman, ch-3
10-11 weeks	Convergence of random variables, Central Limit Theorem (CLT), Law of Large Number (LLN)	Hansen, ch-7 & 8	Wasserman, ch- 5
12	Estimation	MM: Ch 10-11
13	Hypothesis Testing	MM: Ch-12,13	Hansen, Ch- 13
13-15	Review of Linear Algebra	Simon and Blume – Ch-8, 23, 26-28		End- Term (50 %)

7.     Pedagogy:

a.     Instructional strategies: lectures. 

b.     Special needs (facilities, requirements in terms of software, studio, lab, clinic, library, classroom/others instructional space; any other – please specify): Classroom equipped with a projector. 

c.     Expertise in AUD faculty or outside: Economics faculty at AUD are competent to handle the course.

d.     Linkages with external agencies (e.g., with field-based organizations, hospital; any others) NA.

9. Assessment Structure

•  Mid Term -  50 % weightage
• End Term - 50 % weightage