COURSE INTRODUCTION AND APPLICATION INFORMATION


Course Name
Introduction to Data Science with Applications in Economics
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
ECON 465
Fall/Spring
3
0
3
6
Prerequisites
None
Course Language
Course Type
Elective
Course Level
-
Mode of Delivery -
Teaching Methods and Techniques of the Course
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives This course is designed as a follow up to the econometrics course and will be one of the “Applied Economic Analysis” area courses. The aim is to introduce economics students to the rapidly growing field of data science. Students will also learn concepts, tools and techniques required for data wrangling, exploratory data analysis and data visualization with large datasets, and effective communication on a platform that is more amenable to very large datasets than econometrics software. The course also aims to give students a working knowledge of data science through hands-on economic and financial problems and case studies based on real-world data.
Learning Outcomes The students who succeeded in this course;
  • describe the role of each step from data acquisition to insight involved in data science in economics
  • apply tools such as plots, graphs and summary statistics to carry out exploratory data analysis.
  • carry out statistical model prediction and analysis in economics with linear regression.
  • implement machine learning algorithms
  • develop professional skills such as communication, presentation, and storytelling with data.
Course Description
Related Sustainable Development Goals

 



Course Category

Core Courses
Major Area Courses
X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Welcome to Data Science: How data science can answer economic problems? Varian, Hal R. "Big data: New tricks for econometrics." Journal of Economic Perspectives 28.2 (2014): 3-28. Athey, Susan. "The impact of machine learning on economics." The Economics of Artificial Intelligence: An Agenda. University of Chicago Press, 2018. Wickham & Grolemund Chapter 1
2 Meet the toolkit: R, Rstudio, and Rmarkdown: Rmarkdown report on data science, R and programming basics Wickham & Grolemund Chapter 1-2, 4, 27 Irizarry Chapters 1-4
3 Tidying and wrangling data Life expectancy and fertility across countries over the years Wickham & Grolemund Chapter 5, 12 Irizarry Chapters 5
4 Data visualization Application: US Murder rates across states Wickham & Grolemund Chapter 3 Irizarry Chapters 7-9
5 Data visualisation in action Income distribution over the last 50 years Irizarry Chapters 10-11
6 Scientific studies, conditional probability Confounds and Simpson's paradox Admission to the university and gender bias Irizarry Chapter 16
7 Midterm 1
8 The language of models, formalizing linear models, multiple linear regression Crime rate per capita James, Witten, Hastie and Tibshirani, Chapter 3
9 Multiple logistic regression James, Witten, Hastie and Tibshirani, Chapter 4
10 Resampling Methods: Bootstrapping and cross Validation Estimating the probability of default James, Witten, Hastie and Tibshirani, Chapter 5
11 Linear Model Selection: Subset selection, shrinkage and dimension reduction methods James, Witten, Hastie and Tibshirani, Chapter 6
12 Midterm 2
13 Web scraping Economic and geographical features of cities via Citysearch.com Irizarry Chapter 24
14 Functions and automation
15 Review of the semester
16 Review of the Semester  
Course Notes/Textbooks
  • R for Data Science (RDS) by Hadley Wickham and Garret Grolemund (free online) (https://r4ds.had.co.nz/
  • Introduction to Data Science Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry (free online) https://rafalab.github.io/dsbook/
Suggested Readings/Materials
  • Varian, Hal R. "Big data: New tricks for econometrics." Journal of Economic Perspectives 28.2 (2014): 3-28.
  • Athey, Susan. "The impact of machine learning on economics." The Economics of Artificial Intelligence: An Agenda. University of Chicago Press, 2018
  • Khandani, Amir E., Adlar J. Kim, and Andrew W. Lo. "Consumer credit-risk models via machine-learning algorithms." Journal of Banking & Finance 34.11 (2010): 2767-2787.
  • Bastos, Joao. "Credit scoring with boosted decision trees." (2007).
  • Bajari, Patrick, et al. "Machine learning methods for demand estimation." American Economic Review 105.5 (2015): 481-85.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
14
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
6
40
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterm
2
50
Final Exam
Total

Weighting of Semester Activities on the Final Grade
22
100
Weighting of End-of-Semester Activities on the Final Grade
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
Study Hours Out of Class
16
1
16
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
6
10
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms
2
25
Final Exams
    Total
174

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To be able to acquire a sound knowledge of fundamental concepts, theories, principles and methods of investigation specific to the economic field.

X
2

To be able to apply adequate mathematical, econometric, statistical and data analysis models to process economic data and to implement scientific research for development of economic policies.

X
3

To be able to participate in academic, professional, regional, and global networks and to utilize these networks efficiently.

4

To be able to have adequate social responsibility with regards to the needs of the society and to organize the activities to influence social dynamics in line with social goals.

5

To be able to integrate the knowledge and training acquired during the university education with personal education and produce a synthesis of knowledge one requires.

X
6

To be able to evaluate his/her advance level educational needs and do necessary planning to fulfill those needs through the acquired capability to think analytically and critically.

X
7

To be able to acquire necessary skills to integrate social dynamics into economic process both as an input and an output.

X
8

To be able to link accumulated knowledge acquired during the university education with historical and cultural qualities of the society and be able to convey it to different strata of society.

X
9

To be able to take the responsibility as an individual and as a team member.

10

To be able to attain social, scientific and ethical values at the data collection, interpretation and dissemination stages of economic analysis.

X
11

To be able to collect data in economics and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1)

X
12

To be able to speak a second foreign at a medium level of fluency efficiently.

13

To be able to relate the knowledge accumulated throughout human history to their field of economics.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest