Syllabus - Financial Economics (Without Thesis) | İzmir University of Economics

GRADUATE SCHOOL

Financial Economics (Without Thesis)

ECON 577 | Course Introduction and Application Information

Course Name
Data Science with Applications in Economics
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
ECON 577
Fall/Spring
3
0
3
7.5

Prerequisites
None
Course Language
Course Type
Elective
Course Level
-
Course Coordinator -
Course Lecturer(s)
Assistant(s) -
Course Objectives The course is designed to introduce the students the principles, tools, and general mindset of data science and its applications to social sciences especially to economics and finance. The course will teach students concepts, tools and techniques required for data wrangling, exploratory data analysis, predictive modeling, machine learning, data visualization, and effective communication through programming language R. The course aims to give students a working knowledge of data science through hands-on experience with empirical analysis of real-world economic, social and and financial data. Throughout the course, professional skills, such as communication, presentation, and storytelling with data will be fostered. The course is designed to introduce the students the principles, tools, and general mindset of data science and its applications to social sciences especially to economics and finance. The course will teach students concepts, tools and techniques required for data wrangling, exploratory data analysis, predictive modeling, machine learning, data visualization, and effective communication through programming language R. The course aims to give students a working knowledge of data science through hands-on experience with empirical analysis of real-world economic, social and and financial data. Throughout the course, professional skills, such as communication, presentation, and storytelling with data will be fostered.
Learning Outcomes The students who succeeded in this course;
  • List the steps involved in data science in economics and finance, from data acquisition to insight, and describe the role of each step
  • Manage, summarize, and visualize data using the R programming language
  • Carry out statistical model prediction and analysis in economics and finance
  • Apply machine learning methods and assess the quality of predictions in economics and finance
  • Develop professional skills such as effective communication, presentation, and storytelling with data
Course Description The course starts with R basic programming skills and continues with topics including data wrangling, exploratory data analysis, model prediction, machine learning, visualization, and effective communication. The students learn the material and hone their skills through hands-on experience working with real world datasets in economics and finance.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Welcome to Data Science: How can data science answer economic problems? What is Data Science? Mike Loukides, O'Reilly RADAR. 2010.Varian, Hal R. "Big data: New tricks for econometrics." Journal of Economic Perspectives 28.2 (2014): 3-28. Wickham & Grolemund Chapter 1
2 Meet the toolkit: R, Rstudio, and Rmarkdown: Rmarkdown report on data science Wickham & Grolemund Chapter 1-2, 4, 27 Irizarry Chapters 1-3
3 R and programming basics: Data types and vectors; matrices; factors; data frames; lists; indexing; subsetting Application: Star wars Saga box office Irizarry Chapter 4
4 Tidying and wrangling data Application: US Murder rates across states Wickham & Grolemund Chapter 5 Irizarry Chapter 6
5 Data visualization Application: Life expectancy and fertility across countries over the years Irizarry Chapter 8
6 Data visualisation in action Application: Income distribution over the last 50 years Irizarry Chapter 16
7 Midterm 1
8 Descriptive statistics, the language of models, formalizing linear models James, Witten, Hastie and Tibshirani, Chapter 3
9 Multiple linear regression Application: Crime rate per capita James, Witten, Hastie and Tibshirani, Chapter 3
10 Multiple logistic regression Application: Caravan Insurance data James, Witten, Hastie and Tibshirani, Chapter 4
11 Resampling Methods: Bootstrapping and cross Validation Application: Estimating the probability of default James, Witten, Hastie and Tibshirani, Chapter 5
12 Midterm 2
13 Web scraping Application: Sahibinden.com Irizarry Chapter 24
14 Text Mining Application: Trump’s tweets Irizarry Chapter 26
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
  • Introduction to Data Science thought by Çetinkaya-Rundel at Duke

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
15
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
5
30
Presentation / Jury
Project
-
Seminar / Workshop
Oral Exams
Midterm
2
55
Final Exam
Total

Weighting of Semester Activities on the Final Grade
6
70
Weighting of End-of-Semester Activities on the Final Grade
1
30
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
0
Study Hours Out of Class
16
2
32
Field Work
0
Quizzes / Studio Critiques
0
Homework / Assignments
5
20
100
Presentation / Jury
0
Project
-
0
Seminar / Workshop
0
Oral Exam
0
Midterms
2
47
94
Final Exam
0
    Total
274

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

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

To improve and deepen expertise in economics and finance.

2

To be able to comprehend the interaction between economics, finance and related fields.

3

To be able to apply the advanced level knowledge acquired in economics and finance.

4

To be able to create new knowledge by combining the knowledge of finance and economics with the knowledge coming from other disciplines and be able to solve problems which requires expert knowledge by applying scientific methods.

5

To be able to use computer programs needed in the fields of economics and finance as well as information and communication technologies in advanced levels.

6

To be able to think analytically to identify problems in finance and economics and to be able to make policy recommendations in economics and finance based on scientific analysis of issues and problems.

7

To be able to develop new strategic approaches for unexpected, complicated situations in finance and economics and take responsibility in solving it.

8

To protect the social, scientific and ethical values at the data collection, interpretation and dissemination stages and to be able to institute and observe these values.

9

To be able to critically evaluate the knowledge in finance and economics, to lead learning and carry out advanced level research independently.

10

To be able to use a foreign language for both following scientific progress and for written and oral communication.

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

 


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