| 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
|
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| Course Language | ||||||
| Course Type |
Elective
|
|||||
| Course Level |
-
|
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| 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. |
| Course Description |
The students who succeeded in this course;
|
| Course Content | 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. |
|
|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
| 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 |
|
| Suggested Readings/Materials |
|
| Semester Activities | Number | Weigthing |
| Participation |
15
|
|
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Homework / Assignments |
5
|
30
|
| Presentation / Jury | ||
| Project |
1
|
15
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
2
|
40
|
| 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 |
| 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
|
||
| Study Hours Out of Class |
16
|
2
|
|
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Homework / Assignments |
5
|
20
|
|
| Presentation / Jury | |||
| Project |
1
|
26
|
|
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms |
2
|
34
|
|
| Final Exam | |||
| Total |
274
|
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
1 |
2 |
3 |
4 |
5 |
||
| 1 | To improve and deepen expertise in economics and finance. |
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| 2 | To be able to comprehend the interaction between economics, finance and related fields. |
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| 3 | To be able to apply the advanced level knowledge acquired in economics and finance. |
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| 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. |
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| 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. |
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| 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. |
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| 7 | To be able to develop new strategic approaches for unexpected, complicated situations in finance and economics and take responsibility in solving it. |
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| 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. |
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| 9 | To be able to critically evaluate the knowledge in finance and economics, to lead learning and carry out advanced level research independently. |
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| 10 | To be able to use a foreign language for both following scientific progress and for written and oral communication. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
