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.
Course Description 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 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.

 



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 Introduction to Data Science Baumer et al (2017) Modern Data Science with R Ch 1
2 Introduction to Data Science tools: R, Rstudio, and Rmarkdown: Rmarkdown report on data science Peng R. (2018) R Programming for Data Science Chs 2-3
3 Data Visualization Application: Baumer et al (2017) Modern Data Science with R Ch 2
4 Data graphics Application: Banking systems around the world Baumer et al (2017) Modern Data Science with R Ch 3
5 Data Wrangling Application: Government policies and popularity: Hong Kong cash handout Baumer et al (2017) Modern Data Science with R Ch 4
6 Tidy data and iteration Application: U.S. Stock Market: SP500 and VIX Baumer et al (2017) Modern Data Science with R Ch 5
7 Programming in R Functions Application: Non-monetary cost of unemployment Peng R. (2018) R Programming for Data Science Chs 14-15, 18
8 Midterm 1
9 Machine Learning- Linear regression Application: Global value chains Baumer et al (2017) Modern Data Science with R Ch 7, James et. Al. An Introduction to Statistical Learning with Applications in R Ch 2-3
10 Machine Learning -Classification Applications: fundraising and transperancy in loan approval James et. al. An Introduction to Statistical Learning with Applications in R Chs 4
11 Machine Learning – dimension reduction Application: Crime differences between cities James et. al. An Introduction to Statistical Learning with Applications in R Ch 9
12 Machine Learning – clustering Application: longitudinal occupational wage James et. al. An Introduction to Statistical Learning with Applications in R Ch 6, Baumer et al (2017) Modern Data Science with R Ch 9
13 Midterm 2
14 Project Presentations
15 Review of the Semester  
16 Review of the Semester  

 

Course Notes/Textbooks
  • Modern Data Science (MDS) with R by Baumer et al.,. CRC Press
  • An Introduction to Statistical Learning with Applications

in R (ISLAR) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, 2013; Publisher: Springer.(Available freely online)

  • R for Data Science (RDS) by Hadley Wickham and Garret Grolemund (free online)
Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
5
25
Presentation / Jury
Project
1
25
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

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
Study Hours Out of Class
16
2
Field Work
Quizzes / Studio Critiques
Homework / Assignments
5
13
Presentation / Jury
Project
1
61
Seminar / Workshop
Oral Exam
Midterms
2
34
Final Exam
    Total
274

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

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

To improve and deepen actual and advanced level knowledge in economics in the level of expertise by inventive thoughts and/or research and to get inventive contributions to science.

2

To comprehend the interaction between economics and related fields; to achieve inventive results by using knowledge requiring expertise in analysis, synthesis and evaluation of new and complex ideas.

3

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

4

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

5

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

6

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

7

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

8

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

9

To possess the communication network to put the economic and social needs of the region of residence on the agenda.

10

To have adequate social responsibility and conciousness about the needs of society and to have the experience and authority  to organize and support the operations that can affect and drive  the social dynamics when necessary.

11

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

12

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

13

To be able to use the skills of modeling, empirical analysis and formulating policy options that are developed for financial economics, in interdisciplinary contexts.

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