Data Science Lab For Economists

Course goals

Learning objectives

At the end of the course, the student is able to:

• Define a data science project including a research question
• Develop a plan to conduct the data science project
• Find and/or gather the relevant data for the project and prepare it
• Conduct data analyses and/or econometric analyses on the research question
• Interpret the findings and compare them to existing evidence from the literature
• Write a seminar paper on the findings
• Present and discuss the findings

Content

Data science is becoming more and more important in both business and science. Conducting data science involves many steps. In the previous courses of the minor, students have learned to use the statistical programming language R and several data analysis and econometric methods. In this course, students apply these skills to conduct a data science project in small teams on their own, focusing on questions from business and economics. They go through all stages of a data science project:

1. Definition of the project and research question
2. Gathering and preparing the data
3. Conducting the analyses
4. Interpreting the findings and writing a seminar paper on the findings
5. Presenting and discussing the results

A list of topics from business and economics will be provided at the beginning of the course from which the students can choose their topic. They conduct the project in small teams, discuss each other’s projects and exchange on their experience at various stages, and present their final project. At the end, they hand in a team seminar paper on their project. Students are expected to have acquired the knowledge from the preceding courses of the data science minor, as well as from statistics and econometrics.
 
As an alternative to this course, students can do a data-science related internship. This entails doing an internship in a suitable institution where the student works on a data-science related project that is comparable to the projects in this course. This further entails writing a report on this work that is roughly comparable to the papers which students write in this course. The details strongly depend on the individual case. Please contact the course coordinator if you are interested in this.

The course is open only for students of the USE Bachelor Economics and Business Economics with the dedicated minor Applied Data Science for Economists. Other students cannot enrol because knowledge of all preceding courses of the minor is required.

Format
Since this course is about conducting and presenting data science projects in teams, there is no lecture except for an introduction. Instead, the course is split in groups of suitable sizes to ensure a good environment for exchange. Within these groups, students form small project teams of 2-3 students per team. The course consists of

• An introductory lecture: one session for all groups.
• Supervision sessions: one per group every week, where students exchange on their progress to learn from each other and where students can ask for advice about their projects.
• An intermediate presentation session: one for each group, where students present and discuss their results with each other.
• A final presentation session: one for each group, attendance is mandatory.

Students jointly present their team project at the end of the course. Students hand in:

• A Team paper, which consists of
  • an individual part per team member. Each student is responsible for a separate step of the data analysis, as well as the respective section in the paper. This covers data preparation, description, visualization, and empirical analysis. The three parts should be of equal importance and must be approved by the course coordinator.
  • a joint part, for which all students receive the same grade. This covers the remaining sections of the paper, such as e.g. introduction, research question, related literature, discussion and conclusions.
• The corresponding code for the paper. Students highlight who has written which part of the code. Each student receives an individual grade for her/his part of the code.

Effort requirements
Active participation in the intermediate presentation, and final presentation session, as well as the supervision sessions. Students may miss at most 2 supervision sessions.
 
Students are expected to have knowledge of:

• Statistics
• Econometrics
• Introduction to Programming in R
• Data Analysis & Visualization I
• Data Analysis & Visualization II
• Applied Microeconometric Techniques

Courses that build on this course

• Bachelor Thesis

In case online access is required for this course and you are unable (or do not want) to buy the access code, you are advised to contact the course coordinator for an alternative solution. Please note that access codes are not re-usable meaning that codes from second hand books do not work, as well as access codes from books with a different ISBN. Separate or spare codes are usually not available.