Introduction to Applied Data Science

Course goals

On effective completion of the course, students should:

• Understand the basics of R programming in a data science context
• Be able to independently acquire data from a variety of sources
• Understand and be able to analyze non-standard formats of data such as text and spatial data
• Be able to integrate code in reporting, thereby writing reproducible code and analyses

Academic skills
This course focuses on the following academic skills:
 
Analytical skills:

• Being able to identify, interpret and critically evaluate the main line of reasoning, for specific questions.

Information processing

• Being able to process and analyze data using the R programming language

Content

The course will provide a practical introduction to the tools and techniques of modern data science. This course aims to familiarize you with the basic aspects of data science and the process of data acquisition (APIs and Web Scraping), which will allow students to independently collect and acquire data from online sources. Afterwards, we introduce students to the toolkit to process and analyse text data. Special attention is paid to LLM-related workflows. Finally, we will focus on the analysis of spatial data.

Most of the applications and assignments in this course ask you to answer concrete economic questions. The philosophy behind these assignments is that you answer questions from the ground up, just like researchers do, and just like you will have to do at a later stage of your study. As such, this course is also an introduction to what economists do when they conduct empirical research. We develop these skills using the R programming language.

Format
1 lecture (2 contact hours) and 1 tutorial (2 contact hours)
 
Assessment method
1 Mid-Term Exam (40% of final grade), 1 End-Term Exam (60% of final grade);

Effort requirements
Students are expected to attend 6 out of 8 tutorials to meet the effort requirement for this course.

In case online access is required for this course and you are not in the position 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.

Place of the course within the curriculum:
Mathematics, Statistics and introductory economics courses. This course is part of the Dedicated Minor in Applied Data Science for Economists. This is the first course, following which you will learn about:

              Introduction to Programming in R (ECB2PR, year 2, period 3)
              Data Analysis & Visualization I - Supervised Learning (ECB2ADAVE, year 2, period 4)
              Data Analysis & Visualization II - Unsupervised Learning (ECB3ADAVE2, year 3, period 1)
              Applied Microeconometric Techniques (year 3, period 2)
              Data Science Lab for Economics (year 3, period 3)