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EC9D8: Foundations of Data Science in Economics

  • Pedro Souza

    Module Leader
22 CATS - Department of Economics

Principal Aims

Analyses in all fields of Economics nowadays make frequent use of large and detailed datasets ("big data"). The explosion in data access and availability opens many opportunities for applied research, as well as new challenges on how to handle, process, and extract meaningful conclusions from the data. The aim of the module is to introduce students to the R programming language and basic concepts of data science; and to provide and "hands-on" experience with economic data. The module lays the foundation to more advanced materials.

Principal Learning Outcomes

Subject Knowledge and Understanding: Be able to process and work efficiently with large datasets. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Project and test.

Subject Knowledge and Understanding: Develop and enhance computer skills in the R language, including the writing of clear and reproducible R codes. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Project and test.

Subject Knowledge and Understanding: Be able to use R to process data and apply data-science methods. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Project and test.

Subject Knowledge and Understanding: Be able to analyse economic data in R, and understand the difference between predictive and causal models. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Project and test.

Subject Knowledge and Understanding: Be introduced to data science methods that extract and condense information from large sources of data. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Project and test.

Syllabus

- Overview of R and data types. Data as text and date formats;

- Operators, loops, apply family, defining your own functions, scoping rules;

- Reading and writing data;

- Data extraction and acquisition from web or databases (web scraping);

- Organizing, merging and managing data;

- Data visualization;

- Econometrics in R;

- Big data methods: K-means and Principal Component Analysis;

- Optimization;

- Simulation and code profiling.

Context

Assessment

Assessment Method
Coursework (100%)
Coursework Details
Project (70%), Computer-based test (30%)
Exam Timing
N/A