MSc Economics and Data Science

This unit will help students on the MSc Economics and Data Science in the development of vital study, employability and programming skills. Students will be supported in their development of the vital programming skills (R in Semester 1, Python in Semester 2) that are needed to implement the advanced methods taught in the Data Science & Machine Learning units.  

As part of this unit students will also work (in groups) on substantial empirical projects. Through this group work students will learn to deal with issues of data acquisition, handling, wrangling and security as well as ethical issues surrounding the curation of data-sets.  

Throughout the unit und through the work described above students will be supported in developing vital employability skills, such as working in a group and communicating results to a variety of audiences. 

ECON61351 AND ECON62012 are co-requisites for ECON62020 Available to MSC Economics & Data Science only

develop vital study, employability and programming skills

develop vital programming skills (R in Semester 1, Python in Semester 2) that are needed to implement the advanced methods taught in the Data Science & Machine Learning units.  

provide experience in team work on substantial empirical projects.

develop an understanding of issues surrounding data acquisition, data handling, data wrangling and data security

develop an understanding of the ethical issues surrounding the curation of datasets.

gain experience communicating statistical results to a variety of audiences.

This unit will help students on the MSc Economics and Data Science in the development of vital study, employability and programming skills. Students will be supported in their development of the vital programming skills (R in Semester 1, Python in Semester 2) that are needed to implement the advanced methods taught in the Data Science & Machine Learning units. As part of this unit students will also work (in groups) on substantial empirical projects. Through this group work students will learn to deal with issues of data acquisition, handling, wrangling and security as well as ethical issues surrounding the curation of data-sets. Throughout the unit, and through the work described above, students will be supported in developing vital employability skills, such as working in a group and communicating results to a variety of audiences.   

In order to be able to take up positions in government, central banks or private sector organisations as a data analyst/economist students will have to be able to demonstrate strong skills in the areas supported by this unit:

Programming  

Data handling

Group working  

Communication (oral and written) 

Semester 1

Data  

Availability and sources 
Security 
Ethical issues

Databases/SQL

Programming in R

Setup

Data Wrangling 
Data Science techniques 
Data Visualisation  

Collaborative working

Use of Github 
Communication in Teams 
Work sharing

Employability

Career options 
Skills and Portfolio presentation (CV, LinkedIn, GitHub pages)

 

Semester 2  

Data  

Databases/SQL 
 

Programming in Python

Setup

Data Wrangling 
Data Science techniques 
Data Visualisation 
 

Communicating

Communicating in a group 
Communicating with non-technical audiences

Student work will be organised around problem sets and empirical group-work projects communicated through the unit’s Blackboard site.  

Students will meet in weekly three-hour workshops in which they will finalise or continue work prepared asynchronously. Any learning materials required will be delivered through the unit’s Blackboard site.  

 

Workshop attendance: 72h (2 semesters x 12 weeks x 3h)

Prep work on problem sets: 10h (10 x 1h)

Guided programming training: 100h

Group project work: 100h (Semester 2 only)

Employability skill work: 18h

 

Sum: 300h 

Programming Tests (PT) (Sem 1: R, Sem 2: Python), 20%

Group replication Project (REP) 1,000 words, 30%

Group project (written project + presentation) (PRO) 1,500 words, 50%