Skip to main content

Unit information: Economic Data in 2020/21

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Economic Data
Unit code EFIM10016
Credit points 20
Level of study C/4
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Hans Sievertsen
Open unit status Not open




School/department School of Economics
Faculty Faculty of Social Sciences and Law


This unit focuses on obtaining, processing and presenting economic data. The unit consists of three elements. First, students are introduced to the software packages MS Excel and R, including fundamentals in programming. Second, we discuss principles of good and poor data visualization practice with applications in R and MS Excel. Third, the students learn concepts that are important for understanding and presenting economic data in a non-misleading way, for example the definition of GDP, price-indices, survey weights and log-scales. The main software for the unit is R (which is freely available). Methods and solutions using MS Excel will also be shown.

Topics covered will include

  • How data is collected (e.g. issues with measurement error, missing values, survey weights).
  • Fundamentals of programming (e.g. object types, control structures, functions).
  • Principles of good and poor data visualization practice (e.g. the lie factor, data ink ratio)
  • Definitions of internationally used variables (e.g. GPD, Unemployment rates, consumer price index/inflation, Purchasing Power Parity).
  • Publicly available data sources from statistical agencies and international organizations.

The unit will draw on links that the department has with the Office for National Statistics.

Intended learning outcomes

Students will be able:

  • To obtain, process and describe data using verbal, numerical and visual methods;
  • To understand the conceptual issues in producing estimates of variables such as GDP, PPP or inflation;
  • To select appropriate visualization methods.
  • To select the appropriate software for a data task (i.e. spreadsheet software vs. programming software)
  • To create small programming scripts and create advanced graphs

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions such as online teaching for large and small group, face-to-face small group classes (where possible) and interactive learning activities

Assessment Details

Coursework project (100%) This assesses all learning outcomes

Reading and References

Schwabish, J. A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209

Few, S. 2012. “Show Me the Numbers”, Analytics Press; 2nd New edition (30 Jun. 2012)

Grolemund, G. and H. Wickham 2016. “R for Data Science”, O’Reilly

Cairo, A. 2016. “The Turthful Art”, New Riders; 1st edition. Chapter 2.

Tufte, E. R. 2001. “The Visual Display of Quantitative Information”, Graphics Press USA; 2nd edition (31 Jan. 2001) (optional)