As an economist you will deal with real-life data. We use these data to understand crucial relationships in the econmy, to discover stylised facts and to uncover causal relationships. In particular the latter is a conceptually really difficult endeavour and you will have to attend econometrics course units to understand how we use data to establish the existence of such relationships.
We live in times of Big Data. As many transactions in the economy occur via automised services which record these transactions, many organisations (think Google, Facebook, VISA, etc.) are generating truly gigantic datasets. Most of these data are private and not publically available.
However, a very large number of data are available publicably. These are often generated by National statistics agencies, or by government sponsored academic institutions which organise large surveys. Below is a list of places useful data sources for economists.
You could also browse a range of websites which link to a variety of datasets:
Most data will come in some form of sreadsheet. As an economist it is vital that you have spreadsheet skills which will allow you to handle and explore the data. You can even do some proper data analysis in the spreadsheet although, for more advanced analysis, you are likely to use some other software (see Econometrics section).
Different spreadsheet programmes exist but the most poplar ones are Microsoft Excel and Google Sheets.
There are numerous resources available online and for free to help you learn or refresh these skills. Use an internet search engine or search in YouTube to find help on whatever it is you want to learn. If you want more structured and complete advice you may want to follow an online course in Excel. They are typically free or very cheap.
Here (YouTube, 8.46min)is an introduction for the absolute beginner.
If you want to practice your Excel skills on some super interesting economic problems you can work through the empirical problems in CORE - Doing Economics. Any links below labeled “CORE” link to empirical projects utilising the respective techniques.
As an economics student, and importantly graduate, you should be able to do the following things in a spreadsheet:
Econometrics is an important part of any economists toolbox and you should expect to learn a fair bit of econometrics in your programme. While you will no doubt learn about important conceptual issues (Can I use the data to establish a causal relationship?), theoretical properties (Is this estimator unbiased?) you will also have to learn to get your hands dirty.
This will imply statistical programming of sorts. The Economics Department of the University of Manchester has written a specific resource to support students with this skill, Econometric Computing Learning Resource (ECLR). This resource covers some basic techniques for Economists in the R programming language.
Python is a generalist programming software and used often for Data Science Work. There are many online resources to learn R. Here are links to materials for a workshop (February 2024) run for University of Manchester students: Google Collab, Jupyter Notebook for use in VisualStudio.
Another popular statistical language in Economics is STATA and the Stata Corporation provides a list of useful STATA tutorials.