Data skills modules

Key data skills

There is a wealth of data available for reuse in research and reports – but how do you get started with finding good quality data, understanding it and starting your analyses?

These free, interactive modules are designed for anyone who wants to start using secondary data. They cover the following topics:

Introductory modules

Themed and dataset modules

You can follow the modules in your own time and dip in and out when needed, and you can get a certificate of completion at the end of each module.

Please let us know if you have any feedback or suggestions for enhancements or other topics.

We hope you enjoy the modules!

Research data management skills

If you would like to learn about research data management, try these interactive modules.

Introduction to survey data

Learn about how to find survey data from the UK and around the world, what you need to know about it before you can start analysing it, and how to produce simple tables and graphs for your research or reports.

Level: Introductory (no previous knowledge assumed)
Time: approx. 2-2.5 hours (you can dip in and out of the materials and return as you like)
Software: We use SPSS in the examples but you can do the examples in other statistical software. You can follow the module without having statistical software.
Dataset used: Quarterly Labour Force Survey, January-March 2015: Unrestricted Access Teaching Dataset
Certificate of completion available: Yes

Introduction to longitudinal data

Longitudinal studies collect data about individuals, households, businesses or any other unit of observation over time so they can be used for following changes over the life course. Learn what longitudinal studies are available, key features and issues with using longitudinal data and how to start some basic analyses.

Level: Introductory – but if you are new to surveys, we suggest you complete the surveys module first
Time: approx. 2 hours (you can dip in and out of the materials and return as you like)
Software: We use SPSS in the examples but you can do the examples in other statistical software. You can follow the module without having statistical software.
Dataset: UK Household Longitudinal Study (Understanding Society) (End user licence version)
Certificate of completion available: Yes

Introduction to aggregate data

Aggregate data are data combined or ‘aggregated’ from several measurements and are often produced at geographical levels such as countries or regions. Learn where to find these data, how to produce summary statistics using them and how to create a choropleth map.

Level: Introductory (no previous knowledge assumed)
Time: approx.1.5 hours (you can dip in and out of the materials and return as you like)
Software: Excel or Google sheets (Unit 3) and QGIS (Unit 5). You can follow the module without having these software.
Dataset: World Bank (2016) Subnational Population Database, and Census aggregate and boundary data.
Certificate of completion available: Yes

Exploring crime surveys with R (Beta version)

Learn where to find survey data to explore crime and how to access it. This module also covers getting started with survey data using R Studio including basic functions, exploratory analysis, visualisations and conducting weighted analyses.

Level: Introductory for crime survey data, R and R Studio. Basic knowledge of exploratory analysis of surveys is assumed. If you are new to using surveys, you may find it useful to complete the Introduction to survey data module first.
Time: approx. 3 hours (you can dip in and out of the materials and return as you like).
Software: R and R Studio (open-source software).
Dataset: Crime Survey for England and Wales, 2013-2014: Unrestricted Access Teaching Dataset.
Certificate of completion available: Not yet (will be available soon).

Introduction to the British Social Attitudes Survey

Learn about the British Social Attitudes (BSA) survey design, questionnaires and weights, how to access the data from the UK Data Service, how to explore variables using documentation and how to create population estimates using weights and survey design variables.

Level: Basic knowledge of exploratory analysis of survey data using R or SPSS is assumed. If you are new to using surveys, you may find it useful to complete the 'Introduction to survey data' module first.
Time: approx. 1.5 hours. Dip in and out of the materials and return as you like.
Software: R or SPSS (using syntax).
Datasets: BSA 2017 and BSA 2020 (End User Licence versions).
Certificate of completion available: No.

De-identification and anonymisation of transcript data

Learn how to anonymise interview transcripts effectively. The module covers the principles of anonymisation, offers practical steps for identifying and redacting personal information, and discusses the ethical considerations involved. The module is designed to help researchers ensure that their data is securely anonymised while retaining its usefulness for analysis.

De-identification and anonymisation of quantitative data

Learn how to anonymise quantitative data effectively. This module explains the principles of anonymising datasets, provides practical techniques for safeguarding information, and explores the ethical implications of data anonymisation. It's designed to assist researchers in ensuring that their quantitative data is anonymised while maintaining its integrity for analysis.

Ethical consent and data sharing

Learn how to navigate the ethical and consent considerations in research. This module covers the principles of obtaining informed consent, managing participant data responsibly, and ensuring ethical compliance throughout the research process. It’s designed to guide researchers in upholding ethical standards while conducting studies, ensuring that participants' rights and privacy are respected.

Best practices for documenting data collections

Learn how to effectively document research data collections. This module guides you through the principles of data documentation, offering practical advice on how to create thorough and clear documentation that enhances the usability and discoverability of your data collections. It’s designed to help researchers ensure that their data collections are well-documented, facilitating future reuse and analysis.