Coding Demonstrations: Programming for Social Science Research

Computational methods for collecting, cleaning and analysing data are an increasingly important component of a social scientist’s toolkit. Central to engaging in these methods is the ability to write readable and effective code using a programming language.

Topics

The following topics are covered under this training series:

1. Becoming a Computational Social Scientist - Learn how to enhance your existing social science skills with key computational skills to step out into computational social science.

2. Introduction to Python for social scientists - Learn how to utilise the Python programming language for core social science research tasks.

3. Setting up your computational environment - Learn how to create, manage and share a Python computational environment.

Materials

Start by launching the Binder virtual environment in your browser by clicking this button .

The training materials for each of the of the topics listed above can be found in- including recordings, slides, and sample Python code - can be found in the following folders:

• code - run and/or download the code examples using our Jupyter notebook resources. These are the files that end in .pynb. Clicking on these files will open a new browser window with the code notebook running in a virtual environment! These virtual environments are temporary - your changes will not be saved and the environment will shut down if you remain inactive in that browser window too long. But you can always open it again.
• webinars - watch recordings of the coding demonstrations on our Youtube channel.

Acknowledgements

We are grateful to UKRI through the Economic and Social Research Council for their generous funding of this training series.

Further Information

• To access learning materials from the wider Computational Social Science training series: <>[Training Materials]
• To keep up to date with upcoming and past training events: [Events]
• To get in contact with feedback, ideas or to seek assistance: [Help]

Thank you and good luck on your journey exploring new forms of data!

Dr Julia Kasmire and Dr Diarmuid McDonnell
UK Data Service
University of Manchester