Module on the MA Data Journalism at Birmingham City University
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MED7373 Data Journalism on the MA in Data Journalism at Birmingham City University

This repo contains materials for the module on the MA in Data Journalism at Birmingham City University

Module Synopsis

Data Journalism aims to facilitate a flexible and adaptable skillset, including the use of ‘computational thinking’ and communities of practice, that provides a basis for students to critically adapt to both new and existing data journalism techniques.

You will also be learning about other aspects of data journalism in sister modules, for example the Narrative module will give you a range of skills for telling data-driven stories using video, audio, visual journalism, visualisation, and interactivity. In Research in Practice you will explore research around newsrooms. In Specialist Reporting, Investigations and Coding you will expand your coding and investigative skills, and in Law, ethics, regulation and security you will build your infosec skills.


  1. Identify, gather and communicate stories based on structured information using data journalism techniques and technologies for an identified audience
  2. Critically evaluate the professional, legal and ethical contexts surrounding data journalism and apply that to a specific project

Week by week outline

This module begins with formal classes and becomes more student-driven as it progresses. You will be expected to experiment with techniques ahead of sessions, so that class time is spent more fruitfully in interactive discussion rather than one-way lectures.

You will also be expected to feed your own experiences into each class - and your own problems and questions - rather than coming to the sessions with nothing to contribute or build on. As independent learners the emphasis is on you to drive your learning forward through conversation rather than accept it passively.

Week 1: Data journalism's 3 chords

By the end of this week you should be able to use core spreadsheet techniques to find stories, including sorting and filtering, pivot tables, and be able to calculate change and proportions. You will also be able to use GitHub to clone or fork a repo.

You will find instructions on how to get started with GitHub in another repository here

Week 2: How to think like a data journalist: data literacy and algorithmic and computational thinking

By the end of this week you should be able to use a range of spreadsheet functions - but more importantly, use computational thinking to break down editorial challenges into problems that can be tackled systematically, quickly and effectively, with the potential for automation or semi-automation as algorithms.

Additional resources: OpenLearn: Computational thinking and automation

Week 3: Mobile first: responsive and other design considerations

By the end of this week you should be able to create a basic HTML page with CSS styles, and explain the basics of design for mobile devices.

Before the class: read Learning HTML and CSS by making tweetable quotes on Leanpub or OJB

Additional resources: tutorials on responsive web design (free for BCU students)

Week 4: Making it visual, making it interactive - JavaScript

By the end of this week you should be able to explain basic concepts in JavaScript and use it to create basic interactivity and/or visualisations. Note: the Narrative module class on principles of visualisation and visual design, and the class on ergodic storytelling are particularly relevant to your work in this area

Week 5: Solving data problems - R

By the end of this week you should be able to identify common data problems, and use techniques to solve those. You should also be able to use basic data processing and analysis techniques in R.

Week 6: Reading Week. No class.

During this week you should be consolidating your learning so far, developing your skills on one or more editorial project, reviewing examples of data journalism, and continuing to read widely around the subject.

Week 7: Live and connected storytelling: APIs

By the end of this week you should be able to use a range of techniques for fetching data from APIs in both R and JavaScript, and explore possibilities for dynamically analysing and/or presenting that data.

  • Workshop: Using APIs in JavaScript and R. Fork this!
  • Task: Development of data journalism project
  • Reading: Bogost et al (2010) Newsgames: Journalism at Play

Week 8: Critical cartography: why, how - and when - to map

By the end of this week you should be able to create a range of map types (point, shape, heat) and talk about the ethical issues surrounding mapping. You should also be able to use SQL to query data.

Week 9: Fetching the haystack, finding the needle: coding for finding stories

By the end of this week you should be able to use basic commands in the command line to fetch, find, combine and move data. You should also be able to explain how regular expressions (regex) work, and use them to locate and extract information from files.

You can find more tips on using command line in another repo here

Week 10: Big data, sensor journalism, and SQL

By the end of this week you should be able to identify techniques for working with large datasets, and issues surrounding big data, linked data, open data, and sensor journalism.

Week 11: Here come the robots: AI, machine learning and bots

By the end of this week you should be able to identify ways that AI, machine learning and bots can play a role within journalism, both internal- and public-facing, and articulate the critical and ethical issues raised by AI technologies. You should also be able to create a basic bot, and identify relevant libraries for using machine learning within programming.

Weeks 12-14: Newsroom and planning

In the final weeks of the semester you will take part in a live newsroom, as a data journalist. You will need to consider whether to form a team that works separately (e.g. on investigations), in a service role (e.g. producing complementary material to stories by others), or some combination of both. You will find a range of readings on Moodle that you should use to inform those decisions, and that you can draw on in your reflection. Those include:

Final Assessment