This repo contains materials for the module on the MA in Data Journalism at Birmingham City University
- Module Leader: Paul Bradshaw ([email protected]) 0121 331 5367
- Twitter.com/paulbradshaw
- Room: P105
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.
- Identify, gather and communicate stories based on structured information using data journalism techniques and technologies for an identified audience
- Critically evaluate the professional, legal and ethical contexts surrounding data journalism and apply that to a specific project
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.
By the end of this week you should be able to describe what data journalism is, and what types of stories you can find and tell with data journalism techniques. You should be able to generate ideas for data journalism stories yourself, and identify some sources of data.
- Workshop: Data journalism story workshop;
- Directed study: Create a news diary
- Review examples of data journalism
- Brainstorm story ideas (here are some data sources to help)
- Reading:
- Anderson, CW (2012) Towards a sociology of computational and algorithmic journalism
- Data Journalism Heist (BCU students please ask for your voucher for a free copy)
Bonus: you will find instructions on how to get started with GitHub in this repository.
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.
- Workshop: Data journalism basics: sorting, filtering, pivot tables, calculations
- Directed study: write a simple 2-3 par story based on your analysis of data
- Reading:
- Finding Stories in Spreadsheets (BCU students please ask for your voucher for a free copy)
Additional resources: The New York Times have made their internal data journalism training materials available here and written about their training here.
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.
- Workshop: computational thinking challenge
- Directed study: Identify story ideas/leads and break down the tasks needed to achieve those
- Reading:
- Finding Stories in Spreadsheets (BCU students please ask for your voucher for a free copy)
- Adrian Holovaty (2006) A fundamental way newspaper sites need to change
- Watch: Nicolas Diakapoulos on computational thinking in the industry
Additional resources: OpenLearn: Computational thinking and automation
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.
- Workshop: fork this! Creating shape and point maps using Datawrapper; creating hex maps using R or JavaScript
- Reading: When to use maps in data visualisation: a great big guide - part 1 and part 2 on shape maps
- Reading: Thinking about maps
- Task: Write a MAPPED! story
- Optional: any chapter(s) from Mapping, Society and Technology
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.
- Workshop: Importing, analysing, and exporting in R (Fork this repo!)
- Task: Use R to clean and check your data
- Reading:
By the end of this week you should be able to identify common data problems, and use techniques to solve those.
- Workshop: identifying dirty data, and telling stories with accuracy. Choose
- A chapter from Finding Stories With Spreadsheets OR an Open Refine tutorial in the Cleaning repo tutorials list
- One of the tutorials on cleaning in R
- Task: Use Heather Krause’s data biography approach to evaluate a data source(s) you plan to use for a story
- Reading: Stray: The Curious Journalist's Guide to Data
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 in the repo
- Workshop: HTML, CSS and responsive frameworks. Fork this! Intro to responsive web design with Bootstrap
- Task: Create a responsive webpage on GitHub Pages
- Reading: Jeremy Keith: Resilient Web Design
- Reading: Manuel Matuzovic: Writing HTML with accessibility in mind; Writing CSS with Accessibility in Mind
Additional resources:
- YouTube playlist: the UIkit frontend framework (for designing responsive pages, longform and scrollytelling)
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
- Workshop: JavaScript interactivity and vis: counters, quizzes, charts (folder of exercises here)
- Task: Continued work on JavaScript
- Reading:
- Dona Wong (2010) The Wall Street Journal Guide to Information Graphics
- Manuel Matuzovic: Writing JavaScript with accessibility in mind
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, and open data.
- Workshop: Fork this!: Querying large datasets using SQL
- Task: Development of data journalism project - reflection on data-driven vs hypothesis-driven methods
- Reading:
- Vallance-Jones & McKie (2017) ch5: Working with databases
- Hammond (2015): From computer-assisted to data-driven: Journalism and Big Data
- Data.world's Gitbook on SQL is a useful resource and the platform is a good place to practise SQL queries on your own data.
This week we review your progress so far, and look ahead to the assignment. You will also find material in this section on future developments such as AI, machine learning and bots.
- Workshops:
- Fork this! Command line and regex:
- Exercise: combining stop and search data CSV files using command line
- Using grep and regex in R
- Task: Development of data journalism portfolio
- Reading - choose from:
You can find more tips on using command line in another repo here
In the final weeks of the semester you will work on your portfolio for assessment. You will find a range of readings on Moodle that you should use to inform your decisions, and that you can draw on in your reflection. Those include:
- Borges-Rey (2016) Unravelling Data Journalism: A study of data journalism practice in British newsrooms
- Figl (2017) BIGGER IS NOT ALWAYS BETTER: WHAT WE CAN LEARN ABOUT DATA JOURNALISM FROM SMALL NEWSROOMS
- Usher (2016) Inside the Interactive Journalism Newsroom, from Interactive Journalism: Hackers, Data, and Code
- Young and Hermida (2014) From Mr. and Mrs. Outlier To Central Tendencies
- Zanchellia and Crucianelli (2011) Integrating Data Journalism into Newsrooms