Optimizing a crash course in data journalism
This is a cross post from the Internews Kenya Data Journalism Fellowship Blog.
Internews in Kenya’s third week-long data journalism workshop, which wrapped up on August 24, confirmed our growing suspicion that in a week, aspiring data journalists can master the fundamentals of data journalism, but it isn’t easy. From finding and scraping data to cleaning, analyzing and visualizing data, it’s a crash course in both the theory and practice of applied data science. A carefully planned and balanced training program is designed to whet journalists’ appetite for more and open up access to the data journalism community.
###1. Find good storytellers
Data journalism transforms journalists’ role from information transmission to information creation. Selecting journalists who have already demonstrated critical thinking and analytical skills through investigative or feature reporting is essential. No amount of data or software can replace effective narrative that explains the human implications of the data. Choosing participants with a proven nose for news saves from having to explain why data is important.
###2. Develop a cohesive training program
Data journalism requires a diverse toolkit even at the basic level, and honing in on one topic and using the same datasets for all the exercise helps trainees understand how each step and each tool serve to dig deeper into that topic. So if the topic is education, the exercises for advanced site searches, scraping PDFs or html and analyzing data through Excel should all focus on publically available education data. The number of tools and skills can seem overwhelming and a consistent topic will help trainees undertake a data-driven investigation from beginning to end.
###3. Don’t leave anyone behind
Data journalism isn’t for everyone, but it should be for everyone in the workshop. Having enough trainees on hand for individual attention helps ensure that all trainees are following each step of the activities and that the class doesn’t move on until everyone has got it. Trainees will be even more committed if they understand that if they don’t come out of the data cleaning session with a clean dataset, they won’t have material to analyze in the next session or to visualize in the session after that.
###4. Show and tell
People have different learning styles and while providing background materials and resources can help, presenting the information in different ways during the sessions will smooth the path for many. Providing the full presentation with easy-to-follow, step-by-step instructions and screen shots to teach new software will compliment oral presentations. If a trainee misses a step, he or she will have access to the complete presentation and can check previous slides to catch up. Providing only oral instructions also makes it likely that while the trainees all come out with the desired results during the training, they don’t fully understand how they got there or how to use the software themselves. By simply following instructions as they hear them, they may not realize how they got there or they may miss the underlying concepts.
###5. Lead them down the wrong path
It is tempting to lead trainees to fantastic data sets that reveal amazing new insights, but in the real world, many data sets don’t lead anywhere. An exercise that involves calculating the change in per capita crime rate over time may show an insignificant change. Participants may jump on ANY change as significant and exaggerate the results in their story because as journalists, the instinct is to write a splashy headline. Aspiring data journalists need to be able to differentiate between a story and noise when it comes to analyzing data and how to suppress the instinct to exaggerate findings.
###6. Simplify statistical concepts
Journalists need to know how to use data responsibly but they don’t have time for a university-level statistics class. So it is important to touch on the fundamentals of statistics pragmatically with examples of erroneous reporting that confuse gross and per capita, infer causation when there is only correlation and report dramatic changes in poll results that in reality fall within the margin of error. Through these examples the session can teach journalists how to evaluate data sources, understand the basics of statistics and the price of getting it wrong and encourage them to ask experts before jumping to conclusions and reporting data wrong.
###7. Insist on teamwork
Collaboration can be the most challenging aspects of data journalism for journalists who are used to working solo. Activities that encourage trainees to work together to brainstorm hypotheses, re-design an ineffective visualization or analyze international examples of excellent long-form data journalism serve several purposes. Team members begin to think not only in terms of playing to each members’ strengths and dividing tasks accordingly but also helps them recognize the advantage of working together to solve complex data problems.
###8. Emphasize multimedia
In newsrooms, it is often a stretch for journalists to consider putting the time aside to practice data journalism skills and telling that story through multimedia can seem even less tenable. But many of the most high impact data journalism stories are a careful balance between analysis and telling a human story that puts a face to the data through video, audio and other interactive elements. Exposure to such stories and encouragement to think of data stories in terms of a multimedia package can help journalists get out of the mindset of the daily news cycle and ready for a data journalism team.
###9. Put skills to the test in a Data Expedition
The School of Data Expedition model is an excellent wrap-up activity for participants to work as a team to employ all the skills and tools they have learned over the week. Even more importantly, it reinforces the need for collaboration to complete the expedition within a few hours and encourages teams to persevere despite the hurdles the come along with a data investigation including scarcity of data, difficulty in establishing relationships among datasets and producing digestible stories for a general audience.
###10. Create a community
Veteran data journalists rely on internal data journalism teams or external resources such as Hacks/Hackers , School of Data and Data Driven Journalism for support, inspiration and resources. If trainees engage with these global communities, they are much more likely to become local leaders in data journalism.