This a post for my first assignment at the MA Online Journalism
Data journalism: my induction
It wasn’t a casual choice. From my degree I have some experience on audio and video journalism. I made radio, podcasting, audio slideshows and even some infographs. But Computer Assisted Reporting was completely new for me.
I decided to explore it because I have curiosity, I know that it is a growing field and because I’m a little bit masochist.
At the beginning I had a very obvious problem: how am I suppose to blog and comment about data journalism if I don’t have any idea? Well, I think I solve it in a good way and I wrote about all the things that were useful for my learning.
I know that my contribution to the community was very poor in terms of quality, but blogging and tweeting about this was useful for taking my first steps.
For example, my first post was compilation with the most interesting Twitter lists on DDJ and thanks to this research I discovered very interesting people to follow. Moreover, I’m currently building my own list with the most useful users for my projects.
Although I haven’t made a post about it yet, I learn a lot reading blogs about data journalism. I’m sure in the few weeks I’ll improve my current RSS list, but this sites were very useful so far. They allow me to see what are the main trends and also discover new techniques that may be useful for my projects.
This was my starting point: people to follow and blogs to read. Only with this I could talk about learning new skills.
If I want to work on data journalism I will need to develop some skills and techniques.
- Look for the data. It’s true that in the UK there is a Freedom of Information Act, but you still need to know which sources are the most suitable for each case. There are many useful sites to look for: data.gov.uk, Guardian Data Store, openlylocal.com etc. I should blog about this interesting resources.
- Scraping. Many times you can’t find databases with the info you need so you scrape it from the web. This technique is quite complex to understand because it uses code but, luckly, you may play many of them without learning Python or Rubi. I tried the tutorials and the mailing list of Scraperwiki to get started and solve my problems. It’s amazing the first time you bring all the information you need from a webpage to a spreadsheet!
- Excel. This is an essential software for Computer Assisted Reporting. I’m getting familiarized with the interface and the main options and also trying to use the most useful formulas. In fact, I wrote a post about it.
- Refine. Datasets are not as clear as we wish. In fact, many times they are really messy. That’s why Google has created Refine. It’s a very interesting tool but not very intuitive, so I still need more practice with it.
- Find the story behind the numbers. I think that my journalist instinct is a bit rusted, because I still have problems to ‘read’ data from a professional point of view. I’ll keep working with spreadsheet to learn where I have to look to.
- Visualizations. I’ve just tried Many Eyes and the next steps are Google Fusion and Tableau. I’ve read reports about both tools and they seem very powerful.
Engage with the community
Honestly, I didn’t talk too much with the community. I don’t feel confident enough. I’m a very beginner so I don’t have an opinion about most of the things related with data journalism. Basically I just share interesting links on the social networks. But there are two things I’m proud of.
- The interview with Caroline Beavon. We had a very interesting talk about Computer Assisted Reporting and visualizations. She taught me that the best graph is the one that is in the middle between beauty and clarity.
- The blog post for the OJB about La Nación Data. It was not just the opportunity of writing in a famous site but also make a good contact in Argentina. I met two of the main data journalists of the newspaper and we are still connected via e-mail and Twitter.
Finally, I tried to arrange an interview with Simon Rogers from The Guardian, but it wasn’t possible (yet).
- Keep working on my reading and Twitter list
- Develop my technical skills
- Look to as many datasets as I can to find the stories.
- Engage more with the community
Currently, I’m working on a piece of data about A4E funding, but I’m still waiting for some information from the SFA, so I can’t say much more. But I hope that this will be my first story in data journalism.
For my final assignment I thought in the impact of the rise of the tuition fees on the number of applications. But I don’t want to show just the difference between two figures. I would like to go further and research which social groups or social classes are more affected. For this project I will need to understand the british university system, how it’s funded, how students pay their fees and where may I look for the data I need.
I also explored a little bit of podcasting. In fact, I posted three pieces. One was a collective work with my fellow students, where we had a discussion about audio journalism an its potential. Another was the interview with Caroline Beavon I mentioned before and the last one was about Manuel Fraga, a very controversial Spanish politician.
From the feedback I received, I’ve got the following conclusions:
- Sound quality is VERY important. Many people highlight how good was the recording of the interview, even we where in a café.
- A good narrative structure makes things go easier. I need to improve the middle point of my podcasts, especially if I work with non-English speakers.
- I need to pay more attention to the audio edition. That means volumes, fade in, fade out, etc
More information about my work
Delicious with data journalism links
My question in the Scraperwiki mailing list