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Learning paths into data journalism

How five people got their start in the field

Breaking into data journalism can often be challenging for new graduates or seasoned journalists. There's no one route into the field for those lucky enough to secure a coveted internship or even a job.

Embarking on this career path inevitably leads to some form of data journalism education or training for aspiring data journalists. That's because data journalists need to have the skills to wrangle data, design data visualisation, code interactive stories, or write articles.

Some study data journalism in a university with clear intent, while others accidentally stumble upon the discipline in a newsroom. You may even encounter those who got their start as programmers or designers in different industries and moved into a newsroom. But it is also not unusual for a self-taught approach to be taken. There's no one way to study or enter the field, whether it is free YouTube tutorials to intense data bootcamps.

The good news is that the amount of resources relevant to building a solid foundation in data journalism has been on the rise throughout the past decade. Academic and educational institutions have been adapting to the changes in journalism by including data journalism in their programmes. Slowly we also see some academics earn PhDs in data journalism.

To help you navigate this, we profile three data journalists and two academics showing their pathways into the field. Those featured come from various backgrounds, ranging from self-taught data journalists to those who chose a more formal training or academic experience.

Whether you are a self-taught learner, someone interested in obtaining a Bachelor's or Master's degree, or even considering a PhD in data journalism, your path to a future in data journalism may be reflected in theirs.


Roberto Rocha, Data Journalist at CBC Montreal

Pathway: Self-taught

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How did you get into data journalism?

It was a total fluke! I was at the Montreal Gazette for three years working as a tech reporter in the business section. I became interested in tech startup language - HTML, divs, web 2.0. This was 2008 or so. I went to meetup groups and events to meet tech entrepreneurs to get story ideas. I happened to met Michael Lenczner, who at the time was the head of a non-profit that encouraged businesses to give their WIFI for free to customers. Fast forward a few years, I became the interactive and online editor, and my job was to do things for the web. Not just dump print products onto the web, but actually use the web to do interesting things. For the most part, I was doing videos, audio slideshows, pretty simple stuff.

Around this time, I approached Michael at another networking event, and he told me I should try to do data visualisation in Tableau and see what I come up with. I followed his advice and was hooked immediately. I could take these datasets, and instead of just writing up a bunch of boring stats, I could make it visual, interactive and engaging. Look what the numbers say! It was a total rabbit hole from there.

I became good at Excel, started going to NICAR conferences in the US, learned how to write some Python, JavaScript, and just kept going deeper and deeper. I was utterly self-taught by attending conferences, taking MOOCS and reading books. Now I’m starting to get into deep learning and AI. I don’t want to stop learning.

What are some of the pluses and minuses in taking the self-taught route?

The biggest plus and minus are the same -- you decide what path to take, and the plus is that you decide what you’re interested in. Data journalism is so specialised these days. You can either go into interactive data visualisation, analytical big data, statistics, become a news app developer, or create explorable databases on websites.

The minus is how do you know which path is the right one? Do you go by market need? By what job posts are asking for? What your boss wants you to do? Does your boss know enough to make that decision? It can be very confusing and overwhelming with all the stuff there is to learn out there - R, Python, Django, JavaScript, D3. There is way too much to learn, and it can get, without a clear sense of direction of what to specialise in, almost paralysing.

You have to decide what you want to be good at. You can be a generalist and be pretty good at many things, or you can choose to be a super-specialist and make the most impressive, interactive and interesting D3 charts, for example.

If you could start over again, what would you do differently?

I honestly wouldn’t change anything. Even though it was meandering and probably took longer to arrive than somebody who was more focused, I wanted to do everything. Given the total absence of direction and vision from employers, that was pretty much the only thing I could do - meander.

I chose to specialise in the more data science part of data journalism and become really good at getting, analysing and cleaning data. Being able to understand data deeply, know where it can be misleading, know its flaws. I do advanced analytical stuff on massive datasets like network analysis, text mining, and other machine learning techniques.

There has never been a better time to be a self-motivated learner. Google, IBM, and all these big companies are desperate for talent and putting free courses online. Jeremy Merrill, a data journalist I admire a lot, recommended fast.ai to me as a quick and straightforward way to get foundations with image recognition and text. So I watch the videos on the platform, rewatch them, and study the notebooks they put online.

What advice do you have for new graduates who are leaping from learning to their first jobs?

The best advice anyone can give is to make things. Just do a cool thing you can put on your website to show off what you can do. If you’re lucky, you’ll get paid for it. Find a project that’s a little above your level, and level up while you’re doing it. Whether it’s scraping a bunch of tweets from the Twitter API and presenting it in a cool way, you’ll learn about APIs, dealing with messy data, and visualising it.


Michelle McGhee, Journalist-Engineer at The Pudding

Pathway: Bachelor of Science in Computer Science, Stanford University

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What drew you to data journalism?

I studied computer science in college, which has always been a big interest of mine, career-wise, but I found myself trying to figure out how to use it in a way that feels interesting, meaningful and fulfilling. When I discovered data journalism, it felt like I met my people, found my thing, and dove headfirst into it.

I like that it feels more creative. I have always straddled having both creative and technical interests and skills. Data journalism allows me to engage both of those in a way that feels unique. It’s allowed me to rediscover my love for coding. Now I can apply storytelling sensibilities and skills combined with coding to make stuff that I think is cool. It’s an honest and interesting combination of what is important to me.

Did you have any formal journalism training?

No, but I have always been interested in storytelling. I like podcasts, reading, musicals and I also teach a live oral storytelling class, and I just never connected that to being a journalist.

I worked at a radio station without prior experience in the field, but mostly I just observed other people, trying to learn as I went along. I’m still growing into it. Calling myself a journalist still feels daunting, and I’m much more comfortable referring to myself as an engineer.

There are journalism conventions and things I don’t quite get yet, but I’m starting to learn. I worked at Axios for six months before starting at The Pudding, and the data visualisation team was explicitly a hodgepodge of people with different skills.

There were some people with more journalism experience and people with less (that would be me). But it was celebrated, and everyone was learning from each other.

Why the switch from Axios to The Pudding?

I left Axios because The Pudding offered me a job, and I couldn’t say no to that. I longed to produce more in-depth, creative and long-form stories. When I first got to Axios, it was a lot of staying above water with creating quick charts to accompany stories, and that was helpful. I made hundreds of charts, which is great practice. I also got to be in a world of journalism and soak up all this knowledge.

I wasn’t a massive fan of being so tied to the news cycle, and I think that’s a big thing that appeals to me at The Pudding. I can make something on music because I love music. I don’t have to do something important this week that I’ll have to do in two days because it won’t be important later -- that isn’t ideal for me, but it was great grounds for learning.

Can you speak about the skills you learned in your computer science programme and how they have transferred into your role as a data journalist?

I’ve been coding for a long time, and I feel very comfortable writing, debugging and organising code -- that’s what I mostly got from school. I took one web development course but didn’t find it interesting at the time. Out of college, I learned React and everything related to web development and found that my whole job was this class I didn’t pay attention to.

When I decided I wanted to do data journalism, I was teaching myself everything, but at the same time, I had an excellent foundation coding-wise. The main hurdle for me was to learn D3.js.

Where did you go to learn D3?

For D3, I like observable blocks of visual examples where you can see and interact with the code. I usually reference the documentation for what I’m trying to do.

When I am first trying to wrap my head around what I am really trying to do, I find blog posts where someone is slowly and clearly explaining how to think about stuff to be really helpful. I had Amelia Wattenberger’s blog post on combining React and D3 open for months.

What advice do you have for people looking to get into data journalism?

Just do things and don’t wait for permission. Don’t wait for someone to carve out a lane for you to do something. Just go ahead and do things that are interesting to you.

When I wanted to get into data journalism, I didn’t have a job in it; I just knew that I wanted to work at The Pudding and have them teach me how to be like them. When I was starting off, there was nobody giving me a structure for how to do things, so I created my own structure and learned skills.

I was in The Pudding’s Slack channel, and my relationship with them started because I made something and sent it to Russel Goldenberg (Editor at The Pudding) and asked for his feedback. I sent the piece about visualising every spell in the Harry Potter books.

I was just trying to imitate these things I thought were cool. I worked on it for months, sent it to him, and asked how it could be better. I had to push myself to do that and couldn’t wait for someone to tell me it was okay.


Natalie Sablowski, Freelance Data Journalist for WDR

Pathway: Double Master's degree in Eastern European History and Digital Humanities, Universität zu Köln

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How did you get your start in data journalism?

It was a process. My Master’s programme allowed me to take courses and get credits for everything offered within the philosophical faculty. It was for people with different backgrounds, so for example, I was studying with architects who wanted to study the more liberal sciences part of architecture.

I was attracted to what I saw -- they were doing 3D modelling for artefacts, going to seminars with sprint projects, learning about agile and scrum, we made a VR game.

One part of my Master’s was to go abroad to Russia, Poland or Serbia, and I chose to go to Moscow’s Higher School of Economics (HSE). They have a Master’s degree in data journalism.

When I saw this, I got very curious because it was similar to what I was doing with the computer science side of liberal arts. This interdisciplinary approach just really caught my interest. I was taking Python courses, Intro to Graphics, and many other courses related to data journalism.

Then I got very lucky because of a Google News Lab Fellowship. This was in 2018, and I was very slow to complete my Master’s because I was doing internships, traditional journalism, and online journalism, which wasn’t satisfying me. Reporting on the daily news wasn’t what I wanted to do. After the project I did at the fellowship, I started to get more into investigative data journalism.

We have a conference in Germany called Scicar. I met someone who got me onto the team at WDR (German Public Broadcasting Institution), where I started working as a freelance data journalist. For the past year and a half, I’ve also been working as a Project Manager at n-ost, a network for reporting on Eastern Europe.

I manage and coordinate projects to give people the opportunity in Eastern Europe to do investigative data journalism by bringing hackers, analysts and data journalists together. Since I studied in Moscow, I have a great network of people in data journalism, and this was an opportunity to extend this network.

If you could start again, how would you approach studying data journalism?

I would start by interviewing data, learning how it was created, where it comes from. I’m not sure this exists, but ‘slow reading for data’. The complexity at this level is very low, and you have a lot of room to play around. I feel like because I didn’t have this opportunity to play around, I missed out.

I wouldn’t get into serious programming. Newcomers always say they haven’t learned to programme yet and they think this is the most important thing. Of course, it’s great if you can code, or at least can read and understand code, but it’s not the best way to get into data journalism because you miss out on so many things.

It’s a massive hurdle for many people, and they think it’s a must, but it’s not. Not everybody can be a super pro developer, and there will always be people that have spent more time programming than you.

What advice do you have to new data journalists who are looking for their first gigs?

I can only talk about what I experience in Germany and Russia, but the best way to get jobs is to get into the community. In Germany, we have this vast open knowledge and hacking community. Through this, you get to meet a lot of people and can collaborate on some projects together.

It’s essential to have some samples of work to show. You don’t need to be a genius. Many people get there by discipline and do something with the skills you have to showcase your work.

How do you continue building your data journalism skillset?

The primary source of knowledge is from a developer I work with. Also, pointing back to the community, I became a member of a hacking space in all of the steps I took to become a data journalist. We have weekly meetings where we do stuff together.

And then, of course, StackOverflow and Udemy. I know there are a lot of resources -- online courses. I’ve done these, but I’m not very good at keeping up with them, and it’s better to learn in person.


Florian Stalph, Postdoctoral Research at Ludwig-Maximilians-Universität München

Pathway: PhD in Data Journalism from University of Passau

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How did you get into data journalism?

I did my Bachelor's and Master's degrees in Communication Studies at the University of Passau. During my Master's, I took some computer science courses, which is when it all began.

I first heard about data journalism in 2014, and I got pretty excited because it was a new angle to journalism and communication studies. One professor in the computer science department was researching open data, and they brought in some social science students who would do usability testing to see if they could integrate their platforms in a journalistic workflow. We were the beta testers.

After making this connection, I told my supervisor I wanted to do a Master's thesis on data journalism, and he said it was a great idea and let me go ahead with it. When I moved on to the PhD, I realised there is so much potential for research in this young field.

What did the PhD consist of?

I think a lot of universities follow this approach. It was an accumulative dissertation where I published at least three papers, wrote a framework of all the findings, and put them all together. It was exciting to connect all the papers and small studies into one framework.

With publishing papers, it's always a gamble. Some get published quickly, and some take one or two years with many rejections and revisions, which is challenging but also exciting. Exciting because you're getting your research out there, you're interacting with the scientific community, and you can present your papers at conferences.

For me, the most formative part was that there are so many supportive and interesting people out there, particularly in the data journalism field. There are maybe 50 people, you're meeting them regularly at every conference, and I wanted to go there every year and stay in touch with these people.

Do you have any interest in producing data journalism, or are you more interested in the research aspect?

During my PhD, I worked for Datawrapper, creating data visualisations with their tools and writing documentation for their user support. I got more insight into how they work, how data visualisation works and what kind of data you need to make a visualisation. I could take that and implement the ideas into the courses I give at the University of Passau.

This was where I could get practical experience. When you're studying communications, it's not very practical at times. You're constantly fixated on these prime examples of journalism, the Panama Papers and all that, but that's not how journalism regularly works.

It's interesting to learn what data and algorithms do to journalism in general. This is really exciting and quite challenging for research because, in general, in journalism or communications studies, there are no real theories to use. That's why we borrow theories from social sciences and pick out parts to figure out how to study journalism as an observer of society.

But with all the data stuff going on, we're starting to borrow theories from computer science and social-technical fields. There is also a lot of change in communications and journalism studies and how to treat data journalism as an unusual type of journalism. There is this connection between how we in science have to deal with data journalism and all these practitioners doing the work.

How can we analyse what they are doing? It's up to us to figure out some vocabulary to describe what's changing and what they're doing by looking at other disciplines, so it's pretty interdisciplinary.

What can data journalists do to understand their field better?

We can give recommendations to do it better, but journalists typically know what they're doing. Data journalists are highly professional, and they have their community, are constantly training and learning new tools every other month. It's up to us researchers to understand what that means for data journalism.

We as researchers have to catch up in a sense and find ways to describe, contextualise and understand what they're doing.

What are the pluses and minuses of the path you took to get into data journalism research?

In communications studies, we had a lot of courses about social science and empirical methods. I never knew what this was actually for and just thought of them as scientific methods to try and measure things. But then, once I realised this connection to data journalism, I started noticing journalists who deploy social scientific methods as part of their toolkit.

Before, I thought this was something only scientists did for research reports, but there are these data journalists collecting, analysing, cleaning, assessing and visualising data and writing in a very scientific tone. It was really helpful to have this background in social sciences because it easily connects to what data journalists are doing.

Communications studies offer a good baseline for getting into data journalism, but I think it's really important to very quickly specialise in something related to data. It could be programming courses like learning SQL to understand how data structures work and what they mean. Also, try to see if there are some field experiences you can gather.

There are so many startups that are coming into the field of data journalism. Tech startups play a quite central role by now, taking on and developing projects that journalism organisations outsource.

What advice do you have for new graduates trying to make the leap from learning to their first jobs as data journalists?

First of all, I think there are a lot of great data communities. Talk with people in these communities and see if there are any opportunities to do internships. A lot of my students went on to do internships in data teams.

For them, it was always good to show these organisations they have experience from the courses they've taken. Some also move on to do their Master's with more focus on digital journalism, where there are a few modules on data journalism.


Dr Bahareh Heravi, Assistant Professor at University College Dublin (UCD)

Pathway: PhD in Information Systems from Brunel University London

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Tell us your background and what you studied.

My professional background is in software engineering and data science. Before entering academia, I worked for over 10 years in the industry, designing, developing and managing Information Systems in various organisations and sectors. I co-founded a successful software development company at 19 and sold the shares 10 years later.

My PhD was in Information Systems, and I have an undergraduate degree in Computational Science. I am now Assistant Professor at the School of Information and Communication Studies at University College Dublin (UCD) and the founding Director of UCD Data Journalism CPD programme.

Explain to us why there is such a small number of people who have done a PhD in data journalism.

Part of the reason is that the field is so new. A PhD takes a minimum of three to six years to complete, depending on where you study. If you look at the Master's programmes that have popped up in data journalism, they're not very old, and most would have started in the past six years.

As mentioned in my article, 3Ws of Data Journalism Education, I have a section where I talk about how many of the professors have PhDs and how many do not.

To have PhD graduates, you need to have recently active academics who have PhDs. The people who started teaching data journalism about 10 years ago were often not scholarly type professors. They were professors from industry who didn't have PhDs and focused on the practical aspect of data journalism and teaching.

Professors who have PhDs and produce scholarly research would be the ones who get the data journalism PhD students, such as me, who would have taught data journalism in the past seven years or so. By the time the programmes started taking PhD students in data journalism, few have yet to graduate.

At the same time, data journalism is a niche area for PhD students, and it is not as easy to have PhD students specifically studying data journalism. Some would instead prefer to do a PhD in journalism, data science, computer science or communications. Most who are interested in data journalism want to become data journalists. If you aim to become a data journalist, you don't tend to want to earn a PhD.

Where should aspiring data journalists look for data journalism programmes?

Some universities include full programmes solely dedicated to data journalism, while others offer short lectures or two-semester courses. I compiled a publicly available dataset of 221 data journalism modules and programmes around the world.

In your teaching, how do you balance the theoretical and practical sides?

Balancing this is tricky. The UCD Data Journalism CPD programme is a small postgraduate programme. I am training people to become professional journalists with a bit of academic rigour. I'm in a research-intensive university, not a professional type of university.

When I started the programme, the Intro to Data Journalism module had three assignments. One included an academic essay. This was a standard essay you'd do in a postgraduate course. The journalist students were finding it a bit torturous to do a research paper, and I found it a bit torturous to read them. At some point, I decided it wasn't adding so much to the course, and I removed that assessment.

Now the modules I teach are more practical. However, I talk a lot about the theoretical foundations of the concepts and the statistical analysis. The students also have to take a difficult module in quantitative analysis where they learn R programming. This module is entirely academic.

What advice do you have for graduates trying to get their first freelance or full-time data journalism job?

Make sure you have completed some data journalism projects, and be sure to showcase them. That's why at UCD, we have a website for the class so the students can show what they have done. My advice is don't be shy. Contact people and reach out to data journalists. The best way to do this is to go to data journalism conferences. It is a very open environment. I organise the European Data and Computational Conference and this and other events have been helpful networking opportunities for my students.

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Author bio: Duncan Anderson has worked as a journalist for The Tyee and The Tico Times. He began learning how to develop websites shortly after graduating university, and found that learning fundamental programming skills opened a door for him into the fields of data science and data journalism. Currently, he works as a full-time data analyst at an online travel agency, and uses his data science skillset to work on freelance data journalism projects. He publishes his work on his dawork website.

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