Interview with a Data Scientist about Data Journalism
By: Duncan Anderson
Tags: Journalism, Algorithms, Data Science, Data Journalism, Python

Can you please just start by saying your name, a surprising fact about yourself, and what it is you do exactly with U Squared?

My name is Daniel Usvyat. One surprising fact is where my father is from. Most people have never heard of the place. It’s kind of an unusual place, politically charged, recognized as a country by only six other countries. So he’s originally from the Soviet Union and the independent state is Abkhazia.

Before U SQUARED, I used to work at a management consulting company. I did that for a few years but after 1 year I started to get sick of it. There is more to life than living out of a suitcase, traveling week by week. I was learning lots of skills but also building up a client base, and so I wanted to do it on my own on a smaller scale. So I went into freelance consulting in data science, and with like-minded people to myself, Istarted talking about creating some kind of brand, which we could do projects from and that’s kind of how U SQUARED was born. So we’ve grown, worked with lots of interesting clients in media, finance, insurance, and more recently the public sector. It’s been an interesting story from the get go to now. I’ve really enjoyed it. I like working on challenging projects and feeling rewarded by giving clients what we do best.

How many clients have you worked with?

Big clients? Around 6 projects. I’ve also worked with smaller startups, advising them on how they can use AI, and advisory or proof of concepts.

Have you partnered or worked with any data journalists or news organizations?

Recently I did a project with The Guardian — I didn’t work directly with journalists per se, but I was doing some stuff with NLP (Natural Language Processing), which allows them to essentially extract more information from news articles. Before this, the main thing we were looking at was keyword tags, which were human derived. Maybe it was something like “Sport” or “Brexit” but that was used to kind of cluster or group articles together. We were finding more intelligent ways of extracting additional meta-data from articles to use as a way to find commonalities in articles and understand what people like, and what drives engagement around journalism. So that was actually a really cool and interesting project.

How do you see machine learning and AI influencing journalism in the future?

I’d say it’s more data mining than AI/ML. The Washington Post a few months ago was showing the spread of COVID in an interactive way. Using some kind of simulation to show like, if you change this, how would that likely imprint a spread of the virus. And what I like about the Guardian is that they do good visualizations on elections and broad events.

For a journalist like myself looking to become equipped with some tools to produce solid data journalism, how would you structure a self-learning data science environment? So far I’ve been learning Python and I’m wondering what I should learn next.

Understanding basic data retrieval methods like how to scrape information from websites is essential. If you want to go through Google, different news sources and collect info from different topics, how would you do that? I’d say the best way to do it is using Python. It’s probably the easiest entry point and easiest thing to learn. Start with the problem and ask yourself — “What am I trying to solve?” And then ask… “Is that achievable with python through existing packages?” Probably. Then understand what package you need to use, which is as simple as going online to see if people are sharing their work and have done something similar before. Take that code and look at how you can adapt it and solve your problem and bend it to your use case and needs.

This allows you to save a lot of time rather than building from stuff that exists. No point in repeating the same thing again. So once you’ve done that, it’s a question of doing some very small collections. So if you want to collect info on the current US election, you’d map out all the different news sources you’re interested in and try it on this and see how it works. If it works, great, if not, look at what’s going wrong and adapt it. After that, you’ll look at spinning it out across everything and once you’ve done that, you look at what you have to do next.

I like to make it very simple and not get too caught up on the details. So thinking — “What do I have to do first? Ok, I’ll do that. What next? Okay I’ll do that.” Otherwise you can get lost in the cycle in trying to find the perfect solution and never really starting. So I guess to start, start small and build up and learn as you go along.

You don’t need a PHD in math or data science, you just need a willingness to learn, curiosity and resilience — sometimes you’re going to have a lot of bugs and it might be quite painful, and you might feel like you have no clue what you’re doing but you may suddenly have a breakthrough and keep on pushing on.

Sounds a bit ironic, but I’m a bit opposed to studying data science as a degree. I think it’s not really necessary — all of the info is online for free. You can do your own custom projects and get a job in data science. I think there’s too much emphasis on getting a degree. I’m a big advocate on personal projects and learning that way, as opposed to academic learning.

The market is exploding with data viz charts and visuals — some good, some not so good. What are the common pitfalls of these visualizations, and what should data journalists keep in mind when creating a data visualization.

That’s a very good point — It’s very important to not present information in misleading ways. So it’s always good to have transparency in the information that’s presented so when you are presenting these charts it needs to be very clear how they should be used. It should be user friendly, and shouldn’t need instructions… You should just be able to look at it and not misunderstand information. I guess, my opinion in the way with journalism is, is that there is always some bias — like the newspaper’s political viewpoint. I’ve never seen a newspaper that’s not biased in some way, whether it’s good or bad, it depends on whoever is looking at it. The Guardian is a more left leaning, more liberal, and has a more present view point and it represents younger generations on the viewpoint of the world which is good in my opinion. If you read some newspapers on the right wing it’s a bit more conservative, a bit cringe sometimes to read those, and feels like a kind of past generation representation of the world which doesn’t really exist anymore.

So I feel like when it comes to AI and ML the same bias can be perpetrated on the newspapers’ inherent bias they have, or whatever news source they produce.

Is data journalism less biased than traditional journalism?

Generally I would like to say that it is, but from experience I’ve seen it sometimes may not be. I feel like you can always take data sets and adjust it to suit your view point. This is what politicians do all the time — they take the same data set and make it say two different things because they have two different metrics or two cuts of data. I’ve heard people often say stuff like “We want to show this, and if we take this measure will it still make sense…?” I mean, you’re just massaging the numbers now, so it’s always down to the beholder of the info, the analyst — or on the receiving end, in consulting you have a client who is paying for the project, they may have a certain belief and unless you want to get fired, you’re going to find a way to support that belief. It sounds bad and that’s the reality unfortunately. I like to think that if you’re working with someone more open minded and with less of an ego, than hopefully what you’re saying will be true but unfortunately people have egos and agendas.

How important is it to link back to the original source and show we got the data?

In my opinion it’s good to have a high level understanding of how the data is processed and where it’s coming from. If I’m honest, the main important point is where the data comes from first of all, because if it comes from a suspect source or unreliable source that’s the first point of failure. After that, you have all the processing and data wrangling and the next step is what kind of average are you taking? The mean, or the medium? That’s quite important and then of course, understanding the core statistics of the data. What is the spread? Is it evenly distributed? This is quite important to understand. After that, they’re doing some kind of prediction from it, and they’re kind of changing some kind of interpretation.

If I have some time series data with financial news I’m using it to extrapolate what’s going to happen in 3 months time. If you don’t understand what’s done to get that result, then it can be dangerous. You can say something completely insane that doesn’t actually make sense. Just because somebody has made a prediction, but come up with a terrible prediction, understanding the efficacy of what’s being done is important and necessary.

Do you foresee data becoming more readily available from businesses, governments and organizations?

I think it hopefully will, because it would give people more ability have more understanding of what’s going on. You have, which is an interesting source, google data which has a bunch of random data sets you can search through. Google Trends is good to understand what’s trending alongside Twitter and other things especially with news and popularly of incoming trends.

A growing trend is alternative data, which is basically finding disparate data sets that aren’t used for something and using it to make predictions. So this is quite commonly used now in finance or the stock market as a basic as new sentiment, or age distribution of a country and seeing how that can influence equity prices or the future value of a company. Finding disparate data sets that aren’t used for something and using it to make predictions can be used in journalism to understand what is happening in society around injustice and inequality. You can use all these measures that are out there to get a better picture and not just a simplified measure on gender or race inequality. There’s all sorts of measures you can take in and understand on a deeper level.