Give yourself an edge by becoming a Data Journalist
Tags: Media, Data Science, Data, Data Analysis, Data Journalism

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When you try to search online on how to be a data scientist, a lot of items pop up like the names of popular data science programs such as Data Science, Data Analytics, Business Analytics, and so on. In fact, there’s a deluge of online data science-related courses.

However, if you’re already working in the media industry and want to further develop your career, it sounds much more fascinating to learn how to be a data journalist.

Why I decided to be a Data Journalist

I majored in Communication Arts at the University of Wisconsin-Madison. But I couldn’t figure out the problem that plagues most students: What should I do after graduation?

There were so many different opportunities to consider and I didn’t know what to choose.

#1: Working experience at a tech company in Silicon Valley

I worked as a QA specialist in Silicon Valley for a year after graduation, performing software testing across desktop and mobile apps for a Fortune 100 company in Cupertino under an NDA. I gathered data from user experience via a custom bug-tracking software to ensure the quality of the product while collaborating with other testers, producers, and developers. However, I was always eager to get a media-related job even though I loved my job and my amazing colleagues.

#2: Couldn’t afford my rent

San Francisco is as well-known as a city offering the highest salaries.

Image: Bloomberg, May 20, 2019

But the highest salaries are only for the engineers, and the rent is set to the engineers’ salaries.

And seriously, it is even more expensive than rent in New York City.

Image: MarketWatch, May 22, 2016

I couldn’t afford my rent (about $3,500/month for a studio) in the Bay area anymore. At that time, I realized that if I wanted to stay with saving money, I had to find a job related to coding. I also figured out that tech-related jobs increase the chances of staying in the U.S. with high salary.

Also, my company taught me a bit of programming and they suggested me that I learn another programming language such as Python, Java or C. My colleagues who had worked for more than three years were already familiar with at least one programming language. Sometimes, I had to use Terminal when I worked at the company, but it didn’t sound interesting to me at that time.

#3: Still want to work with the media, but as a ‘tech-savvy’ journalist

That’s why I decided to be a data journalist, who analyzes data with background knowledge about media, makes a chart from analyzed data to show insights via media outlets. I recognized that a lot of tech-related jobs in the U.S. still need more people, and media industry needs the same. So, I decided to study data analytics/data science and visualization.

What I Learned From Experts and What I Am Learning Now?

I searched for data scientists’ profiles on LinkedIn to check what skills I had to learn. I sent messages and questions to famous data scientists, and some of them replied to the questions. Also, I could get really helpful information about the field of data science from Medium as well, my favorite one being Adam Thomas’s article about data-journalism. He introduced such a great link ‘datajournalism.com’ in his article.

People also shared their experience or tips to get a data scientist job. From their articles, I realized that my expectation (DS = The World’s sexiest job) does not match reality (the real things I have to learn such as Python, SQL, Machine learning and so on). That’s why I am constantly reading about the field. I wholeheartedly recommend Kelly Peng’s article which helped me to develop a strong mindset because this is REALLY important.

Now I know that I have to learn at least one programming language such as Python, R, SQL and even math such as linear algebra. For visualization, I have to learn Tableau, Pandas or MATLAB/Seaborn. In fact, I am currently learning and using Python, MySQL, Tableau, Pandas and MATLAB. And yes, it has been really hard. But everyone knows that getting started — learning the user interface of the software — is the hardest part. Now, I am getting familiar with using these skills and it’s getting more interesting.

To newbies who want to start a career in data-related field, I want this article is helpful to you to understand this field. I will keep writing about my experience, projects and thoughts about the data-science.