Finding Data in a Haystack

When we think about data and data analysis, images of huge data sets come to mind. We’re immediately drawn to data sets in health care, at large tech firms, at Fortune 500 companies across the globe.

But when we say data is everywhere, we mean it—even at publishing companies.

For Professional Program in Data Analysis graduate Chloe Medosch, her introduction to working, synthesizing and analyzing data came when she moved into a manager role at PLOS ONE, the interdisciplinary journal from the Public Library of Science, an open-access scientific research journal. 

Focused on the operations of moving papers through the peer-review process to eventual publication—and we’re talking hundreds of papers at any given time—Chloe was keyed-in on the various moving parts to do this work.

“I found that there were a lot of data points around how long each part of the process takes and where we were spending a lot of time and how we could make the process more efficient,” she tells me during a recent Zoom chat. “So I started looking at those various data points: How many papers are getting through? How long does it take to get revisions? How can we go faster? What changes can we look into to make the process better for both authors and reviewers? How do we find the ones that are stuck in the process? How do we use the data to make the process better?”

I really wanted to beef up my skills in terms of how I manipulated the data.

You were looking at the data for optimization purposes. 

Exactly. I was starting to work with a lot of data, which was really interesting to me. I have a large data set with various dates that we're hitting and I’m trying to understand how to use the data to investigate where there could be optimizations.

I really like the investigative aspect of working with data, when you start asking, “Why did this happen? Why is there a spike in longer turnaround times in September? Is it because our reviewers are back to teaching classes?” That’s an easy example, but thinking about what we see and what could explain it.

I was also really interested in learning how to talk about the data. When I’m in a meeting with editors of the journal, I have to explain why some of the papers are taking longer in the process and illustrate that with visuals.

Also, I was working with Excel data all of the time, and I really wanted to beef up my skills in terms of how I manipulated the data. I started to use data visualization tools such as Tableau and Sisense. I wanted to understand how the data is structured behind the scenes.

My classmates were going through a similar professional journey as myself: We were all interested in this subject and wanted to gain this skill set no matter what our career path was.

This is when you started our data analysis program?

I was already familiar with Berkeley from my undergrad—I received my bachelor’s in English and Spanish—so when I started looking online for data analysis programs, I found yours and thought, “This is exactly what I’m interested in.” The journal also had an education stipend so I was able to use that to help pay for the courses.

I really enjoyed the Introduction to SQL course with instructor Michael Kremer because it covered the basic coding in order to get data from a database. It was satisfying to test out my code and see if it gave me the correct information back. It was a lot of hands-on testing. It was a really good start to the program and I always received quick help from Michael when I reached out and had questions.

My classmates were going through a similar professional journey as myself: We were all interested in this subject and wanted to gain this skill set no matter what our career path was. There were students who wanted to move into a data analyst position, but also those—myself included—who wanted to make my current skill set stronger in what I do next.

So you were working while taking these courses. Did you transfer skills from class directly to the job?

Absolutely. In fact, I still use SQL in my job today, and I think it's been a boon for me to have that skill set.

I recently pivoted into cybersecurity. I now work for Cloudflare, an Internet infrastructure cybersecurity company, focused on our public-sector customers. I'm on the customer success team, and my job is to help customers fully use the solutions that they've purchased, adopt them properly and then see value.

I still use SQL in my job today, and I think it's been a boon for me to have that skill set.

How are you using your new data skills?

Moving into a tech company, there's a lot of data! The scale got much, much larger. But even though moving into the tech sector was quite a jump, I still have to use data to understand how customers are utilizing our services.

I still use SQL in order to pull the data and understand it, and then it's my job to meet with customers regularly and do presentations to show them how they’re currently using the solutions, my recommendations and essentially plotting out a roadmap to get them where they want to be in their next stages of adoption.

There are a lot of roles out there that can utilize the information from these courses. 

What does earning the certificate mean for you, both personally and professionally?

It was a really helpful milestone in my professional journey. Earning the certificate made me confident to move into this totally new role for myself in a completely new industry. I have the experience that I've brought from my previous role, and the data analysis and visualization skills tell a story about why I was ready for this new different role.

There are a lot of roles out there that can utilize the information from these courses. For me, I wanted to combine my writing background with my interest in data—and to find work that combines elements of each.

What advice would you give to someone who's just starting the program? 

Give each course adequate time to take it seriously. A lot of us were taking courses and working full time, and it’s a lot of extra work but it is worth it if you put that time in, even if it means a few extra late nights.

Also, make connections with the people in your class! They have interesting experiences, have different perspectives on how they use data in their own roles and could just be great study partners.