WiDA Speaker Series: Building a Career in Data Science with Olumide.

Hello, Data Nerd.

This is the WiDA Speaker Series, a segment of the Women in Data Africa Newsletter. Biweekly, WiDA interviews data professionals to get insight into what life is like for them, their onbecoming career story/ journey, insight on the “how” of building a career in tech, the careers they are building, challenges, ideologies, and more.

These series are prerecorded conversations from our Twitter Spaces, Virtual Community Meet-ups, interview features, and more.

Today, we will be hearing from Olumide, a Data Scientist with experience working in the Fintech and Banking sectors and a background in credit risk modelling, machine learning and statistical modelling.

It is a great conversation, share, like, and leave comments if it resonates with you.

Hello, Olumide. Nice to meet you. We like to kick off our session with an icebreaker. Can you share two random facts about yourself people generally do not know?

Haha, the first random fact about me is that I have always wanted to be a tailor. I did learn how to sew at a point; I highly doubt anyone knows this about me. I decided to learn because my mother’s tailor traumatized me when I was younger. The other random fact will be my worst fear. Which is falling in public. This is because I fell in public one time ago and I never want to ever experience that again.

I am so sorry about you falling. Did you notice that your two random facts are from PTSD?

Haha, yeah that is true.

The second icebreaker question I have for you is when you are not working what will we find you doing?

You’d find me probably talking to people or observing them. I love psychology and sociology.

Your Bio is one of the most interesting Bios I have had to read. Why did you decide to pursue a career in Data Science?

I think I would like to take us back to my uni days. In university, I studied Computer Science but when I got into my third year I had this unease within me that was a result of me not knowing what I wanted to do career-wise. One day I was surfing the web and came across the course “University of Stanford - CSC22 - or so”. I played a couple of the videos that had to do with “Data Analytics”, “Machine Learning”, and “Data Science” Of course then I knew close to nothing about the data field but found the course very interesting. I didn’t do anything about it until my final year when I decided to implement a Machine Learning topic to implement for my final year project. Curiosity got me into the field and made me build an interest in the industry.

What was your journey like? What did you learn and what are you currently learning?

I started out as self-taught. When I started the buzz on data was very minimal in the social space. People were aware that it existed but we couldn’t fully comprehend the importance and impact of data. I started with working on projects every week, using this as an opportunity to learn data science. Once I covered my basis with projects I started taking gigs in data analytics, went fully into it, and played my hand, a couple of iterations down the line I finally got an offer. When I got into my first role which was in the banking industry I realized that the foundation of data science is consistent across industries what is different is understanding and knowing how to apply my knowledge in a specific industry which means going the extra mile to understand what industry I was working in. I started learning with Excel - learnt basic operations in Excel (it will be good to note that because of my degree in Tech, I already understood Python.), I studied Python for DS and learnt SQL. If I were to advise someone on a learning path they should go with I’d say start with Excel, SQL, and then learn Python. I am currently learning a bit about Data Engineering.

What would you do differently if you were to start again?

If I was to start again, I think I’d have read more books. I’d have read more technical books! Lastly, I’d also have blocked all the noise/ buzz that eventually came on on social media.

Listen to the conversation here: Twitter Space

Thank you so much for sharing. So far, can you share what you love about your field?

Specifically, I love the impact on business because we can see the impact firsthand. As a data scientist, we spend so much time building models, and dashboards, analyzing datasets and more being. So, witnessing how this translates into decisions for a business is incredibly fulfilling I think that is what I love the most about my field.

Thank you for sharing. A beginner asked me this recently: “As a beginner do I need to become a Data Analyst first before becoming a Data Scientist”?

Personally, I do not believe that you need to become a data analyst first to become a data scientist. I understand that as a data scientist from time to time, you will be required to wear the hat of a data analyst which means the knowledge of analysis is important to have skill as a data scientist. It is important to be able to identify the intersectionality of the skills for both fields that are extremely important and make sure you have them.

In your opinion, what are the common mistakes you see beginners make?

One of the mistakes I see beginners make is not having an understanding of the solution they are trying to deploy or create. Another mistake I see beginners making is neglecting the very necessary fundamentals in programming, statistics and mathematics (to some extent). I’d say the last mistake I see is beginners not communicating results effectively.

Stop gathering certifications without projects, and build a solid portfolio!!!

Thank you for pointing these out, I’d like us to talk about certifications. I know you have a degree in CS and other certifications. Would you say during applications your degree and certifications combined got you into the door? Or, it was just your skillsets and projects you worked on.

These days, I understand that the course you study in uni will not limit you to getting a job as a Data Scientist. I’d say yes my course and certifications did give me an edge but I’d say that ultimately the most important was my portfolio and the projects I had worked on. You need to get your hands dirty by working on projects; during my interviews, the conversations I remember having were not about my degree and certifications but about my portfolio and projects.

Are certifications important? What certifications would you recommend a data scientist have to be competitive?

There are a handful of standard certifications you can take from the likes of Microsoft, Coursera and more. You can always check this online; what I’d say is if you do decide to take certifications try as much as possible to have a solid portfolio. Once you have a solid portfolio people tend to not reference your degree or certifications.

Thank you so much Olumide for speaking with us. This was an amazing session!

If you’d love to listen to the entire conversation (recording), kindly click on the link below. Share, like, and leave comments if it resonate with you: Listen to the Twitter Space!

Connect with Olumide on Twitter, and LinkedIn.

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