#66DaysofData Member Spotlight: Rea Kalampaliki

This is the fourth in a weekly series spotlighting different members of the 66DaysofData discord community, an initiative started by data scientist and YouTuber Ken Jee. You can find out more about the initiative here and here. The intent of this series is to celebrate those who have made it through the challenge already (although it never truly ends!); share their stories; learn from their successes and failures; and hopefully inspire anybody else walking this path, wherever you happen to be in your journey. If you’re interested in joining the community, you can do so here.

If you needed one more example of how quickly data science has exploded (and how old I feel), I remember being taught Pascal in my final year of high school. In Rea Kalampaliki’s first year of university, she learned VBA. In Rea’s final year which she just completed, she was exposed to programming in R — and first year students are now being taught Python.

Her data science journey started with using R for her university thesis to analyze protein sequences last spring. After learning that what she had done was within the realm of data science, she took to YouTube and found Ken Jee’s channel. With her thesis and degree coming to an end around the same time as Ken’s first 66 days of data challenge, the stars aligned perfectly for her to jump into this domain with both feet.

While this budding biotechnologist was exposed to R first, it was again by chance that she found herself taking the leap into data science with Python. After having technical difficulties with an online R course she was trying to take, Rea was swayed by the abundance of Python fans she found herself interacting with. This was my first takeaway from our conversation:

The people you surround yourself with can have such a large impact on your decisions.

Yes, the people weren’t the only factor, but choose your company carefully! Rea loves that she got to meet many, many interesting people, from a wide range of skills and backgrounds through the 66 days of data discord server and found them to be very helpful in pushing her along the data science path.

In the first round of the 66 days of data, Rea followed many of Kaggle’s mini-courses, learning basic Python as well as SQL and standard Python libraries like pandas and matplotlib. She greatly recommends these to others starting out on their data science journey. Her ability to understand and follow along with other Kaggle notebooks has been night and day after building up a solid foundation. However, she admits organization was a struggle in the first round of the challenge — and remains a struggle at times. (I empathize and share in her struggles. Data science is a vast domain and sometimes it feels difficult not to get overwhelmed.)

However, while we can’t make perfect progress, we can still make progress and this was one of the many lessons that Rea shared with me.

It’s okay to take baby steps. We don’t have to conquer the world in a month (or a year, or even a decade).

She finds herself with a lot more confidence and more persistence now, thanks to the work she’s put in every single day starting from the challenge onward. After getting a handle on some of the basics, she’s started trying to be more project oriented, by following along with other people’s projects and applying the steps and concepts to her own world of bioinformatics. Yet another valuable lesson:

You don’t have to do everything from scratch. You can learn so much by thinking critically about established work.

After building some foundation in Python, following along with other’s work can be effective because if you engage with their project, it helps you establish an informal learning path and it forces you to do your own research to better understand their methods and the why behind the steps they’ve taken.

And speaking of research, throughout this journey, Rea’s focus has been to become a data scientist in the field of biotechnology. To that end, she may take a not-so-baby step to supplement her self-learning by applying for a master’s degree in data science and bioinformatics soon. With a lot of the intangibles she’s developed through the challenge and beyond, I’m confident she can find success if she decides to take that route. However, in her words:

The 66 days of data challenge (and her data science journey) has been a happy challenge so far.

No matter where she ends up, to me, that’s what success looks like.

You can connect with Rea on LinkedIn. She’s also written an article on Medium about ROC curves! We will hopefully see more writing from her in the future, so stay tuned!

Finally, if you’d like to join a community of data science learners in all stages of the journey, feel free to join the 66DaysofData discord server.