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"I refused to accept my negative thoughts."

Author Spotlight

In the Author Spotlight series, TDS Editors chat with members of our community about their career path in Data Science, their writing, and their sources of inspiration. Today, we’re thrilled to present our conversation with Khuyen Tran.

Khuyen is a data consultant at Ocelot Consulting. She has written over 100 data science articles on Medium, and more than 270 daily data science tips at Data Science Simplified, attracting a wide audience of learners and professionals. Her current mission is to make open source projects more accessible to the data science community.


Let’s start at the very beginning – how did you decide to become a data scientist?

Two years ago, I wanted to find out what I could do with my math degree. I was interested in neither being a professor nor going into finance. Luckily, when attending a math conference, a speaker who used to be a mathematician shared how rewarding his current career as a data engineer was. That is when I discovered data science.

Since then, I took the time to learn and share new data science knowledge every day no matter how busy I was. Thanks to the articles I share, I have been able to land multiple jobs in the data science field, creating a positive feedback loop extending my knowledge deeper into data science.

How challenging was it to branch out into an entirely different field?

At the beginning, I had difficulty retaining new knowledge obtained from data science courses. For example, I often forgot the syntax and theoretical explanation behind a machine learning algorithm after taking a machine learning course for three months. I felt like maybe coding and data science were not for me.

However, I refused to accept my negative thoughts. Maybe my learning approach just wasn’t right. I asked myself what learning techniques made learning enjoyable and helped me retain the information the most. The answer was to learn by doing projects and teaching. I found that I retained more new knowledge when I tried to use it to solve a problem or taught somebody else what I learned.

Now, whenever I want to gain a deeper understanding about a data science concept or tool, I write about that topic.

Based on your own experience, what advice would you give to people who are just starting out in data science?

I recommend that data scientists learn by doing and share what they learn. Instead of taking one data science course after another, you should only learn the minimum and build upon it by using your knowledge to solve the problems around you.

The problems you tackle should be interesting but not too difficult. Then you can start sharing your projects by writing articles about them or share them on LinkedIn. Gradually, your skills will be recognized and offers will come to you before you even have the chance to apply for them.

What benefits do you see in public writing? And how do you decide what to write about?

I believe we all have something valuable to share no matter how simple that idea is. I love open-source tools, but some of the tools are not well-known because they are hidden in GitHub. So I want to be a medium that helps spread these open-source tools to those who are not familiar with GitHub. I frequently published on Towards Data Science because it helped me spread my articles to a much broader audience.

I also have a lot of fun writing articles. I often understand a tool much better after writing an article about it. Plus, I got a lot of new offers related to data science thanks to the articles I wrote.

What are the kinds of jobs or projects that motivate you the most these days?

I’m motivated by data science projects that answer interesting questions and require a combination of skills and tools. I’m especially interested in building data science web applications using Python since they are fun to build and can be used by others.

Looking ahead, what are your hopes for the data science community? In what direction do you want to see it move?

In the next couple of years, I hope AI ethics will be the focus among many data science practitioners. Even though AI can have a really strong positive impact on society, many models being used today are unregulated and incontestable even when they’re wrong. As a result, these models can reinforce discrimination and bias. Data science practitioners should be aware of these negative effects and build a model with ethics in mind.

For those who want to learn more about how AI can negatively affect our society, I recommend checking out the book Weapons of Math Destruction by Cathy O’Neil.


Curious to learn more about Khuyen’s work and data science interests? You’ll find her writing on her Medium profile, on her site, Data Science Simplified, as well as on her Twitter account. Here are some of our recent favorites.

  • [3 Tools to Track and Visualize the Execution of your Python Code](http://Visualize the Execution of your Python Code) (TDS, April 2021) As readers of Khuyen’s posts know, her writing always centers practical, real-world advice. In her most recent viral hit, she explains the benefits of three tools that help streamline your work in Python.

  • Stop Using Print to Debug in Python. Use Icecream Instead (TDS, January 2021) Another Python-focused post, this one highlights the benefits of using the Icrecream library for faster, smoother debugging.

  • How to Create Mathematical Animations like 3Blue1Brown Using Python (TDS, April 2021) Everybody loves a sleek animation, but not everyone has the time or energy to learn the necessary skills. Here, Khuyen introduces Manim, a Python package that empowers data scientists to create impressive visuals with minimal heavy-lifting.

  • Build an Impressive Github Profile in 3 Steps As a consultant who transitioned to data science from a different field, Khuyen recognizes the need to present one’s skills and experience effectively. This tutorial walks readers through the (short) process of building a professional, focused GitHub profile.

Stay tuned for our next featured author, coming soon. If you have suggestions for people you’d like to see in this space, drop us a note in the comments!


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