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LAST UPDATED APRIL 2021

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About Us | Our Team 📸 — by Leone Venter

Hey there!

If your goals are to…

  • Explore and start learning about data science, machine learning, programming, or artificial intelligence
  • Switch careers to become a data scientist or machine learning engineer
  • Stay up to date with the most important developments in the field
  • See what other data scientists are working on and discussing

…then Towards Data Science is the right place for you. We’re a leading destination for anyone who wants to read about data science and machine learning, share insights, and find a supportive community of both learners and pros.

Before we get started, sign up for our newsletter…


The Variable

Our weekly selection of must-read Editors’ Picks and original features

The evergreen popularity of careers in data science is the result of many factors, from shifts in the labor market to advances in cloud computing. It also hinges, though, on a fundamental idea: smart and passionate people look for work that feels meaningful. And meaningful work, by definition, answers important questions and solves real-world problems.

Bias in hiring is one such problem, and Grégoire Martinon was interested in examining AI’s role in perpetuating it—and, hopefully, its potential to end it. The result is a thought-provoking article that shows just how tricky it is to isolate the causes of bias, let…


Author Spotlight

On working in public, chasing intrinsic motivation, and taking risks in writing

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 Mark Saroufim.

Photo courtesy of Mark Saroufim

Mark is an ML Partner Engineer on the PyTorch team. In his past lives, Mark has worked as an ML engineer and Product Manager at Graphcore, his own company yuri.ai, and Microsoft. Mark is optimistic about a future where people forge their own education and companies.

You took a rather unconventional career path in ML. From leaving your job at Microsoft to start your own game development AI company, to working at Graphcore, and now as a PyTorch Engineer at Facebook. Could you share a bit about how you found your way into the field, and through these roles?

Haha, I get asked this question a lot. So I really started to go deep in machine…


THE VARIABLE

Our weekly selection of must-read Editors’ Picks and original features

It’s always energizing to chat with members of the TDS community, but it’s especially so in one specific way: realizing just how wide-ranging people’s learning habits and styles are. It makes sense, of course. People come to data science from diverse professional, academic, and cultural backgrounds, and what works for some might not do the job for others.

In this week’s Author Spotlight, for example, prolific contributor Khuyen Tran shared her tried-and-true method for digesting complex topics: teaching them. It’s only when Khuyen translates concepts into something others can understand and use themselves that they click for her, too.


Author Spotlight

“I refused to accept my negative thoughts.”

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.

Photo courtesy of 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…


MONTHLY EDITION

As machine learning models penetrate almost every area of knowledge, actually understanding what ML systems do starts to seem problematic.

Modern Dancer (Marta Reguera) by Carlos Ojeda, local artist and friend of Carlos Mougan. Posted with permission.

Many papers, blogs, and software tools present explainability and interpretability in a quasi-mathematical way, but… is there a canonical definition of what interpretability and explainability mean? Or even of how we evaluate explanations?

Machine learning algorithms definitely can’t be left to themselves, running in the wild. The question is, how can we, as human beings, understand algorithms that surpass human performance?

So, for this Monthly Edition, we decided to highlight some of the best blogs and podcasts that TDS authors want you to know about. Whether you’re a data scientist in industry, a researcher in academia, a student, or just…


For a very long time, artificial intelligence was a thought experiment—a sci-fi trope. In what feels like the blink of an eye, it’s now everywhere in our daily lives. How did we get here?

A good place to start is Mike Ferguson’s new series on artificial general intelligence, where he patiently walks us through decades of philosophy, neuroscience, and adjacent disciplines that have theorized about humans’ potential to create AGI. (When you’re done with the intro, you can already continue to parts two and three.)

Still in the mood for some high-level, foundational reading? Follow Mike’s post with Gabriel de…


This edition of the Variable comes to you loaded, as always, with some of the best TDS reads of the past week. Before we get to them, though, we wanted to share a new resource we’ve just recently launched—our free, on-demand, email-based beginners’ guide.

If you’re currently taking your first few steps in data science, you can sign up to receive a daily dose of beginner-friendly articles and practical tips. (It’s a two-week curriculum, and you can unsubscribe at any time.) We hope you enjoy it.

Photo by Calum MacAulay on Unsplash

Alright, reading recommendations! Let’s start with Joseph Rocca and Baptiste Rocca’s deep dive into…


Author Spotlight

Is it time for data science to be “broken up?”

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 Kurtis Pykes.

Photo courtesy of Kurtis Pykes

Kurtis is an ex-postman-turned-blogger and a data scientist with a keen interest in Natural Language Processing (NLP). He currently works as a freelancer on Upwork, covering projects ranging from technical writing to various NLP tasks. Kurtis is a prolific TDS contributor, and is passionate about harnessing the power of machine learning and data science to help people become more productive and effective.

What made you consider data science as a career?

I…


“Story” is a word that sometimes feels overused—including in the context of data science. Not every slide deck with a clear structure and useful takeaways is a story, and that’s ok. But as Marie Lefevre argues in her post about compelling data storytelling, there are time-tested ways to make any analysis memorable and engaging, so why settle for dry and perfunctory?

Photo by amandazi photography on Unsplash

TDS authors certainly don’t settle for dry and perfunctory; we’re frequently amazed by the range of narratives and voices we get to share. It takes great skill and effort to breathe life into topics that at first glance appear…

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