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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.

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

As people who work with code on a daily basis, it’s perhaps inevitable that data scientists sometimes default to binary thinking. Ones and zeros. Signal and noise. Statistical significance—or its absence.

As Jessica Dai writes in a recent post on algorithms and fairness, we don’t have to stick to this either/or framework in every conversation, especially when what’s on the line is building models that don’t perpetuate bias. Looking at the full development lifecycle, Jessica points to potential points of intervention where data scientists can act as guardrails against bias, but without sacrificing accuracy. …


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

We’re feeling festive this week—Towards Data Science recently hit a major milestone when we waved an excited “Hello!” at our 600,000th follower on Medium.

There are many ways to measure the success of a publication, but none of them matter if your audience doesn’t show up. So, to everyone reading the Variable: thank you for showing up, keeping our community supportive and welcoming, and helping us spread the word. If you’d like to support us and our contributors in more direct ways, consider becoming a Medium member. …


Author Spotlight

On finding your niche, how writing is both a multiplayer and single-player game, and why we all need to think more about ML systems

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 Elliot Gunn in conversation with Eugene Yan.

Photo courtesy of Eugene Yan

Eugene Yan designs, builds, and operates machine learning systems that serve customers at scale. He’s currently an Applied Scientist at Amazon. Previously, he led the data science teams at Lazada and uCare.ai. He writes and speaks about data science, data/ML systems, and career growth at eugeneyan.com and tweets at @eugeneyan.

You took a rather interesting career path into data science. From breaking into the field with a psychology degree, to landing a “rocketship” role at Lazada, and now as an Applied Scientist at Amazon. Could you share a bit about how you found your way into the field?

I don’t recall starting with data science as a career goal…


MONTHLY EDITION

Moving beyond tutorials, courses, and side projects

Photo by olia danilevich from Pexels

Courses, textbooks, blog posts, and personal projects are great ways to learn to do data science. But it’s also widely acknowledged that these aren’t enough to actually start working in a data science role. Vicki Boykis, a senior machine learning engineer at Automattic, identified this as a problem of implicit versus explicit knowledge. Explicit knowledge is what we have easy access to, as it’s written down somewhere for us to learn. Implicit knowledge is what we call learning on the job–it resists being packaged into a textbook or article. …


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

What do you do when things aren’t going the way you were hoping they would? Whether it’s a machine learning model that required too much tinkering, a job offer that never materialized, or just a passing “wait, it’s almost June?!” moment of terror, we all face a setback (or worse) every once in a while. We found inspiration in our recent conversation with data scientist and TDS author Carolina Bento, whose pragmatic approach to problem-solving might resonate with you, too: “Sometimes, when I get stuck, I think about how I can approach the problem from a different angle.”

Photo by Bas van den Eijkhof on Unsplash

Carolina’s advice…


Author Spotlight

Not sure how to move beyond a thorny data science problem? Break it down to smaller pieces—and ask for a peer’s perspective.

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 Carolina Bento.

Photo courtesy of Carolina Bento

Carolina is a data scientist with a special interest in data visualization. Before dropping out of a PhD program where she focused on data analysis and data visualization in large-scale networks, she completed a Master’s degree in Computer Science. She’s been working on data science and analytics ever since.

When she’s not writing about the fundamentals of data science and data visualization, you…


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

The amount of data we can collect has grown exponentially in recent years, and so has the computing power we have at our disposal to analyze it. Yet data scientists still have to make tough decisions every day around using this newfound power. That is true in business, in tech, and in medicine—and in more or less every other field of human activity.

Companies both big and small, for example, have been investing money and resources to ensure their business strategy aligns with underlying data. As Scott Lundberg shows, however, we still have to be extremely careful not to fall…


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 Elliot Gunn in 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…

TDS Editors

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