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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…


What can you do to help move the field in the right direction?

Photo by Jordan Madrid on Unsplash

Whether you’re just getting started or are already building your career, you can help data science and machine learning evolve in a way that benefits people and communities. If you are interested, here are a few ideas that could guide your next steps.

1. Learn more

A key element in getting involved and helping the field move in the right direction is to stay up-to-date with the latest developments in data science and machine learning. We’ve created this resource page to support your learning journey.

“Doing good” is a noble goal, but it’s sometimes hard to figure out how to start — some…


Think of a creative baker: the image popping up in your mind is likely that of someone with an endless flow of ideas who then translates them into beautiful (and hopefully tasty) confections. A creative architect? Sure: someone who takes a seemingly boring project and brings it to life, delighting the people who occupy and move through a new space.

What about a data scientist? Does creativity in the field appear in the form of developing new machine learning models? Applying academic research to real-world problems? Running innovative A/B tests? Finding unlikely solutions to thorny problems? In a field that’s…


Author Spotlight

For Vicky Yu, a handful of key skills made the transition from finance to programming to data analytics smoother.

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 share Vicky Yu’s conversation with Ben Huberman.

Vicky has worked as a data engineer, data scientist, and most recently a data analyst. She started writing to discuss her experiences and provide career advice she found lacking during her data journey.

What was your journey into data analytics like?

I went into the data science field by chance. I was a data engineer in a consulting company. One of my projects involved working with the data science team…


No matter where you are in the great universe of data science, you’ve probably set yourself a goal (or a few) for the next couple of months. Here at the Variable, we want to do more than just present our favorite recent posts—we’d like to also help you on your journey. So this week, choose your own adventure. We’re sure you’ll find at least one pick—or, who knows? Seven?—that fit your current needs.


Author Spotlight

Sophia Yang talks about the importance of ongoing learning and finding great colleagues and mentors.

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 share Sophia Yang’s conversation with Ben Huberman.

Photo courtesy of Sophia Yang

Sophia is a Senior Data Scientist at Anaconda, Inc., where she manages key metrics, data pipelines, and models, and uses data science to facilitate decision making for various departments across the company. She is also the author of multiple open-source libraries such as condastats, cranlogs, PyPowerUp, intake-stripe, and intake-salesforce. She holds an M.S. in Statistics and a Ph.D. …


A hand-picked “listening list” on the questions and stakes at the forefront of artificial intelligence research

Photo by Philipp Berndt on Unsplash

Recent advances in AI and machine learning have helped create new tools and products and pushed scientific knowledge forward. They also bring along risks and complexities that we don’t yet fully understand—and these range from the hyperlocal (for example, companies perpetuating bias in their AI-powered hiring processes) to the existential (a general artificial intelligence wiping out life as we know it 😱).

It can be hard to keep up with the state of the field, let alone understand the deep implications of new research. But we’re here to help: the second season of the TDS Podcast, hosted by Jeremie Harris


We sometimes wonder: if we let an AI curate this newsletter, would it land on the same picks as TDS’s 100% human team? Would it rely on views, claps, and social shares, or could it somehow detect an article’s less effable qualities—a writer’s voice, originality, or clarity? Carolina Bento asks a similar question in her superb explanation of Decision Tree algorithms. Using the process of holiday-destination selection as an example, she demonstrates how such a system would work, as well as the limitations it would face.

Photo by Ricardo Gomez Angel on Unsplash

Many experts argue that it’s more important to understand why a model produced a…


MONTHLY EDITION

Picturing the story that your data is trying to tell

Photo by ritesh arya from Pexels

When someone hears the words “data science,” they will often assume the meaning is a job that deals with analyzing data. Though that might be true, I like to think of data science as a storytelling field. When a data scientist is given a dataset to analyze, they often try to find patterns, trends, and anomalies within that dataset and use that information to make business decisions or predict future data. …

TDS Editors

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