We see these debates ebb and flow almost every week on TDS: should data scientists first master high-level concepts and get fluent in, say, probability theory—or dig right into the (occasionally) messy world of model tuning and data cleaning? In truth, the best posts we read and share blend these two sides of data science seamlessly. This week’s lineup is no exception; we focused on the practical and the hands-on a bit more than we usually do, but don’t worry: our authors always see the forest and the trees. Let’s get to it!
August is supposed to be a slow, lazy month—the dog days of summer and all that—but you wouldn’t know it just by looking at the posts that resonated the most with TDS readers last month. Both aspiring and seasoned data scientists seemed to focus on practicality and efficiency, and wanted to level up their skills—so they found their way to articles that offered actionable ideas and insights. From Python packages and visualization tools to useful practice datasets, let’s dive in to some of our most-read recent posts.
August’s runaway viral success was this comprehensive-yet-concise guide from Sharan Kumar Ravindran, who…
We’re now deep in back-to-[something] season — whether it’s a new school year, a new job, or just a return from the slower pace of summer (for those of us in the Northern Hemisphere, at least). It can be a stressful and hectic time, but also a period for nurturing new skills and thinking about new ideas. We hope it’s the latter for you; we’re here to help with some excellent reads that range from the highly theoretical to the decidedly hands-on. It’s September! Let’s get going.
In the Community Spotlight series, TDS Editors chat with members of the data science community about the exciting initiatives that help push the field forward. Today, we’re thrilled to share Elliot Gunn’s conversation with Madeline Lisaius, Lead Data Scientist, and the Statistics and Machine Learning team at the Rockefeller Foundation, a philanthropic organization founded in 1913 that works to solve global challenges related to health, food, power, and economy mobility.
What does data science look like at the Rockefeller Foundation? What does a typical day look like for the team?
Data has actually been at the core of the Rockefeller…
We are an incredible species: we are extremely curious about the world around us, love to learn, and often find new ways and tools of doing just that. One such advance in the last couple of decades has been computation. By improving the architecture of compute engines, we have gotten better at simulating complex dynamics, enumerating large numbers of potential outcomes, and performing quick calculations over those vast sets of possibilities. All of these events have propelled our understanding of the world around us. Machine learning (ML), in particular, has equipped us with a highly flexible and powerful set of…
There’s a growing awareness among data scientists of the risks we face when we simply plop our data into a model—however powerful —and use the results without truly understanding what produced them. To limit costly (or even harmful) errors and biases, we need to avoid the proverbial black box. Fortunately, TDS authors are extremely good at making sense of complex concepts. Here are some of our favorite recent posts explaining the nuts and bolts of models and algorithms.
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 Haaya Naushan’s conversation with Ben Huberman.
Haaya is a data scientist focused on integrating data science and machine learning with socially conscious research. She currently works with NLP and econometrics for causal inference, and has experience leveraging big data and graph algorithms to study social media.
Prior to data science she studied biology, with the intent of working as a veterinarian. In her free time, she plays competitive…
Early-career data scientists often worry about their coding and math skills, or about whether or not they’ve mastered enough algorithms. As we see time and again, though, the answer to the most complicated questions often lies in combining the right tools with a clear approach. Learning to do that is easier said than done, of course. Here are six recent articles that take on thorny problems and tackle them with curiosity and patience—we hope they inspire you to try something new.
In fast-changing fields like data science and machine learning, adding new skills to your toolkit might sometimes feel overwhelming: how do you choose your next step? Do you focus on something practical and job-related, or expand your horizon with the latest research? Do you explore a brand-new area, or build on an existing interest?
While we can’t answer these questions for you, thanks to our community of authors we can offer options—exciting, diverse, and often unexpected ones. Here are a few we wanted to highlight this week.
In the context of data science, beauty might seem like an odd concept to think about—charts might be clear and easy to digest, databases might be clean and well-maintained, but can they ever be beautiful—or even moving?
Kie Ichikawa’s recent post made us wonder about all kinds of new possibilities for telling powerful human stories with big data. Drawing on centuries of cherry-blossom data in Japan, Kie shows there’s a lot to explore around the ways we shape and present data and how we use its power to connect objective facts to personal and social experiences. …