Is machine learning the actual focus of data scientists’ everyday work? Do you need to learn all the things to be a data scientist? And, most importantly: Do data scientists have a sense of humor?
On the Data Science Mixer podcast, I always ask our expert guests the same question in our “Alternative Hypothesis” segment: “What’s one thing that people think is true about data science or about being a data scientist that you have found to be incorrect?” (Be sure to check out the first roundup of debunked myths, too.)
Amazingly, we always get a fresh response. It seems…
Alan Jacobson, Chief Data and Analytics Officer at Alteryx, joined us on the Data Science Mixer podcast to talk about his work leading teams of data scientists who themselves build tools that are used by other data scientists, including the Alteryx platform and the open-source Python libraries EvalML, Featuretools, Woodwork and Compose.
Alan shared with us what makes a solid data science team, how he thinks about model interpretability, and how to communicate clearly about data science. Here are three “principal components” of our conversation that will get you thinking about these big issues in the field.
Some of the…
Scary red eyes. Overexposures. Blurry pets. My childhood photos are full of these photographic flaws; adjusting photos on the spot wasn’t something the average person did back then. But now we all edit photos routinely — and, even more impressively, we can do it at scale, preparing many images quickly so we can extract useful information from them.
The Computer Vision tool palette in the Alteryx Intelligence Suite now unites the tool previously known as PDF Input with an array of tools that make image-based data even more accessible. …
Kristen Werner, director of data science and engineering at Snowflake, joined us on the Data Science Mixer podcast to talk about her work on data automation and developing tools to streamline the daily tasks of data science. Our conversation revealed her fascinating background in neuroscience, her methodical approach to problem solving, and her interest in developing mechanisms that support data consistency and access.
Here are three “principal components” of what Kristen shared, including her enthusiasm for automating common processes for the data scientist.
Automation, simplification and commodification of common processes became super, super appealing to me.
You see a lot…
Renee Teate has informed, guided and encouraged many aspiring data scientists through publicly sharing her own career journey into data science. She has hosted a podcast, Becoming a Data Scientist; has blogged about her learning and job search; and tweets often about this career shift. Renee recently joined us for a special video episode of the Data Science Mixer podcast, aired at the virtual Alteryx Inspire conference and also available in our podcast feed.
Here are three “principal components” of what Renee shared that will motivate everyone working in data science to continue their learning and career growth.
Alex Engler, research fellow at the Brookings Institution and civic data scientist, joined us on the Data Science Mixer podcast to discuss issues of policy that affect the daily work of data scientists. His encyclopedic knowledge of this topic and his experience in using data for the public good made this a wide-ranging yet deep conversation.
Here are three “principal components” of what Alex shared, including his perspective on how data scientists should think about policy issues and ethics in the context of their day-to-day work.
Illinois clearly is moving forward with prosecuting illegal uses of biometric data through its…
You know how on TV crime shows, it seems magically possible to extract clear, easy-to-read information from even super-fuzzy images or video? We’re getting ever closer to that level of understanding for even difficult forms of data. This week on the Data Science Mixer podcast, we talked with Trevor Jones, vice president of business operations, and Robbie Booth, senior director of AI cognitive engines, at Veritone, whose aiWARE offers “layered cognition” for processing audio, image, and video data.
Here are three “principal components” of our conversation that offer new ways to think about and analyze your unstructured data.
On the Data Science Mixer podcast, I always ask our expert guests the same question for our “Alternative Hypothesis” segment:
“What’s one thing that people think is true about data science or about being a data scientist that you have found to be incorrect?”
Guests’ responses to this question have been diverse and fascinating. They’re excited to do a little data-science myth-busting, and they offer thoughtful reasons based on experience for pushing back against conventional wisdom.
Margot Gerritsen is a Stanford professor and a co-founder of the international organization Women in Data Science. She joined us recently for an interview on the Data Science Mixer podcast (and, of course, she also hosts the WiDS podcast!). We talked about her incredibly wide-ranging experiences in data science, from studying fluid dynamics to designing sailboats to building a scale model of a pterosaur for a National Geographic documentary.
Margot’s full episode is well worth a listen for all the inspiration and advice she offers. Here are three “principal components” of what she shared. …
John K. Thompson, bestselling author and the global head of advanced analytics and artificial intelligence at CSL Behring, joined us recently for an interview on the Data Science Mixer podcast. We talked about his deep experience building data teams, which he documented in his thoughtful, practical book Building Analytics Teams, published last year.
While you should definitely listen to the full episode to hear more of John’s story and ideas, here are three “principal components” of what he shared. They’re useful strategies and insights for anyone involved in data analytics and data science.
John: If they come to your office…
Sr. Data Science Journalist, Alteryx. Host, Data Science Mixer podcast. Data geek. Former journalism professor and researcher. Writer, knitter, hiker. she/her