Video, audio, images and text can all be rich sources of insights, but they can also be challenging to analyze. New tools offer easier access to the information within them.

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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.

Recordings of interactions provide multiple layers of potential insights for rich analysis.

Trevor: One…


Alternative Hypotheses from Experts on the Data Science Mixer Podcast

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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.

Here’s a sampling of responses from some of our recent episodes, with more to come. …


The Stanford professor and co-founder of Women in Data Science tells us about her experiences in the data science field, including how to deal with uncertainty and how to find common ground across data projects.

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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. …


The bestselling author shares his top tips for building data teams and managing data scientists effectively.

Hands on a railing with orange lights or sparks in the foreground
Hands on a railing with orange lights or sparks in the foreground
Christopher Burns on Unsplash

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.

Encourage people to bring fresh project ideas to the data science team — before they can overthink them.

John: If they come to your office…


Identifying social media influencers is both an art and a science. Let’s use network analysis to help find them on Twitter.

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My dog loves napping in his super-fuzzy dog bed. And I have to confess: I like to think I’m a rational consumer, but I bought him the bed because of cute photos and a discount code shared by a social media influencer.

Identifying social media influencers who can help promote your business is both an art and a science. There are plenty of commercial services that say they can tell you who those people are. But why pay for that service when you can use a tool already at your fingertips to find and analyze potential influencers and their posts…


Getting Started

Who is P. Value, really? It’s the most popular celebrity calculation of our time — and maybe the most misunderstood.

Image by Jake Blucker on Unsplash

The mystery and influence of P. Value (also known as the p-value) have made it the most popular celebrity calculation of our time — and maybe also the most misunderstood. Despite “significant” starring roles in thousands of data analyses, many still find P. Value mystifying, or even misleading.

So who is P. Value, really? Our interviewer sat down with P. Value for an exclusive Q&A to hear its origin story and find out why its success isn’t just due to chance. And, yes, we asked the tough questions: You’ll learn the truth behind that “p-hacking” controversy you’ve heard about.

Read…


Is your data ready for machine learning? Check its health, and learn how to deal with outliers you may find

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At the doctor’s office, you and the medical assistant go through a familiar routine before the doctor arrives. They’ll check your vital signs — pulse, blood pressure, respiration rate and more — and collect some general information. Those steps summarize some important aspects of your health, letting the doctor jump right into more complex analysis.

The Data Health Tool, now included in the Alteryx Intelligence Suite, does something similar for your data. It gives you a quick but thorough assessment of a dataset’s readiness for further analysis, especially prior to predictive analytics and machine learning. …


Use your data science skills to make a delicious beverage

Photo by Sara Cervera on Unsplash

Ready for a refresher on your pandas skills — or just ready for a refreshing drink?

Pandas, the widely used Python library for data analysis and data wrangling, has an incredible variety of useful functions. If you’re new to pandas or just want to practice, this Data-Driven Cocktail Challenge will help you gain familiarity with indexing, utility functions, string functions, and more.

Be sure to refer to the pandas documentation for help if you need it, and, of course, Google and Stack Overflow are your friends, too.

If you want to be extra Pythonic, try to solve each step in…


Everyday examples have made “artificial intelligence” and “machine learning” familiar concepts not just to data experts, but also to the general public. But how familiar? And to whom?

Blog teaser photo by Jorge Flores on Unsplash

We have a special audio treat this week: a bonus episode of the Data [in the] Sandbox podcast miniseries! We explain what “artificial intelligence” and “machine learning” mean in a way kids can understand, using everyday examples like TV show recommendations, robot vacuums and math homework.

Those everyday examples have made “artificial intelligence” and “machine learning” familiar concepts not just to data experts, but also to the general public. But how familiar? And to whom?

Google Trends data can show us with a bit more precision how popular these terms are, how their use has changed over time, and even…


Let’s use data science to find out where danger lurks among the sparkly lights and shiny ornaments. One dangerous item is something you probably use year-round.

Photo by Chad Madden on Unsplash

When I think of dangerous Christmas decorations, I always think of scenes like this one from “National Lampoon’s Christmas Vacation”:

And yet the most dangerous part of holiday decorating doesn’t involve electricity.

I downloaded and analyzed the U.S. Consumer Product Safety Commission’s latest 10 years of data on injuries involving Christmas-related products. Applying a bit of data science, we’ll find where danger might lurk among the sparkly lights and shiny ornaments. You might be surprised: One dangerous item is something you probably use year-round.

Unwrapping the Data

There are 3,917 injuries related to Christmas products in the dataset from the CPSC’s National…

Susan Currie Sivek, Ph.D.

Data Science Journalist, Alteryx. Host, Data Science Mixer podcast. Data geek and former journalism professor and researcher. Writer, knitter, hiker. she/her

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