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This week we are interviewing Anita Tee from Fact Vs Fitness. Anita is a personalized nutritionist, studied her Masters in Personalized Nutrition & her Bachelors in Genetic & Molecular Biology. She is also a personal trainer with the Canadian Register of Exercise & Fitness Professionals.

You can find her at www.factvsfitness.com
Twitter @factvsfitness

You can find Thrival Nutrition at www.thrivalnutrition.com
Facebook - Thrival Nutrition
Twitter - @thrival_orlando
Instagram - thrivalnutrition

Grab your free copy of the Top Ten Superfoods at www.thrivalnutrition.com/superfoods

Episode Seven - Prevention of Metabolic Disorders, Type 2 Diabetes, Heart Disease, and Cancer

Part 3 of "Overcoming Depression" an 8-part series distilling the best advice from thousands of experts into an easy to follow, step-by-step audio program. This section covers Nutrition, Exercise and Sleep routines for taking control of your Depression through Biology.

Overcoming Depression Part 3: Getting a Biological Advantage

In this session of the Super Data Science Podcast, I chat with Ruben Kogel who has done some amazing data analytics in the startup space.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/1

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 001 : Self-serve Analytics, Data Science MBA and Managing a Team of Analysts with Ruben Kogel

We're excited to welcome a guest, Tim Head, who is one of the maintainers of the scikit-optimize package.  With all the talk about optimization lately, it felt appropriate to get in a few words with someone who's out there making it happen for python.  

Relevant links:
https://scikit-optimize.github.io/
http://www.wildtreetech.com/

Scikit + Optimization = Scikit-Optimize

If you work in tech, software or data science, there's an excellent chance you use tools that are built upon open source software.  This is software that's built and distributed not for a profit, but because everyone benefits when we work together and share tools.  Tim Head of scikit-optimize chats with us further about what it's like to maintain an open source library, how to get involved in open source, and why people like him need people like you to make it all work.

Open Source Software for Data Science

https://www.miamiobgyns.com/blog/how-ph-levels-affect-you/

It seems these days we’re constantly searching for ways to live a well-balanced life. From eating a balanced diet, to finding a healthy work-life balance, to getting the right balance of sleep, good nutrition and exercise, “balance” is today’s hot-trend (that will hopefully have lasting power).

And then of course, there’s the biology of it. The billions of cells in our bodies that must maintain a healthy pH balance in order for our bodies to function at an optimal level.

pH Balance: The ABC’s of Understanding How pH Levels Affect You

In this episode of the SuperDataScience Podcast, I chat with Head of Data Jaco Van Der Berg. You will talk about many different topics in the space of data such as data strategy, data security, data flow within an organisation and the single point of truth. You will also get to learn about managing and hiring for a data science team and will be able to gain lots of knowledge all about the structure of a data team. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/55

SDS 055: Building and Managing a Successful Data Science Team

As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level.  Most of our episodes are about the cool work being done by other people, but this one summarizes some thinking Katie's been doing herself around how to guide data science teams toward more mature, effective practices.  We'll go through five key characteristics of great data science teams, which we collectively refer to as "disciplined data science," and why they matter.

Disciplined Data Science

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2

The Data Science Open Source Ecosystem

This is a re-release of an episode that originally ran in October 2019.

If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Procella: YouTube's super-system for analytics data storage

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

A Data Science Take on Open Policing Data

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.

A Reality Check on AI-Driven Medical Assistants

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night!

—Katie and Ben