DataFramed

This week, Hugo will be speaking with Skipper Seabold about the current and looming credibility crisis in data science. Skipper is Director of Data Science at Civis Analytics, a data science technology and solutions company, and also the creator of the statsmodels package for statistical modeling and computing in python. Skipper is also a data scientist with a beard bigger than Hugo's.

They’re going to be talking about how data science is facing a credibility crisis that is manifesting itself in different ways in different industries, how and why expectations aren’t met and many stakeholders are disillusioned. You’ll see that if the crisis isn’t prevented, the data science labor market may cease to be a seller’s market and we’ll have big missed opportunities. But this isn’t an episode of Black Mirror so they’ll also discuss how to avoid the crisis, taking detours through the role of randomized control trials in data science, the rise of methods borrowed from econometrics and how to set realistic expectations around what data science can and can’t do.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on DataFramed?)

FROM THE INTERVIEW

• Skipper on Twitter
• Skipper on Github
• What's the Science in Data Science? (Video by Skipper Seabold)
• The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics (By Joshua D. Angrist & Jörn-Steffen Pischke, American Economic Association)
• Project Management for the Unofficial Project Manager: A FranklinCovey Title (By Kory Kogon)
• Courtyard by Marriott Designing a Hotel Facility with Consumer-Based Marketing Models (Jerry Wind et al., The Institute of Management Sciences)
• Statsmodels's Documentation

FROM THE SEGMENTS

Guidelines for A/B Testing (with Emily Robinson ~15:48 & ~35:20)

• Guidelines for A/B Testing (By Emily Robinson)
• 10 Guidelines for A/B Testing Slides (By Emily Robinson)

Original music and sounds by The Sticks.

This week, Hugo speaks with Noemi Derzsy, a Senior Inventive Scientist at AT&T Labs within the Data Science and AI Research organization, where she does lots of science with lots of data.

They’ll be talking about her work at AT&T Labs Research, the mission of which is to look beyond today’s technology solutions to invent disruptive technologies that meet future needs. AT&T Labs works on a multitude of projects, from product development at AT&T, to how to combat bias and fairness issues in targeted advertising and creating drones for cell tower inspection research that leverages AI, ML and video analytics. They’ll be talking about some of the work Noemi does, from characterizing human mobility from cellular network data to characterizing their mobile network to analyze how its topology compares to other real social networks reported to understanding tv viewership, and how engaged people are in different shows. They’ll discuss what the future of data science looks like, whether it will even be around in 2029 and what types of skills would help you land a job in a place like AT&T Labs.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on DataFramed?)

FROM THE INTERVIEW

• Noemi on Twitter
• Noemi's Website
• Human Mobility Characterization from Cellular Network Data (By Richard Becker et al., Communications of the ACM)
• AT&T Labs Research Website
• NASA Datanauts Website
• Open NASA Website

FROM THE SEGMENTS

Guidelines for A/B Testing (with Emily Robinson ~18:23 & ~36:38)

• Testing multiple statistical hypotheses resulted in spurious associations: a study of astrological signs and health (By Peter C. Austin et al., Journal of Clinical Epidemiology)
• From Infrastructure to Culture: A/B Testing Challenges in Large Scale Social Networks (By Ya Xu et al., LinkedIn Corp)
• Guidelines for A/B Testing (By Emily Robinson)
• 10 Guidelines for A/B Testing Slides (By Emily Robinson)

Original music and sounds by The Sticks.

This week, Hugo speaks with Chris Albon about getting your first data science job. Chris is a Data Scientist at Devoted Health, where he uses data science and machine learning to help fix America's healthcare system. Chris is also doing a lot of hiring at Devoted and that’s why he’s so excited today to talk about how to get your first data science job. You may know Chris as co-host of the podcast Partially Derivative, from his educational resources such as his blog and machine learning flashcards or as one of the funniest data scientists on Twitter.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on DataFramed?)

FROM THE INTERVIEW

• Chris on Twitter
• Chris's Website
• Devoted Website
• Machine Learning Flashcards (By Chris Albon)
• Machine Learning with Python Cookbook (By Chris Albon)

FROM THE SEGMENTS

Guidelines for A/B Testing (with Emily Robinson ~26:50)

• Guidelines for A/B Testing (By Emily Robinson)
• 10 Guidelines for A/B Testing Slides (By Emily Robinson)

Original music and sounds by The Sticks.

This week, Hugo speaks with Reshama Shaikh, about women in machine learning and data science, inclusivity and diversity more generally and how being intentional in what you do is essential. Reshama, a freelance data scientist and statistician, is also an organizer of the meetup groups Women in Machine Learning & Data Science (otherwise known  as WiMLDS) and PyLadies. She has organized WiMLDS for 4 years and is a Board Member. They’ll discuss her work at WiMLDS and what you can do to support and promote women and gender minorities in data science. They’ll also delve into why women are flourishing in the R community but lagging in Python and discuss more generally how NUMFOCUS thinks about diversity and inclusion, including their code of conduct. All this and more.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on DataFramed?)

FROM THE INTERVIEW

• Reshama’s Blog
• Reshama on Twitter
• List of Relevant Conferences (and Code of Conduct info)
• NYC PyLadies meetup
• Code of Conduct for NeurIPS and Other Stem Organizations
• NumFOCUS Diversity & Inclusion in Scientific Computing (DISC)
• NumFOCUS DISCOVER Cookbook (for inclusive events)
• fastai deep learning notes

WiMLDS (Women in Machine Learning and Data Science)

• NYC WiMLDS meetup
• To start a WiMLDS chapter: email [email protected] and more info at our starter kit.
• WiMLDS Website
• Global List of WiMLDS Meetup Chapters
• WiMLDS Paris: They run their meetups in English, so knowledge of French is not required.  

FROM THE SEGMENTS

DataCamp User Stories (with David Sudolsky ~17:27 & ~31:50)

• Boldr Website

Original music and sounds by The Sticks.

This week, Hugo speaks with Marco Blume, Trading Director at Pinnacle Sports. Marco and Hugo will talk about the role of data science in large-scale bets and bookmaking, how Marco is training an army of data scientists and much more. At Pinnacle, Marco uses tight risk-management built on cutting-edge models to provide bets not only on sports but on questions such as who will be the next pope? Who will be the world hot dog eating champion, who will land on mars first and who will be on the iron throne at the end of game of thrones. They’ll discuss the relations between risk management and uncertainty, how great forecasters are necessarily good at updating their predictions in the light of new data and evidence, how you can model this using Bayesian inference and the future of biometric sensing in sports betting. And, as always, much, much more.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on DataFramed?)

FROM THE INTERVIEW

• Pinnacle Website
• Training an army of new data scientists (Presentation by Marco Blume)

FROM THE SEGMENTS

Data Science Best Practices (with Ben Skrainka ~16:40)

• Python Debugging With Pdb (By Nathan Jennings)
• pdb Tutorial (Github)
• The Visual Python Debugger for Jupyter Notebooks You’ve Always Wanted (By David Taieb)
• Debugging with RStudio (By Jonathan McPherson)
• Basics of Debugging

Statistical Distributions and their Stories (with Justin Bois at ~36:00)

• Justin's Website at Caltech
• Probability distributions and their stories (By Justin Bois)

Original music and sounds by The Sticks.

This week on DataFramed, the DataCamp podcast, Hugo speaks with Gabriel Straub, the Head of Data Science and Architecture at the BBC, where his role is to help make the organization more data informed and to make it easier for product teams to build data and machine learning powered products. They’ll be talking about data science and machine learning at the BBC and how they can impact content discoverability, understanding content, putting the right stuff in front of people, how Gabriel and his team develop broader data science & machine learning architecture to make sure best practices are adopted and what it means to apply machine learning in a sensible way. How does the BBC think about incorporating data science into its business, which has been around since 1922 and historically been at the forefront of technological innovation such as in radio and television? Listen to find out!

LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on DataFramed?)

FROM THE INTERVIEW

• Gabriel Straub: It's bigger on the inside (Video)
• BBC datalab

FROM THE SEGMENTS

DataCamp User Stories (with Krittika Patil ~16:10 & ~38:12)

• Kespry (Drone Aerial Intelligence for Industry)

Original music and sounds by The Sticks.

This week Hugo speaks with Dr. Brandeis Marshall, about people of color and under-represented groups in data science. They’ll talk about the biggest barriers to entry for people of color, initiatives that currently exist and what we as a community can do to be as diverse and inclusive as possible.

Brandeis is an Associate Professor of Computer Science at Spelman College. Her interdisciplinary research lies in the areas of information retrieval, data science, and social media. Other research includes the BlackTwitter Project, which blends data analytics, social impact and race as a lens to understanding cultural sentiments. Brandeis is involved in a number of projects, workshops, and organizations that support data literacy and understanding, share best data practices and broaden participation in data science.

LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on DataFramed?)

FROM THE INTERVIEW

• Brandeis on Twitter
• The BlackTwitter Project
• The Impact of Live Tweeting on Social Movements (By Brandeis Marshall, Takeria Blunt, Tayloir Thompson)
• EvergreenLP: Using a social network as a learning platform (By Brandeis Marshall, Jaye Nias, Tayloir Thompson, Takeria Blunt)
• Journal of Computing Sciences in Colleges (By Brandeis Marshall)
• DSX (Data Science eXtension Faculty development and undergraduate instruction in data science) 
• African American Women Computer Science PhDs
• 500 Women Scientists
• Black in AI
• Women in Machine Learning

FROM THE SEGMENTS

What Data Scientists Really Do (with Hugo Bowne-Anderson & Emily Robinson ~21:30 & ~41:40)

• What Data Scientists Really Do, According to 35 Data Scientists (Harvard Business Review article by Hugo Bowne-Anderson)
• What Data Scientists Really Do, According to 50 Data Scientists (Slides from a talk by Hugo Bowne-Anderson)

Original music and sounds by The Sticks.

In episode 50, our Season 1, 2018 finale of DataFramed, the DataCamp podcast, Hugo speaks with Cathy O’Neil, data scientist, investigative journalist, consultant, algorithmic auditor and author of the critically acclaimed book Weapons of Math Destruction. Cathy and Hugo discuss the ingredients that make up weapons of math destruction, which are algorithms and models that are important in society, secret and harmful, from models that decide whether you keep your job, a credit card or insurance to algorithms that decide how we’re policed, sentenced to prison or given parole? Cathy and Hugo discuss the current lack of fairness in artificial intelligence, how societal biases are perpetuated by algorithms and how both transparency and auditability of algorithms will be necessary for a fairer future. What does this mean in practice? Tune in to find out. As Cathy says, “Fairness is a statistical concept. It's a notion that we need to understand at an aggregate level.” And, moreover, “data science doesn't just predict the future. It causes the future.”


LINKS FROM THE SHOW

DATAFRAMED SURVEY

• DataFramed Survey (take it so that we can make an even better podcast for you)

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on Season 2?)

FROM THE INTERVIEW

• Cathy on Twitter
• Cathy's Blog Mathbabe
• Weapons of Math Destruction: How big data increases inequality and threatens democracy by Cathy O'Neil
• Cathy's Opinion Column, Bloomberg 
• Doing Data Science (By Cathy O'Neil and Rachel Schutt)
• Cathy O'Neil & Hanna Gunn's "Ethical Matrix" paper coming soon.

FROM THE SEGMENTS

Data Science Best Practices (with Heather Nolis ~20:30)

• Using docker to deploy an R plumber API (By Jonathan Nolis and Heather Nolis)
• Enterprise Web Services with Neural Networks Using R and TensorFlow (By Jonathan Nolis and Heather Nolis)

Data Science Best Practices (with Ben Skrainka ~39:35)

• The Clean Coder Blog (By Robert C. Martin)
• James Shore’s blog post on Red, Green, Refactor
• Jeff Knupp’s Python Unittesting tutorial (general unit tests in Python)
• John Myles White’s Intro to Unit Testing in R

Original music and sounds by The Sticks.

Hugo speaks with Wes McKinney, creator of the pandas project for data analysis tools in Python and author of Python for Data Analysis, among many other things. Wes and Hugo talk about data science tool building, what it took to get pandas off the ground and how he approaches building “human interfaces to data” to make individuals more productive. On top of this, they’ll talk about the future of data science tooling, including the Apache arrow project and how it can facilitate this future, the importance of DataFrames that are portable between programming languages and building tools that facilitate data analysis work in the big data limit. Pandas initially arose from Wes noticing that people were nowhere near as productive as they could be due to lack of tooling & the projects he’s working on today, which they’ll discuss, arise from the same place and present a bold vision for the future.

LINKS FROM THE SHOW

DATAFRAMED SURVEY

• DataFramed Survey (take it so that we can make an even better podcast for you)

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on Season 2?)

FROM THE INTERVIEW

• Wes on Twitter
• Roads and Bridges: The Unseen Labor Behind Our Digital Infrastructure by Nadia Eghbal
• pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
• Ursa Labs

FROM THE SEGMENTS

Data Science Best Practices (with Ben Skrainka ~17:10)

• To Explain or To Predict? (By Galit Shmueli)
• Statistical Modeling: The Two Cultures (By Leo Breiman)
• The Book of Why (By Judea Pearl & Dana Mackenzie)

Studies in Interpretability (with Peadar Coyle at ~39:00)

• Modelling Loss Curves in Insurance with RStan (By Mick Cooney)
• Lime: Explaining the predictions of any machine learning classifier 
• Probabilistic Programming Primer

Original music and sounds by The Sticks.

In this episode of DataFramed, the DataCamp podcast, Hugo speaks with Angela Bassa about managing data science teams. Angela is Director of Data Science at iRobot, where she leads the team through development of machine learning algorithms, sentiment analysis, and anomaly detection processes. iRobot are the makers of consumer robots that we all know and love, like the Roomba, and the Braava which are, respectively, a robotic vacuum cleaner and a robotic mop. Angela will talk about how to get into data science management, the most important strategies to ensure that your data science team delivers value to the organization, how to hire data scientists and key points to consider as your data science team grows over time, in addition to the types of trade-offs you need to make as a data science manager and how you make the right ones. Along the way, you’ll see why a former marine biologist has the skills and ways of thinking to be a super data scientist at a company like iRobot and you’ll also see the importance of throwing data analysis parties.

LINKS FROM THE SHOW

FROM THE INTERVIEW

• Angela on Twitter
• HBR Newsletters
• iRobot Careers
• Data Science Internship

FROM THE SEGMENTS

Correcting Data Science Misconceptions (w/ Heather Nolis ~18:45)

• Using docker to deploy an R plumber API (By Jonathon Nolis)
• Enterprise Web Services with Neural Networks Using R and TensorFlow (By Jonathan Nolis and Heather Nolis)

Project of the Month (w/ David Venturi ~38:45)

• Rise and Fall of Programming Languages (R Project by David Robinson)
• Learn, Practice, Apply! (By Ramnath Vaidyanathan)
• Apply to create a DataCamp project! 

Original music and sounds by The Sticks.