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DataFramed

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[Note: DataFramed is currently on a short hiatus, but we plan to bring it back during Summer, 2019.] Data science is one of the fastest growing industries and has been called the ‘Sexiest job of the 21st Century’. But what exactly is data science? In this podcast, brought to you by DataCamp, Hugo Bowne-Anderson approaches the question by exploring what problems data science can solve rather than defining what data science is. From automated medical diagnosis and self-driving cars to recommendation systems and climate change, come on a journey with experts from industry and academia to explore the industry that will change the course of the 21st century.
58 Episodes
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This week, Hugo speaks with Sean Law about data science research and development at TD Ameritrade. Sean’s work on the Exploration team uses cutting edge theories and tools to build proofs of concept. At TD Ameritrade they think about a wide array of questions from conversational agents that can help customers quickly get to information that they need and going beyond chatbots. They use modern time series analysis and more advanced techniques like recurrent neural networks to predict the next time a customer might call and what they might be calling about, as well as helping investors leverage alternative data sets and make more informed decisions.What does this proof of concept work on the edge of data science look like at TD Ameritrade and how does it differ from building prototypes and products? And How does exploration differ from production? Stick around to find out.LINKS FROM THE SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWSean on TwitterSean's WebsiteTD Ameritrade Careers PagePyData Ann Arbor MeetupPyData Ann Arbor YouTube Channel (Videos)TDA Github Account (Time Series Pattern Matching repo to be open sourced in the coming months)Aura Shows Human Fingerprint on Global Air QualityFROM THE SEGMENTSGuidelines for A/B Testing (with Emily Robinson ~19:20)Guidelines for A/B Testing (By Emily Robinson)10 Guidelines for A/B Testing Slides (By Emily Robinson)Data Science Best Practices (with Ben Skrainka ~34:50)Debugging (By David J. Agans)Basic Debugging With GDB (By Ben Skrainka)Sneaky Bugs and How to Find Them (with git bisect) (By Wiktor Czajkowski)Good logging practice in Python (By Victor Lin)Original music and sounds by The Sticks.Support the show
This week, Hugo speaks with Debbie Berebichez about the importance of critical thinking in data science. Debbie is a physicist, TV host and data scientist and is currently the Chief Data Scientist at Metis in NY.In a world and a professional space plagued by buzz terms like AI, big data, deep learning, and neural networks, conversations around skill sets and less than productive programming language wars, what has happened to critical thinking in data science and data thinking in general? What type of critical thinking skills are even necessary as data science, AI and machine learning become even more present in all of our lives and how spread out do they need to be across organizations and society? Listen to find out!LINKS FROM THE SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWDebbie on TwitterDebbie's WebsiteDebbie Berebichez- Media Reel (Video)Deborah Berebichez' Keynote at Grace Hopper Celebration 2017 (Video)Debbie Berebichez on Perseverance and Paying it Forward (Video)Things about the Future and the Future of Things (By Debbie Berebichez, Video)FROM THE SEGMENTSData Science tools for getting stuff done and giving it to the world (with Jared Lander ~21:55)Lander Analytics WebsiteDocker Websiteplumber WebsiteStatistical Distributions and their Stories (with Justin Bois ~39:30)Probability distributions and their stories (By Justin Bois)The History of Statistics (By Stephen M. Stigler)The Evolution of the Normal Distribution (By Saul Stahl)Original music and sounds by The Sticks.Support the show
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 SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWSkipper on TwitterSkipper on GithubWhat'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 DocumentationFROM THE SEGMENTSGuidelines 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.Support the show
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 SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWNoemi on TwitterNoemi's WebsiteHuman Mobility Characterization from Cellular Network Data (By Richard Becker et al., Communications of the ACM)AT&T Labs Research WebsiteNASA Datanauts WebsiteOpen NASA WebsiteFROM THE SEGMENTSGuidelines 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.Support the show
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 SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWChris on TwitterChris's WebsiteDevoted WebsiteMachine Learning Flashcards (By Chris Albon)Machine Learning with Python Cookbook (By Chris Albon)FROM THE SEGMENTSGuidelines 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.Support the show
#54 Women in Data Science

#54 Women in Data Science

2019-02-2500:47:172

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 SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWReshama’s BlogReshama on TwitterList of Relevant Conferences (and Code of Conduct info)NYC PyLadies meetupCode of Conduct for NeurIPS and Other Stem OrganizationsNumFOCUS Diversity & Inclusion in Scientific Computing (DISC)NumFOCUS DISCOVER Cookbook (for inclusive events)fastai deep learning notesWiMLDS (Women in Machine Learning and Data Science)NYC WiMLDS meetupTo start a WiMLDS chapter: email info@wimlds.org and more info at our starter kit.WiMLDS WebsiteGlobal List of WiMLDS Meetup ChaptersWiMLDS Paris: They run their meetups in English, so knowledge of French is not required.  FROM THE SEGMENTSDataCamp User Stories (with David Sudolsky ~17:27 & ~31:50)Boldr WebsiteOriginal music and sounds by The Sticks.Support the show
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 SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWPinnacle WebsiteTraining an army of new data scientists (Presentation by Marco Blume)FROM THE SEGMENTSData 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 DebuggingStatistical Distributions and their Stories (with Justin Bois at ~36:00)Justin's Website at CaltechProbability distributions and their stories (By Justin Bois)Original music and sounds by The Sticks.Support the show
#52 Data Science at the BBC

#52 Data Science at the BBC

2019-02-1101:01:37

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 SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWGabriel Straub: It's bigger on the inside (Video)BBC datalabFROM THE SEGMENTSDataCamp User Stories (with Krittika Patil ~16:10 & ~38:12)Kespry (Drone Aerial Intelligence for Industry)Original music and sounds by The Sticks.Support the show
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 SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWBrandeis on TwitterThe BlackTwitter ProjectThe 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 PhDs500 Women ScientistsBlack in AIWomen in Machine LearningFROM THE SEGMENTSWhat 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.Support the show
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 SHOWDATAFRAMED SURVEYDataFramed Survey (take it so that we can make an even better podcast for you)DATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on Season 2?)FROM THE INTERVIEWCathy on TwitterCathy's Blog MathbabeWeapons of Math Destruction: How big data increases inequality and threatens democracy by Cathy O'NeilCathy'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 SEGMENTSData 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, RefactorJeff Knupp’s Python Unittesting tutorial (general unit tests in Python)John Myles White’s Intro to Unit Testing in ROriginal music and sounds by The Sticks.Support the show
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Comments (8)

Jorge Arbelaez

interesting interview

May 23rd
Reply

Anh D Tran

excuse me im just taking note here: some process with the truck guy tips how to do data science in big org with google guy from superdatascience eda explaratory analysys from tukey

May 22nd
Reply

Moncsi

Hi there, is it possible to get links to the data philanthropy organisations? I'm super curious. Thank you!

Mar 25th
Reply

Jokus Jodokus

The short section about the connection between data scientists and project managers resonated with me

Feb 26th
Reply

Anthony Rossi

400 million people do not have diabetic retinopathy, incorrect statistic.

Jan 23rd
Reply

Paolo Eusebi

Amazing episode! How many listeners worked with Stan in R? What are their impressions over other bayesian software?

Oct 9th
Reply

Rafael Anjos

The contents are very good. Thank you for your good job

Sep 18th
Reply

Anthony Giancursio

Ol

Jul 19th
Reply
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