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  • Before the COVID-19 crisis, we were already acutely aware of the need for a broader conversation around data privacy: look no further than the Snowden revelations, Cambridge Analytica, the New York Times Privacy Project, the General Data Protection Regulation (GDPR) in Europe, and the California Consumer Privacy Act (CCPA). In the age of COVID-19, these issues are far more acute. We also know that governments and businesses exploit crises to consolidate and rearrange power, claiming that citizens need to give up privacy for the sake of security. But is this tradeoff a false dichotomy? And what type of tools are being developed to help us through this crisis? In this episode, Katharine Jarmul, Head of Product at Cape Privacy, a company building systems to leverage secure, privacy-preserving machine learning and collaborative data science, will discuss all this and more, in conversation with Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp.

    Links from the show

    FROM THE INTERVIEW

    Katharine on TwitterKatharine on LinkedInContact Tracing in the Real World (By Ross Anderson)The Price of the Coronavirus Pandemic (By Nick Paumgarten)Do We Need to Give Up Privacy to Fight the Coronavirus? (By Julia Angwin)Introducing the Principles of Equitable Disaster Response (By Greg Bloom)Cybersecurity During COVID-19 ( By Bruce Schneier)
  • 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 SHOW

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    FROM THE INTERVIEW

    Sean 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 Quality

    FROM THE SEGMENTS

    Guidelines 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.

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  • 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 SHOW

    DATAFRAMED GUEST SUGGESTIONS

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

    FROM THE INTERVIEW

    Debbie 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 SEGMENTS

    Data Science tools for getting stuff done and giving it to the world (with Jared Lander ~21:55)

    Lander Analytics WebsiteDocker Websiteplumber Website

    Statistical 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.

  • 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 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 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 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 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 TwitterChris's WebsiteDevoted WebsiteMachine 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 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 notes

    WiMLDS (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 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 WebsiteTraining 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 CaltechProbability 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 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 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 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 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, RefactorJeff 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 TwitterRoads and Bridges: The Unseen Labor Behind Our Digital Infrastructure by Nadia Eghbalpandas, 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 TwitterHBR NewslettersiRobot CareersData 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.

  • Hugo speaks with Peter Bull about the importance of human-centered design in data science. Peter is a data scientist for social good and co-founder of Driven Data, a company that brings cutting-edge practices in data science and crowdsourcing to some of the world's biggest social challenges and the organizations taking them on, including machine learning competitions for social good. They’ll speak about the practice of considering how humans interact with data and data products and how important it is to consider them while designing your data projects. They’ll see how human-centered design provides a robust and reproducible framework for involving the end-user all through the data work, illuminated by examples such as DrivenData’s work in financial services and Mobile Money in Tanzania. Along the way, they’ll discuss the role of empathy in data science, the increasingly important conversation around data ethics and much, much more.

    LINKS FROM THE SHOW

    FROM THE INTERVIEW

    Peter on TwitterDrivenDataDeon (Ethics Checklist)Cookiecutter Data ScienceIf you liked this interview, you might be interested in working with DrivenData! Currently, the team is looking for a software engineer who loves the idea of building Python applications for social impact. Apply Here!

    FROM THE SEGMENTS

    Probability Distributions and their Stories (with Justin Bois at ~24:00)

    Justin's Website at CaltechProbability distributions and their stories (By Justin Bois)

    Studies in Interpretability (with Peadar Coyle at ~38:10)

    Interpretable ML SymposiumHow will the GDPR impact machine learning? (By Andrew Burt)How to use Bayesian Stats in your daily job (Gates, Perry, Zorn (2002))Fairness in Machine Learning (By Moritz Hardt)

    Original music and sounds by The Sticks.

  • In this episode of DataFramed, a DataCamp podcast, Hugo speaks with Arnaub Chatterjee. Arnaub is a Senior Expert and Associate Partner in the Pharmaceutical and Medical Products group at McKinsey & Company. They’ll discuss cutting through the hype about artificial intelligence (AI) and machine learning (ML) in healthcare by looking at practical applications and how McKinsey & Company is helping the industry evolve.

    Tune in for an insider’s account into what has worked in healthcare, from ML models being used to predict nearly everything in clinical settings, to imaging analytics for disease diagnosis, to wound therapeutics. Will robots and AI replace disciplines such as radiology, ophthalmology, and dermatology? How have the moving parts of data science work evolved in healthcare? What does the future of data science, ML and AI in healthcare hold? Stick around to find out.

    LINKS FROM THE SHOW

    FROM THE INTERVIEW

    McKinsey Analytics on TwitterHot off the press article for HBR’s Future of Healthcare online forum (By Arnaub Chatterjee)Our latest piece on the promise & challenge of AI (By James Manyika and Jacques Bughin)Are robots coming for our jobs? (mckinsey.com)Analytics Careers page (mckinsey.com)How we help clients in healthcare analytics (mckinsey.com)AI analysis of 400+ use cases, including ones in healthcare (By Michael Chui et al. mckinsey.com)

    FROM THE SEGMENTS

    Machines that Multi-task (with Manny Moss)

    Part 1 at ~21:05

    Responsible AI in Consumer EnterpriseHilary Mason, DJ Patil and Mike Loukides on Data EthicsEthicalOS Tookit

    Part 2 at ~40:00

    21 Definitions of Fairness Tutorial from FAT* (Arvind Naranayan)Kate Crawford's keynote address "The Trouble with Bias" from NIPS 2017The (im)possibility of Fairness (Sorelle et al. arXiv.org)Learning from disparate data sources (Li Y et al. PubMed.gov)Distributed Multi-task Learning (Liyang Xie et al. KDD.org)The Cost of Fairness in Binary Classification (Aditya Krishna Menon et al. proceedings.mlr.press)

    Original music and sounds by The Sticks.

  • In this episode of DataFramed, Hugo speaks with Cassie Kozyrkov, Chief Decision Scientist at Google Cloud. Cassie and Hugo will be talking about data science, decision making and decision intelligence, which Cassie thinks of as data science plus plus, augmented with the social and managerial sciences. They’ll talk about the different and evolving models for how the fruits of data science work can be used to inform robust decision making, along with pros and cons of all the models for embedding data scientists in organizations relative to the decision function. They’ll tackle head on why so many organizations fail at using data to robustly inform decision making, along with best practices for working with data, such as not verifying your results on the data that inspired your models. As Cassie says, “Split your damn data”.

    Links from the show

    FROM THE INTERVIEW

    Cassie on Twitter Is data science a bubble? (By Cassie Kozyrkov, Hackernoon)Incompetence, delegation, and population (By Cassie Kozyrkov, Hackernoon)Populations — You’re doing it wrong (By Cassie Kozyrkov, Hackernoon)What on earth is data science? (By Cassie Kozyrkov, Hackernoon)

    FROM THE SEGMENTS

    Probability Distributions and their Stories (with Justin Bois at ~19:45)

    Justin's Website at CaltechProbability distributions and their stories (By Justin Bois)

    Machines that Multi-Task (with Friederike Schüür of Fast Forward Labs ~43:45)

    Sebastian’s Ruder’s Overview of Multi-Task Learning in Deep Neural NetworksMulti-Task Learning for NLP, also by Sebastian RuderGANs for Fake Celebrity Images (Karras et al, Nvidia)Adversarial Multi-Task Learning for Text Classification (Liu et al., arXiv.org)

    Original music and sounds by The Sticks.

  • In this episode of DataFramed, Hugo speaks with Brian Granger, co-founder and co-lead of Project Jupyter, physicist and co-creator of the Altair package for statistical visualization in Python.

    They’ll speak about data science, interactive computing, open source software and Project Jupyter. With over 2.5 million public Jupyter notebooks on github alone, Project Jupyter is a force to be reckoned with. What is interactive computing and why is it important for data science work? What are all the the moving parts of the Jupyter ecosystem, from notebooks to JupyterLab to JupyterHub and binder and why are they so relevant as more and more institutions adopt open source software for interactive computing and data science? From Netflix running around 100,000 Jupyter notebook batch jobs a day to LIGO’s Nobel prize winning discovery of gravitational waves publishing all their results reproducibly using Notebooks, Project Jupyter is everywhere. 


    Links from the show 

    FROM THE INTERVIEW

    Brian on Twitter Project JupyterBeyond Interactive: Notebook Innovation at Netflix (Ufford, Pacer, Seal, Kelley, Netflix Tech Blog)Gravitational Wave Open Science Center (Tutorials)JupyterCon YouTube Playlistjupyterstream Github Repository

    FROM THE SEGMENTS

    Machines that Multi-Task (with Friederike Schüür of Fast Forward Labs)

    Part 1 at ~24:40

    Brief Introduction to Multi-Task Learning (By Friederike Schüür)Overview of Multi-Task Learning Use Cases (By Manny Moss)Multi-Task Learning for the Segmentation of Building Footprints (Bischke et al., arXiv.org)Multi-Task as Question Answering (McCann et al., arXiv.org)The Salesforce Natural Language Decathlon: A Multitask Challenge for NLP 

    Part 2 at ~44:00

    Rich Caruana’s Awesome Overview of Multi-Task Learning and Why It WorksSebastian’s Ruder’s Overview of Multi-Task Learning in Deep Neural NetworksMassively Multi-Task Network for Drug Discovery, 259 Tasks (!) (Ramsundar et al. arXiv.org)Brief Overview of Multi-Task Learning with Video of Newsie, the Prototype (By Friederike Schüür)

     Original music and sounds by The Sticks.

  • Hugo speaks with Andrew Gelman about statistics, data science, polling, and election forecasting. Andy is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University and this week we’ll be talking the ins and outs of general polling and election forecasting, the biggest challenges in gauging public opinion, the ever-present challenge of getting representative samples in order to model the world and the types of corrections statisticians can and do perform. 

    "Chatting with Andy was an absolute delight and I cannot wait to share it with you!"-Hugo 

     Links from the show 

    FROM THE INTERVIEW

    Andrew's Blog Andrew on Twitter We Need to Move Beyond Election-Focused Polling (Gelman and Rothschild, Slate)We Gave Four Good Pollsters the Same Raw Data. They Had Four Different Results (Cohn, The New York Times).19 things we learned from the 2016 election (Gelman and Azari, Science, 2017)The best books on How Americans Vote (Gelman, Five Books)The best books on Statistics (Gelman, Five Books)Andrew's Research 

    FROM THE SEGMENTS

    Statistical Lesson of the Week (with Emily Robinson at ~13:30)

    The five Cs (Loukides, Mason, and Patil, O'Reilly)

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

    Oberkampf & Roy’s Verification and Validation in Scientific Computing provides a thorough yet very readable treatment A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (Roy and Oberkampf, Science Direct)

     Original music and sounds by The Sticks.

  • Hugo speaks with Vicki Boykis about what full-stack end-to-end data science actually is, how it works in a consulting setting across various industries and why it’s so important in developing modern data-driven solutions to business problems. Vicki is a full-stack data scientist and senior manager at CapTech Consulting, working on projects in machine learning and data engineering. They'll also discuss the increasing adoption of data science in the cloud technologies and associated pitfalls, along with how to equip businesses with the skills to maintain the data products you developed for them. All this and more: Hugo is pumped!

    Links from the show FROM THE INTERVIEW Vicki's Tech Blog Vicki on Twitter CapTech Consulting Vicki's Tweet about Programming Building a Twitter art bot with Python, AWS, and socialist realism art

    FROM THE SEGMENTS

    Data Science Best Practices (with Ben Skrainka~15:00)

    Cross-industry standard process for data mining Fundamentals of Machine Learning for Predictive Data Analytics

    Statistical Lesson of the Week (with Emily Robinson at ~32:05)

    Sex Bias in Graduate Admissions: Data from Berkeley (Bickel et al., Science, 1975) Time Series Analysis Tutorial with Python

    Original music and sounds by The Sticks.

  • Hugo speaks with Allen Downey about uncertainty in data science. Allen is a professor of Computer Science at Olin College and the author of a series of free, open-source textbooks related to software and data science. Allen and Hugo speak about uncertainty in data science and how we, as humans, are not always good at thinking about uncertainty, which we need be to in such an uncertain world. Should we have been surprised at the outcome of the 2016 election? What approaches can we, as a data reporting community, take to communicate around uncertainty better in the future? From election forecasting to health and safety, thinking about uncertainty and using data & data-oriented tools to communicate around uncertainty are essential.

    Links from the show FROM THE INTERVIEW Data Science Data Optimism Allen's Twitter List of cognitive biases Why are we so surprised? (Allen's Blog) Probably Overthinking It (Allen Downey's Blog) Think Stats (Allen's Book) There is only one test! (Allen's Blog)

    FROM THE SEGMENT

    Statistical Distributions and their Stories (with Justin Bois at ~27:00)

    Justin's Website at Caltech Probability distributions and their stories LeBron James Field Goals

    Original music and sounds by The Sticks.