Podcasts about dataframed

Introducing the DataFramed Careers Series. Over the past year hosting the DataFramed podcast, we've had the incredible privilege of having biweekly conversations with data leaders at the forefront of the data revolution. This has led to fascinating conversations on the future of the modern data stack, the future of data skills, and how to build organizational data literacy.  However, as the DataFramed podcast grows, we want to be able to provide the data science community across the spectrum from practitioners to leaders, with distilled insights that will help them manoeuvre their careers effectively. And we want to do that more often.  This is why we're excited to announce the launch of a four-day DataFramed Careers Series. Throughout next week, we will interview four different thought leaders and experts about what it takes to break into data science in 2022, best practices to stand out from the crowd, building a brand in data science, and more. Moreover, this episode series will mark DataFramed's transition from biweekly to weekly. Starting Monday the 30th of May, DataFramed will become a weekly podcast. For next week's DataFramed Careers Series, we'll be covering the ins and outs of building a career in data, and the different aspects of standing out from the crowd during the job hunt. We'll be hearing from Sadie St Lawrence, CEO and Founder of Women in Data on what it takes to launch a data career in 2022. Nick Singh, Co-author of Ace the Data Science Interview and 2nd time guest of DataFramed will join us to discuss what makes a great data science portfolio project. Khuyen Tran, Developer Advocate at Prefect on will outline how writing can accelerate a data career, and Jay Feng, CEO of Interview Query will join us to provide tips and frameworks on acing the data science interview. For future DataFramed episodes, we'll definitely still cover the different aspects of building a data-driven organization, cover the latest advancements in data science, building data careers, and more. So expect more varied guests, topics, and more specials series like this one in the future.

As data volumes grow and become ever-more complex, the role of the data analyst has never been more important. At the disposal of the modern data analyst, are tools that reduce time to insight, and increase collaboration. However, as the tools of a data analyst evolve, so do the skills.  Today's guest, Peter Fishman, Co-Founder at Mozart Data, speaks to this exact notion.  Join us as we discuss: Defining a data-driven organization & main challenges Breaking down the modern data stack & what it means What makes a great data analyst How data analysts can develop deep subject matter expertise in the areas they serve Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can't see the links? Just search for DataFramed in your favorite podcast player.

When you hear the term-digital first, you might think about tech, platforms and data.  But digital transformation succeeds when you put people first. Gathering and analyzing data, then using it to provide the customer value and an unparalleled experience, is vital for an organization's success. Today's guest, Bhavin Patel, Director o f Analytics and Innovation at J&J joins the show to share why people are the most important component to digital transformation. Join us as we discuss: Why you need to put people first The importance of customer value and experience Why digital transformation is an ongoing process, not an end-state  Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can't see the links? Just search for DataFramed in your favorite podcast player.

The data journey is a slow painstaking process. But knowing where to start and the areas to focus on can help any organization reach its goals faster. Today's guest, Vijay Yadav, Director of Quantitative Sciences & Head of Data Science at the Center for Mathematical Sciences at Merck, explains the 6 key elements of data strategy, complete with advice on how to navigate each. Join us as we discuss: The different components of a data strategy Shifting mindset within the C-Suite Structuring the operating model Enabling people to work with data at scale Most effective tactics to kickstart a community around data science Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can't see the links? Just search for DataFramed in your favorite podcast player.

It's no secret that data science jobs are on the rise; but data skills across the board are rising — leading to what today's guest calls “hybrid jobs.” This will require a paradigm shift in how we think about jobs and skills.   Today's guest, Matt Sigelman, President of The Burning Glass Institute & Chairman of Emsi Burning Glass, talks about the difficulties of connecting companies with top talent, the hybridization of many positions, and how to position yourself in the ever-changing market.  Join us as we discuss: The methodology of using data science on the labor market The demand for data skills & how they're evolving Blending skills to get ahead in the job market & the rise of subskills How educational institutions can prepare students for hybridization  Advice to the audience on how to structure their approach to skill acquisition  Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can't see the links? Just search for DataFramed in your favorite podcast player.

Throughout the middle east, efforts are underway to build smart cities from the ground up. But to create a modern, intelligently-designed city, you first need to lay a solid foundation. And the strongest foundation you can build a smart city upon is data. In today's episode, we speak with Kaveh Vessali, Digital, Data & AI Leader, PwC Middle East, about the intersection between data and public policy and the many exciting insights he's gained from his role delivering smart cities and data transformation projects within the public sector in the middle east.  Join us as we discuss: The important role data plays in shaping public policy  What goes into designing a smart city The change management skills vital for successful digital transformation  Data ethics and the importance of transparency   Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn.  Listening on a desktop and can't see the links? Just search for DataFramed in your favorite podcast player. 

When most people hear digital transformation, it's almost always the technology that first springs to mind.That's a mistake.You can have the most sophisticated tech stack in the world, but if you don't build your organization's data culture, your digital transformation efforts will be for naught.Today's guest, Mai AlOwaish, Chief Data Officer at Gulf Bank, knows this better than anyone. As the first female CDO in Kuwait, she's on a mission to ensure everyone at Gulf Bank becomes an expert in the data they use every day.Join us as we discuss:Why data and people are more important than technology for digital transformationThe pioneering Data Ambassador program Mai spearheaded at Gulf BankThe importance of diversity in data science and technology overallFind every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can't see the links? Just search for DataFramed in your favorite podcast player. 

In this episode of DataFramed, we speak with Vishnu V Ram, VP of Data Science and Engineering at Credit Karma about how data science is being leveraged to increase financial inclusion.Throughout the episode, Vishnu discusses his background, Credit Karma's mission, how data science is being used at Credit Karma to lower the barrier to entry for financial products, how he managed a data team through rapid growth, transitioning to Google Cloud, exciting trends in data science, and more. Relevant links from the interview:You can now learn data science with your team for free—try out DataCamp Professional with our 14-day free trial. Data roles at Credit KarmaCredit Karma's mission

In this episode of DataFramed, we speak with Andy Cotgreave, Technical Evangelist at Tableau about the role of data storytelling when driving change with analytics, and the importance of the analyst role within a data-driven organization.Throughout the episode, Andy discusses his background, the skills every analyst should know to equip organizations with better data-driven decision making, his best practices for data storytelling, how he thinks about data literacy and ways to spread it within the organization, the importance of community when creating a data-driven organization, and more.Relevant links from the interview:We'd love your feedback! Let us know which topics you'd like us to cover and what you think of DataFramed by answering this 30-second surveyCheck out our upcoming webinar with AndyCheck out Andy's bookBecome a Tableau expert

In this episode of DataFramed, we speak with Brian Campbell, Engineering Manager at Lucid Software about managing data science projects effectively and harnessing the power of collaboration. Throughout the episode, Brian discusses his background, how data leaders can become better collaborators, data science project management best practices, the type of collaborators data teams should seek out, the latest innovations in the data engineering tooling space, and more.Relevant links from the interview:We'd love your feedback! Let us know which topics you'd like us to cover and what you think of DataFramed by answering this 30-second surveyLucid's Tech Blog

In this episode of DataFramed, we speak with Shameek Kundu, former group CDO at Standard Chartered Bank, and Chief Strategy Officer & Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.Throughout the episode, Shameek discusses his background, the state of data transformation in financial services, the depth vs breadth of machine learning operationalization in financial services today, the challenges standing in the way of scalable AI adoption in the industry, the importance of data literacy, the trust and responsibility challenge of AI, the future of data science in financial services, and more.Relevant links from the interview:We'd love your feedback! Let us know which topics you'd like us to cover and what you think of DataFramed by answering this 30-second surveyCheck out TruEra in actionBank of England Report: The impact of Covid on machine learning and data science in UK BankingMIT Tech Review — Hundreds of AI tools have been built to catch covid. None of them helped

In this episode of DataFramed, we speak with Syafri Bahar, VP of Data Science at Gojek about building high-performing data teams, and how data science is central to Gojek's success. Throughout the episode, Syafri discusses his background, the hallmarks of a high-performance data team, how he measures the ROI on data activities, the skills needed in every successful data team, what is the best organizational model for data mature organizations, how Covid-19 affected Gojek's data teams, his thoughts on data literacy and governance, future trends in data science and AI, and why data scientists should sharpen their maths and machine learning skills in an age of increasing automation. Relevant links from the interview:We'd love your feedback! Let us know which topics you'd like us to cover and what you think of DataFramed by answering this 30-second surveyGojek's Data Blog

In this episode of DataFramed, we speak with Noah Gift, founder of Pragmatic AI Labs and prolific author about operationalizing machine learning in organizations and his new book Practical MLOPs. Throughout the episode, Noah discusses his background, his philosophy around pragmatic AI, the differences between data science in academia and the real world, how data scientists can become more action-oriented by creating solutions that solve real-world problems, the importance of dev-ops, his most recent book on the practical guide to MLOps, how data science can be compared to Brazilian jiu-jitsu, what data scientists should learn to scale the amount of value they deliver, his thoughts on auto-ml and automation, and more. Relevant links from the interview:We'd love your feedback! Let us know which topics you'd like us to cover and what you think of DataFramed by answering this 30-second surveyUnsettled: What Climate Science Tells Us, What It Doesn't, and Why It MattersCheck out Noah's booksCheck out Noah's course on DataCampConnect with Noah on LinkedInGain access to DataCamp's full course library at a discount!

In this episode of DataFramed, we speak with Rick Scavetta and Boyan Angelov about their new book, Python and R for the Modern Data Scientist: The Best of Both Worlds, and how it dawns the start of a new bilingual data science community.  Throughout the episode, Rick and Boyan discuss the history of Python and R, what led them to write the book, how Python and R can be interoperable, the advantages of each language and where to use it, how beginner data scientists should think about learning programming languages, how experienced data scientists can take it to the next level by learning a language they're not necessarily comfortable with, and more. Relevant links from the interview:We'd love your feedback! Let us know which topics you'd like us to cover and what you think of DataFramed by answering this 30-second surveyCheck out Rick and Boyan's bookCheck out Rick's courses on DataCampCheck out Boyan's other booksConnect with Rick on LinkedInConnect with Boyan on LinkedIn

In this episode of DataFramed, we speak with Brent Dykes, Senior Director of Insights & Data Storytelling at Blast Analytics and author of Effective Data Storytelling: How to Turn Insights into Action on how data storytelling is shaping the analytics space. Throughout the episode, Brent talks about his background, what made him write a book on effective data storytelling, how data storytelling is often misinterpreted and misused, the psychology of storytelling and how humans are shaped to resonate with it, the role of empathy when creating data stories, the blueprint of a successful data story, what data scientists can do to become better data storytellers, the future of augmented analytics and data storytelling, and more. Relevant links from the interview:Connect with Brent on LinkedInRegister for Brent's Webinar on DataCampCheck out Brent's Book

In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.Throughout the episode, Maria talks about her background, the differences & intersections between "AI ethics" and "Responsible AI", the state of responsible AI adoption within organizations, the link between responsible AI and organizational culture, what data scientists can do today to ensure they're part of their organization's responsible AI journey, and more. Relevant links from the interview:Connect with Maria on LinkedInKate Crawford's Atlas of AI9 Ethical AI Principles for Organizations to FollowPwC's Responsible AI ToolkitRead our Data Literacy for Responsible AI White Paper

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs,  an AI Observability company on a mission to build the interface between AI and human operators. Throughout the episode, Alessya talks about the unique challenges data teams face when operationalizing machine learning that spurred the need for MLOps, how MLOps intersects and diverges with different terms such as DataOps, ModelOps, and AIOps, how and when organizations should get started on their MLOps journey, the most important components of a successful MLOps practice, and more. Relevant links from the interview:Connect with Alessya on LinkedInAndrew Ng on the important of being data-centricJoe Reis on the data culture and all things datawhylogs: the standard for data logging — please send you feedback, contribute, help us build integrations into your favorite data tools and extend the concept of logging to new data types. Join the effort of building a new open standard for data logging!Try the WhyLabs platform

In this episode of DataFramed, Adel speaks with Sudaman Thoppan Mohanchandralal, Regional Chief Data, and Analytics Officer at Allianz Benelux, on the importance of building data cultures and his experiences operationalizing data culture transformation programs.Throughout the episode, Sudaman talks about his background, the Chief Data Officer's mandate and how it has evolved over the years, how organizations should prioritize building data cultures, the science behind culture change, the importance of executive data literacy when scaling value from data, and more.Relevant links from the interview:Connect with Sudaman on LinkedInCheck out Sudaman's Webinar on DataCampWhy Data Culture Matters

In this episode of DataFramed, Adel speaks with Elad Cohen, VP of Data Science and Research at Riskified on how data science is being used to combat fraud in eCommerce.Throughout the episode, Elad talks about his background, the plethora of data science use-cases in eCommerce, how Riskified builds state-of-the-art fraud detection models, common pitfalls data teams face, his best practices gaining organizational buy-in for data projects, how data scientists should focus on value, whether they should have engineering skills, and more.Relevant links from the interview:Connect with Elad on LinkedInRegister for our upcoming webinarsHow Riskified chooses what to research

In this episode of DataFramed, Adel speaks with Barr Moses, CEO, and co-founder of Monte Carlo on the importance of data quality and how data observability creates trust in data throughout the organization. Throughout the episode, Barr talks about her background, the state of data-driven organizations and what it means to be data-driven, the data maturity of organizations, the importance of data quality, what data observability is, and why we'll hear about it more often in the future. She also covers the state of data infrastructure, data meshes, and more. Relevant links from the interview:Connect with Barr on LinkedInLearn more about data meshesCheck out the Monte Carlo blogDataCamp's Guide to Organizational Data Maturity

In this episode of DataFramed, Adel speaks with Sergey Fogelson, Vice President of Data Science and Modeling at Viacom on how data science has evolved over the past decade, and the remaining large-scale challenges facing data teams today.Throughout the episode, Sergey deep-dives into his background, the various projects he’s been involved with throughout his career, the most exciting advances he’s seen in the data science space, the largest challenges facing data teams today, best practices democratizing data, the importance of learning SQL, and more. Relevant links from the interview:Connect with Sergey on LinkedInCheck out Sergey’s course on DataCampLearn more about AirflowLearn more about PySparkLearn more about SQLMore resources from DataCampUpskill your team with DataCampOur Guide on Open Source Software in Data ScienceYour Organization’s Guide to Data Maturity

In this episode of DataFramed, Adel speaks with Dan Becker, CEO of decision.ai and founder of Kaggle Learn on the intersection of decision sciences and AI, and best practices when aligning machine learning to business value.Throughout the episode, Dan deep-dives into his background, how he reached the top of a Kaggle competition, the difference between machine learning in a Kaggle competition and the real world, the role of empathy when aligning machine learning to business value, the importance of decisions sciences when maximizing the value of machine learning in production, and more. Links:Follow Dan on TwitterFollow Dan on LinkedInWhat 70% of data science learners do wrongCheck out Dan’s course on DataCampdecision.aiDan’s climate dashboard

In this episode of DataFramed, Adel speaks with Amen Ra Mashariki, principal scientist at Nvidia and the former Chief Analytics Officer of the City of New York on how data science is done in government agencies, and how it's driving smarter cities all around us. Throughout the episode, Amen deep-dives into the use-cases he worked on to make the city of New York smarter, how data science allows cities to become more reactive and proactive, the unique challenges of scaling data science in a government setting, the friction between providing value and data privacy and ethics, the state of data literacy in government, and more. Links from the interview:Follow Amen on LinkedInFollow Amen on TwitterThe New York City Business AtlasHurricane Sandy FEMA After-Action ReportData Drills

We are super excited to be relaunching the DataFramed podcast. In this iteration of DataFramed, Adel Nehme, a data science educator at DataCamp, will uncover the latest thinking on all things data and how it’s impacting organizations through biweekly (once every two weeks) interviews and conversations with data experts from across the world. Check out this snippet for a preview of what’s to come and for a short chat with DataCamp’s CEO Jonathan Cornelissen on where he thinks data science is headed and the major challenges facing data teams today. Links:For the rest of April, get free access to DataCamp.Get involved with DataCamp Donates

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.

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.

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 SHOWFROM THE INTERVIEWAngela on TwitterHBR NewslettersiRobot CareersData Science InternshipFROM THE SEGMENTSCorrecting 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.

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 SHOWFROM THE INTERVIEWMcKinsey 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 SEGMENTSMachines that Multi-task (with Manny Moss)Part 1 at ~21:05Responsible AI in Consumer EnterpriseHilary Mason, DJ Patil and Mike Loukides on Data EthicsEthicalOS TookitPart 2 at ~40:0021 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 showFROM THE INTERVIEWCassie 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 SEGMENTSProbability 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 INTERVIEWBrian 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 RepositoryFROM THE SEGMENTSMachines that Multi-Task (with Friederike Schüür of Fast Forward Labs)Part 1 at ~24:40Brief 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 ~44:00Rich 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.

For this episode, I speak with Hugo Bowne-Anderson; a data scientist at DataCamp (an educational platform for learning to code) and host of the DataFramed podcast. The idea for asking Hugo to appear on this episode, was to chat about learning a programming language. Because for some traders, having the ability to write code can have great advantages—such as having the ability to collect stats on market behavior, perform research in a robust data-driven way, visualize large amounts of data, backtest and analyse trading ideas, implement algorithmic strategies, etc. Plus more professional trading firms and finance related positions now require applicants to have some programming skills. And the same goes for many industries, which should be no surprise, considering a recent IBM study revealed that ‘90% of the world’s data has been created in the last two years alone.’ Hugo and I discuss when someone should consider learning to code, determining what’s relevant, the time it takes to become fluent in a programming language, working with new datasets, what to be wary of when using predictive models. And for fun, I ask Hugo (as a data scientist) how he’d go about creating a basic strategy…

"Cloud computing is a huge revolution in the computing space, and it's also probably going to be one of the most transformative technologies that any of us experience in our lifetime. " Paige Bailey, Senior Cloud Developer Advocate at Microsoft, in this episode of DataFramed. In this conversation with Hugo, Paige reports from the frontier of cloud-based data science technologies, having just been at the Microsoft Build and Google I/O conferences. What is the future of data science in the cloud? How can you get started? Stick around to find out and much, much more.

Air pollution, the environment and data science: where do these intersect? Find out in this episode of DataFramed, in which Hugo speaks with Roger Peng, Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, co-director of the Johns Hopkins Data Science Lab and co-founder of the Johns Hopkins Data Science Specialization. Join our discussion about data science, it's role in researching the environment and air pollution, massive open online courses for democratizing data science and much more.

We are super pumped to be launching a weekly data science podcast called DataFramed, in which Hugo Bowne-Anderson, a data scientist and educator at DataCamp, speaks with industry experts about what data science is, what it’s capable of, what it looks like in practice and the direction it is heading over the next decade and into the future. Check out this snippet for a sneak preview!