data science

science podcasts

orielly

The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

O'Reilly Big Data Podcast

Data Science

Cloudera Co-Founder Mike Olson shares his insights on the present landscape and possible future of big data and the data management industry. In conversation with Ping Li of Accel Partners, Olson also discusses the advantages of building a business on top of open source technologies and the many surprising benefits of competition.

Mike Olson, Ping Li - Opportunities Abound in the Big Data Space

Max Shron and Jake Porway talk with Jon Bruner about frameworks for making reasoned arguments with data. Max's recent book, <a href="http://shop.oreilly.com/product/0636920029182.do"><i>Thinking with Data</i></a>, outlines the crucial process of developing good questions and creating a plan to answer them. Jake's nonprofit, <a href="http://www.datakind.org/">DataKind</a>, connects data scientists with worthy causes where they can apply their skills.

For more on this episode, visit the <a href="http://radar.oreilly.com/2014/03/podcast-thinking-with-data.html">O'Reilly Radar blog</a>.

<i>This episode is brought to you by <a href="http://solidcon.com/">Solid</a>, O'Reilly's new conference about the intersection between software and the physical world--including hardware, manufacturing, design, and the code that makes sense of it all. For more information on Solid, and to reserve tickets, visit http://solidcon.com.</i>

Thinking with data

Social Science, Small worlds and Big Data

In this episode, I talk with Liza Kindred, founder of Third Wave Fashion and author of the new free report “Fashioning Data: How fashion industry leaders innovate with data and what you can learn from what they know.” Kindred addresses the evolving role data and analytics are playing in the fashion industry, and the emerging connections between technology and fashion companies. 

In the second segment, I chat with Max Firtman, founder of ITMaster, about the state of the HTML5 space and the future of mobile. He stresses that the change the Internet of Things will bring to the space will be significant and companies need to prepare: "All we know for sure right now is that everything is going to change."

For more, visit O'Reilly Radar: http://oreil.ly/101zhb5

What happens when fashion meets data?

O’Reilly’s big data guru Ben Lorica sits down with prominent data scientist DJ Patil to talk about his new ebook (written with Hilary Mason) and other topics in data science and big data.

A Brief Look At Data Science's Past And Future

Dr. Mark Gerstein of Yale University speaks to CHI on January 16, 2015. Dr. Gerstein will be a speaker during the Clinical Genomics track at Bio-IT World Conference & Expo, April 21-23, 2015 in Boston, MA.
 
Topics include evolving data science applications to genomics, the utility of networks for biological data analysis and integration, studying non-coding mutations for cancer genomics and personal genomics research, the computational tool FunSeq and more.

http://www.bio-itworldexpo.com/

BioIT World 2015 | Applying Data Science, Bioinformatics and Networks to Cancer Genomics

O’Reilly’s Ben Lorica chats with Kira Radinsky, CTO and co-founder of SalesPredict, about predicting events using machine learning, NLP, and semantic analysis.

Forecasting events, from disease outbreaks to sales to cancer research

We often talk about designing security into a product from the get go, but we don't often discuss what that means. In today's podcast John Kestner, a principle with Supermechanical, the company behind the Twine sensor and the Range thermometer, shares his thoughts on how he designed Twine and range to be more secure but also how we might design our devices to indicate that what was once a humble lock is now a connected computer with all the pros and cons that entails.

Before Kestner and I dig into security, Kevin Tofel and I talk about my experience playing with the Logitech Harmony Ultimate Remote and home hub and then we dig into CSRMesh and the Bluetooth SIG's creation of a working group to add mesh networking to the Bluetooth Smart standard. That could put Bluetooth in closer competition with thread, Zigbee and Z-wave. So kick back and listen up.

Hosts: Stacey Higginbotham and Kevin Tofel
Guests: John Kestner, principal with Supermechanical

Thoughts on the Logitech Harmony Hub and remote (it's pretty nifty, guys)
The Bluetooth SIG is forming a working group on mesh technologies. Why this is cool!
Should you rely on security by obscurity or hope your device doesn't do much that's scary as a way to be secure?
Thinking about how to convey connectedness in design

How to design security into your connected product

O'Reilly's Mary Treseler talks with Andy Goodman, designer and group director of Fjord’s US studios, about service design, embeddables, and predictive analytics.

Designing On A System Level

O'Reilly's Ben Lorica chats with David Blei, co-author of the seminal paper on topic models, and who remains one of the leading researchers in the field. They talk about the origins of topic models, their applications, improvements to the underlying algorithms, and his new role in training data scientists at Columbia University.

Topic models: Past, present, and future

The Internet of Things is challenging designers to rethink their craft. I recently sat down with Claire Rowland, independent designer and author of the forthcoming book Designing Connected Products to talk about the changing design landscape.

During our interview, Rowland brought up three points that resonated with me: interoperability and the Internet of Things, experience designers and networks, and the IoT’s latency problem.

For more on our interview and for related links, check out the related post on O’Reilly Radar: oreil.ly/1EoyfUV

The complexity of the IoT requires experience design solutions

O'Reilly's Ben Lorica talks with Angie Ma, co-founder and president of ASI, about the concept of the ASI, the structure of their training programs, and the data and startup scene in the UK.

Turning Ph.D.s Into Industrial Data Scientists and Data Engineers

Eliot Knudsen, Field Architect at Tamr, on the business value of prescriptive analytics and machine learning algorithms.

Machine learning for procurement analytics

Collaboration tools in the open-source Trusted Analytics Platform (TAP) address the human side of data analytics. O'Reilly's Rachel Roumeliotis speaks with Chuck Freedman, Chief Developer Advocate for TAP. 

Making data analytics easier for data scientists, developers, and DevOps

In this episode of the Data Show, I spoke with Mike Tung, founder and CEO of Diffbot - a company dedicated to building large-scale knowledge databases. Diffbot is at the heart of many web applications, and it’s starting to power a wide array of intelligent applications. We talked about the challenges of building a web-scale platform for doing highly accurate, semi-supervised, structured data extraction. We also took a tour through the AI landscape, and the early days of self-driving cars.

Using AI to Build a Comprehensive Database of Knowledge

In episode three of the O’Reilly Bots podcast, Pete Skomoroch and I speak with Dennis Yang, co-founder and chief product officer of Dashbot, an analytics platform for bots. Bots are a new way for humans to interact with computers, and require new ways of thinking about measurement.

We discuss crucial differences between bots and conventional interfaces, how human writers are essential for setting the right tone in a bot, and why users ask bots to tell them jokes.

Dennis Yang on Bot Analytics

In episode five of the O’Reilly Bots podcast, Pete Skomoroch and I speak with Joshua Browder, the 19-year old founder and CEO of DoNotPay, a series of bots that help people with legal issues, including challenging parking tickets, challenging bank charges, and claiming government assistance for homelessness. Dubbed “the world’s first robot lawyer,” his bots have attracted 260,000 users and provided 175,000 successful parking-ticket appeals.

Joshua Browder on Bots That Fight Bureaucracy

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.