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it is playlist about data science. I'm putting here all podcast in english in related topic. If you know something that should be here please drop me a message with link. And follow me on twitter: @hyzhak_en

data science

Data

DS podcasts

What does it take to build a data-driven culture? In this O’Reilly Podcast episode, I pose that question to Ashish Thusoo, founder of Qubole. With his co-founder Joydeep Sen Sarma, Thusoo built a self-service data infrastructure at Facebook beginning in 2007. That infrastructure transformed Facebook’s culture and became part of practically every product decision that the company made.

Building a Data-Driven Culture

In this episode I spoke with Michael Franklin, co-director of UC Berkeley’s AMPLab and chair of the Department of Computer Science at the University of Chicago. AMPLab is well-known in the data community for having originated Apache Spark, Alluxio (formerly Tachyon) and many other open source tools. Today marks the start of a two-day symposium commemorating the end of AMPLab, and we took the opportunity to reflect on its impressive accomplishments.

Building the Next-Generation Big Data Analytics Stack

In this episode of the Super Data Science Podcast, I chat with Chief Data Scientist Damian R Mingle. You will learn about how to build models with 95% accuracy rates, know the 3 top tips on quick wins and winning people over; and get ideas on how to save lives with data science.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/13

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 013 : 95% Accuracy Models, Winning People Over, and Saving Lives with Damian Mingle

In this episode, I sit down with Brad Knox, founder and CEO of Emoters, a startup building a product called bots_alive—animal-like robots that have a strong illusion of life. We chat about the approach the company is taking, why robots or agents that pass themselves off as human without any transparency should be illegal, and some challenges and applications of reinforcement learning and interactive machine learning.

Brad Knox on Creating a Strong Illusion of Life

In this episode I spoke with Ion Stoica, cofounder and chairman of Databricks. Stoica is also a professor of computer science at UC Berkeley, where he serves as director of the new RISE Lab (the successor to AMPLab). Fresh off the incredible success of AMPLab, RISE seeks to build tools and platforms that enable sophisticated real-time applications on live data, while maintaining strong security. As Stoica points out, users will increasingly expect security guarantees on systems that rely on online machine learning algorithms that make use of personal or proprietary data.

Data is Only as Valuable as the Decisions it Enables

In this podcast episode, Ken Krupa, enterprise CTO at MarkLogic, walks me through the challenge of data integration and outlines an approach for dealing with it known as the “operational data hub.”

Integrating Across Silos With Operational Data Hubs

This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working on? And how should that decision be made? That's the subject of a talk that I (Katie) gave at Strata Data in early March, about how my co-department head and I select projects for our team to work on.

We have several goals in data science project selection at Civis Analytics (where I work), which can be summarized under "balance the best attributes of bottom-up and top-down decision-making." We achieve this balance, or at least get pretty close, using a process we've come to call the Idea Factory (after a great book about Bell Labs). This talk is about that process, how it works in the real world of a data science company and how we see it working in the data science programs of other companies.

Relevant links: https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63905

How to pick projects for a professional data science team

In a recent episode of the O’Reilly Media Podcast, we spoke with George Miranda about the importance of service mesh technology in creating reliable distributed systems. As discussed in the new report The Service Mesh: Resilient Service-to-Service Communication for Cloud Applications, service mesh technology has emerged as a popular tool for companies looking to build cloud-native applications that are reliable and secure.

During the podcast, we discussed the problems a service mesh infrastructure solves and the service mesh features you’ll find most valuable. We also talked about how to choose the right service mesh for your organization, the challenges involved in getting it deployed to production, and the best ways for getting started with a service mesh.

How to Choose and Deploy a Service Mesh

If you're a data scientist at a firm that does a lot of software building, chances are good that you've seen or heard engineers sometimes talking about "agile software development." If you don't work at a software firm, agile practices might be newer to you. In either case, we wanted to go through a great series of blog posts about some of the practices from agile that are relevant for how data scientists work, in hopes of inspiring some transfer learning from software development to data science.

Relevant links:
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 1: The Good

There's just too much interesting stuff at the intersection of agile software development and data science for us to be able to cover it all in one episode, so this week we're picking up where we left off last time. We'll give a quick overview of agile for those who missed last week or still have some questions, and then cover some of the aspects of agile that don't work well out-of-the-box when applied to data analytics. Fortunately, though, there are some straightforward modifications to agile that make it work really nicely for data analytics!

Relevant links:
https://www.agilealliance.org/agile101/12-principles-behind-the-agile-manifesto/
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 2: Where Modifications Help

In this episode of the O’Reilly Media Podcast, Rachel Roumeliotis, VP of content strategy at O’Reilly, sat down with Daniel Krook, IBM developer advocate. They discussed how developers across industries can participate in the Call for Code initiative, the benefits of the program, support from its charitable partners—United Nations Human Rights and the American Red Cross—and the positive impacts IBM hopes to achieve by investing in Call for Code.

Developers Worldwide Will Collaborate on Solutions to Save Lives in Call For Code

If you've been in data science for more than a year or two, chances are you've noticed changes in the field as it's grown and matured. And if you're newer to the field, you may feel like there's a disconnect between lots of different stories about what data scientists should know, or do, or expect from their job. This week, we cover two thought pieces, one that arose from interviews with 35(!) data scientists speaking about what their jobs actually are (and aren't), and one from the head of data science at AirBnb organizing core data science work into three main specialties.

Relevant links:
https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal

The Three Types of Data Scientists, and What They Actually Do

This episode of the O’Reilly Podcast, features a conversation on serverless and Kubernetes, with Kelsey Hightower, developer advocate for Google Cloud Platform at Google (and co-author of "Kubernetes: Up and Running"), and Chris Gaun, Kubernetes product marketing manager at Mesosphere.

Kelsey Hightower and Chris Gaun on Serverless and Kubernetes

At Google’s 2019 Cloud Next conference, I sat down with Stephen Thorne, site reliability engineer on Google’s customer reliability engineering team and co-author of "The Site Reliability Workbook," to talk about how organizations, both large and small, can use SRE to reduce operational costs, improve reliability, and create productive cross-functional teams.

How to Get Started With Site Reliability Engineering (SRE)

In this interview, Tim Craig and fellow Googler Gustavo Franco, a site reliability engineer (SRE), discuss the wide range of events that qualify as “incidents;” the need for a conscious, robust, and well-defined process for understanding them; the role of training; and how to get buy-in from management so you can spread incident response training throughout an organization.

Taming Chaos: Preparing for Your Next Incident

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.

Integrating Across Silos With Operational Data Hubs

O'Reilly Radar