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

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

This special episode is hosted by Jonathon Morgan. 

He went to the White House (the White House!) to interview DJ Patil, the Chief Data Scientist of the United States. DJ talked with Jonathon about the relationship between government and Silicon Valley, the White House’s leadership on data ethics, and why the first US Chief Data Scientist was actually George Washington. This was a really special experience, and we were honored at the invitation. Huge thanks to DJ and the White House for making this possible.

https://talkpython.fm/episodes/show/89/a-conversation-with-the-chief-data-scientist-of-the-united-states

Talk Python To Me

#89: A conversation with the Chief Data Scientist of the United States

In this episode of the SuperDataScience Podcast, I chat with Director at PricewaterhouseCoopers Richard Hopkins. You will know how data drives business operations, learn about bottom-up and top-down consulting approaches and get insights about mentoring.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/16

SDS 016 : Data-Driven Operations, Consulting Approaches, and Mentoring with Richard Hopkins

Today we are delighted to bring you an interview with Matt Might, computer scientist and medical researcher extraordinaire and architect of President Obama's Precision Medicine Initiative.  As the Obama Administration winds down, we're talking with Matt about the goals and accomplishments of precision medicine (and related projects like the Cancer Moonshot) and what he foresees as the future marriage of data and medicine.  Many thanks to Matt, our friends over at Partially Derivative (hi, Jonathon!) and the White House for arranging this opportunity to chat.  Enjoy!

Data and the Future of Medicine : Interview with Precision Medicine Initiative researcher Matt Might

Do you have a dirty, messy data problem? Whether you work as a software developer or as a data scientist, you've surely run across data that was malformed, incomplete, or maybe even wrong. Don't let messy data wreck your apps or generate wrong results. 

What should you do? Listen to this episode of Talk Python To Me with Katharine Jarmul about the book she co-authored called Data Wrangling with Python and her PyCon UK presentation entitled How to Automate your Data Cleanup with Python.

https://talkpython.fm/episodes/show/90/data-wrangling-with-python

#90: Data Wrangling with Python

It's been an amazing year for Python and Data Science. It's time to look back at the major headlines and take stock in what we've done as a community. 

I've teamed up with the Partially Derivative podcast and we're running down the top 10 data science stories of 2016 in this joint episode.

https://talkpython.fm/episodes/show/91/top-10-data-science-stories-of-2016

#91: Top 10 Data Science Stories of 2016

Take Google-size data, the flexibility of a neural net, and all (well, most) of the languages of the world, and what you end up with is a pile of surprises.  This episode is about some interesting features of Google's new neural machine translation system, namely that with minimal tweaking, it can accommodate many different languages in a single neural net, that it can do a half-decent job of translating between language pairs it's never been explicitly trained on, and that it seems to have its own internal representation of concepts that's independent of the language those concepts are being represented in.  Intrigued?  You should be...

Zero Shot Translation

If one machine learning model is good, are two models better?  In a lot of cases, the answer is yes.  If you build many ok models, and then bring them all together and use them in combination to make your final predictions, you've just created an ensemble model.  It feels a little bit like cheating, like you just got something for nothing, but the results don't like: algorithms like Random Forests and Gradient Boosting Trees (two types of ensemble algorithms) are some of the strongest out-of-the-box algorithms for classic supervised classification problems.  What makes a Random Forest random, and what does it mean to gradient boost a tree?  Have a listen and find out.

Ensemble Algorithms

This week: everybody's favorite WWII-era classifier metric!  But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

Rock the ROC Curve

In this episode of the SuperDataScience Podcast, I chat with Marketing Expert Sam Flegal. You will know about the essential roles of data in marketing, learn about statistical significance, and get insights and advices about management and career.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/23

SDS 023 : Data in Marketing, Statistical Significance and Management and Career Advice

Remember last week, when we were talking about how great the ROC curve is for evaluating models?  How things change...  This week, we're exploring calibrated risk models, because that's a kind of model that seems like it would benefit from some nice ROC analysis, but in fact the ROC AUC can steer you wrong there.

Calibrated Models

In this episode of the SuperDataScience Podcast, I chat with Data Analyst Kimberly Deas. You will get to listen to a discussion about women in STEM, know how being into bench science field could get you to data science and be able to learn about data and medical ethics. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/25

SDS 025 : Women in STEM, Bench Science to Data Science and Data and Medical Ethics w/ Kimberly Deas

Have you been out protesting lately, or watching the protests, and wondered how much effect they might have on lawmakers?  It's a tricky question to answer, since usually we need randomly distributed treatments (e.g. big protests) to understand causality, but there's no reason to believe that big protests are actually randomly distributed.  In other words, protest size is endogenous to legislative response, and understanding cause and effect is very challenging.

So, what to do?  Well, at least in the case of studying Tea Party protest effectiveness, researchers have used rainfall, of all things, to understand the impact of a big protest.  In other words, rainfall is the instrumental variable in this analysis that cracks the scientific case open.  What does rainfall have to do with protests?  Do protests actually matter?  What do we mean when we talk about endogenous and instrumental variables?  We wouldn't be very good podcasters if we answered all those questions here--you gotta listen to this episode to find out.

Endogenous Variables and Measuring Protest Effectiveness

Say you're looking to use some Bayesian methods to estimate parameters of a system.  You've got the normalization figured out, and the likelihood, but the prior... what should you use for a prior?  Empirical Bayes has an elegant answer: look to your previous experience, and use past measurements as a starting point in your prior.

Scratching your head about some of those terms, and why they matter?  Lucky for you, you're standing in front of a podcast episode that unpacks all of this.

Empirical Bayes

In this episode of the O’Reilly Bots Podcast, I speak with Tim Hwang, an affiliated researcher at the Oxford Internet Institute, about AI-driven psyops bots and their capacity for social destabilization.

Tim Hwang on Bots That Cause Chaos

When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who belongs to a baseball team), how should you estimate it: use measurements of the individual, or get some extra information from the group?  The James-Stein estimator tells you how to combine individual and group information make predictions that, taken over the whole group, are more accurate than if you treated each individual, well, individually.

Stein's Paradox

I'm machine learning enthusiast with applied math background and a lot experience as full stack developer.
I'm from Ukraine but now I'm working remotely in Montenegro.

data science

Hyzhak