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Data Science is Awesome

data science

SuperDataScience

Machine Learning

From Aaron Sorkin to Steve Jobs to Meredith Perry and Elon Musk, "original" thinkers -- such as entrepreneurs -- do a lot of different things to move the world to their visions. And many of those things (and traits) are counterintuitive, such as ... Embracing procrastination. But there's a catch: It's about being the just-right amount of procrastinator, expert, or confident. There's a curvilinear relationship between too much and too little.

There's also some surprising findings about why NOT to "start with the why" but with the how. Because sometimes the how is much more believable than the why. Especially when it comes to getting people to engineer things from ubeam to SpaceX. Or to really being able to tell the difference between communication vs. confidence vs. competence.

Ultimately, it's all about being flexible, argues top Wharton management professor and New York Times columnist Adam Grant in his new book Originals. So how do we strike the just-right balance -- whether making an entrepreneur or just trying to raise more creative, productive kids? Is the answer perhaps to immerse them in sci-fi books and video games? Well, J.K. Rowling could be the most influential "original" alive, argues Grant in this podcast... but not for the reasons you think.

a16z Podcast: How to Be Original and Make Big Ideas Happen

In this session of the Super Data Science Podcast, I chat with Ruben Kogel who has done some amazing data analytics in the startup space.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/1

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

SDS 001 : Self-serve Analytics, Data Science MBA and Managing a Team of Analysts with Ruben Kogel

In this session of the Super Data Science Podcast, I chat with machine learning expert Hadelin de Ponteves about his work at Google, Canal+ and what career implications may come from the rapid growth of the Data Science field.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/2

SDS 002 : Machine Learning, Recommender Systems and The Future of Data with Hadelin de Ponteves

In this session of the Super Data Science Podcast, I chat with Nano Science PHD turned Data Scientist Dr Wilson Pok. You will learn about the importance of defining business problems, a scientific approach to thinking about data analytics and more.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/3

SDS 003 : Defining the Data Problem, Academia vs Career and R Modeling Libraries with Dr. Wilson Pok

In this session of the Super Data Science Podcast, I chat with business strategy expert Brendan Hogan. You will learn how he uses Data to alter strategy of businesses, you will learn about the three pillars of research and how to build a successful career from your strengths.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/4

SDS 004 : Data and Strategy, Three Pillars of Research and Building your Career with Brendan Hogan

In this session of the Super Data Science Podcast, I chat with Forensics Investigator Dmitry Korneev. You will learn about Computer Forensics, what Fraud Analytics actually is and we will discuss how to know when to take a break from work.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/5

SDS 005 : Computer Forensics, Fraud Analytics And Knowing When To Take A Break With Dmitry Korneev

In this episode of the SuperDataScience Podcast, I chat with Data Science Author Sinan Ozdemir. You will learn the different applications of data science, advance your knowledge in democratizing AI and hear advices that will help the learners of data science.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/21

SDS 021 : Applications of Data Science, Democratizing AI and Advice with Sinan Ozdemir

In this episode of the Data Show, I spoke with Jason Dai, CTO of big data technologies at Intel, and co-chair of Strata + Hadoop World Beijing. Dai and his team are prolific and longstanding contributors to the Apache Spark project. Their early contributions to Spark tended to be on the systems side and included Netty-based shuffle, a fair-scheduler, and the “yarn-client” mode. Recently, they have been contributing tools for advanced analytics. In partnership with major cloud providers in China, they’ve written implementations of algorithmic building blocks and machine learning models that let Apache Spark users scale to extremely high-dimensional models and large data sets. They achieve scalability by taking advantage of things like data sparsity and Intel’s MKL software. Along the way, they’ve gained valuable experience and insight into how companies deploy machine learning models in real-world applications.

Deep Learning for Apache Spark

Do you sweat every day? Why do you think would you want to?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/26

SDS 026 : Sweat Every Day

In this episode of the SuperDataScience Podcast, I chat with Vice President of Finance Aziz Mamatov. You will get advices on how to choose a career, learn about the disruptions in the financial industry landscape and get to know about application stacks. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/27

SDS 027 : Career Choice, Disruptions in Finance and Application Stacks with Aziz Mamatov

How do you think can you find a way to feel satisfied with your achievements without taking away your energy from future greatness?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/28

SDS 028 : Pride and Humility

In this episode of the SuperDataScience Podcast, I chat with Chief Data Scientist Ben Taylor. You will learn about the role of AI in recruitment, know about a crash course in deep learning and discuss on where machines can outperform humans. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/29

SDS 029: Dive Into Deep Learning and Find Out Where Machines Can Outperform Humans With Ben Taylor

In this episode of the Data Show, I spoke with Ken Stanley, founding member of Uber AI Labs and associate professor at the University of Central Florida. Stanley is an AI researcher and a leading pioneer in the field of neuroevolution—a method for evolving and learning neural networks through evolutionary algorithms. In a recent survey article, Stanley went through the history of neuroevolution and listed recent developments, including its applications to reinforcement learning problems.

Effective Mechanisms for Searching the Space of Machine Learning Algorithms

What happens if you take four of today’s most popular buzzwords and string them together? Does the result mean anything? Stephen Wolfram explores his blog post "Buzzword Convergence: Making Sense of Quantum Neural Blockchain AI." Read the post here: https://wolfr.am/buzzword-convergence

Buzzword Convergence: Making Sense of Quantum Neural Blockchain AI

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

SDS 029: Dive Into Deep Learning and Find Out Where Machines Can Outperform Humans With Ben Taylor

Super Data Science: ML & AI Podcast with Jon Krohn