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Related tracks: Java Language Architect Brian Goetz on Java and the JDK

Speaker(s): Professor Andrew Gamble | One of the aims of LSE Summer School is to bring the LSE experience to all those attending its many courses between July and August. Five years ago we launched our first full programme of Lectures. These have been an immense success. All lectures are exclusively for LSE Summer School students, and are given by top flight speakers talking on the big issues of the day. The events begin at 5.30pm and take place in the Old Theatre, located in the Old Building on Houghton Street. Lectures will be followed by a reception where students will be able to speak to the lecturers. Credits: Tom Sturdy (Audio Post-Production), LSE AV Services (Audio Recording). Thu, 31 Jul 2014 17:30:00 GMT

Crisis without End? The Unravelling of Western Prosperity [Audio]

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

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

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 Up and Rising Tableau Expert Megan Putney. You will know about how Megan started with Tableau through online learning, learn about know about Ad Hoc Analytics and gain some powerful Tableau insights.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/12

SDS 012 : Online Learning, Tableau Insights and Ad Hoc Analytics with Megan Putney

In this episode of the SuperDataScience Podcast, I chat with Aspiring Data Scientist Nicholas Cepeda. You will be able to discover the many different pathways in Data Science, learn R from a SQL background and know about SAS Enterprise Miner. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/41

SDS 041: An Inspiring Journey from a Totally Different Background to Data Science

In this episode of the SuperDataScience Podcast, I chat with Kaggle Grandmaster Eu Jin Lok. You will be able to know how to forecast year-ahead hospital stays using an ensemble of algorithms, discover feature extraction in a credit scoring model, discuss his great competition experiences and be able to get ideas on how to be involved in a Kaggle competition.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/45

SDS 045: Exclusive Interview with the Legendary in Kaggle Competitions Eu Jin Lok

In this episode of the SuperDataScience Podcast, I chat with the Deep Learning Expert Hadelin de Ponteves. You will be able to have a look at the 6 supervised and unsupervised deep learning models, learn the idea to the question: why deep learning instead of machine learning, and discover TensorFlow and PyTorch as the two major tools in Deep Learning.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/47

SDS 047: An Expert Overview of the Deep Learning Models, Supervised and Unsupervised

In this week’s Design Podcast, I sit down with Julie Stanford, founder and principal of user experience agency Sliced Bread Design. We talk about how to get in the rapid experimentation mindset, the design thinking process, and how to get started with rapid experimentation at your company. Hint: start small.

Julie Stanford on Vetting Designs Through Rapid Experimentation

In this episode of the SuperDataScience Podcast, I chat with the Chief Data Officer at Ziff, Ben Taylor. You will discuss about the future of AI & what the world of future could look like, learn about the different approaches to Deep Learning revolution, and also get to know the consequences of the oncoming AI developments.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/85

SDS 085: The AI Revolution – What the Future Will Look Like

Are you willing to make the digital shift to be a model-driven business?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/212

SDS 212: Model Driven Vs Data Driven

In this episode of the SuperDataScience Podcast, I chat with the legends of visualization, Andy Kriebel and Eva Murray. You will learn about visualization and why it is important for data scientists and machine learning experts to know how to visualize data. You will also hear some amazing tips from their brand new creation, MakeoverMonday the Book. 

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/213

SDS 213: Amazing Tips from Two Legends of Visualization

In this episode of the SuperDataScience Podcast, I chat with the full stack web developer and aspiring data scientist, Brian Dowe. You will hear what is like to go from developer to data scientist and how to integrate data science in your career as a developer. You will learn about the development, deployment and maintenance life cycle of models in business and you will hear some ideas on modeling in general.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/215

SDS 215: Integrating Data Science as a Developer

What do you think is the most significant application of data science in the healthcare industry so far?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/216

SDS 216: Data Science In Healthcare

On this week’s podcast, Wes Reisz talks with Brian Goetz. Brian is the Java Language Architect at Oracle. The two start with a discussion on what the six-month cadence has meant to the teams developing Java. Then move to a review of the features in Java 9 through 12. Finally, the two discuss the longer-term side projects (such as Amber, Loom, and Valhalla) and their role in the larger release process for the JDK.

* The JVM’s sixth-month cadence changed the way the JDK is delivered and planned. While it definitely provides more rapid delivery at expected intervals, the release train approach turned out to also improve flexibility and efficiency.
* Oracle JDK and OpenJDK are almost identical. Most of the JDK distributions are forks from OpenJDK with different bug fixes and backports applied. So the difference between the distributions now is largely which bug fixes are picked up.
* Local Variable inference (which was released as part of Java 10) illustrated the tension on making changes to the language. Many people wanted the change, but many others felt it would enable people to write bad code. Oracle had to balance the two views when making the change.
* The number of Java versions allow finer grain decision making on what is appropriate for an application. With the adoption of containers, applications are bundled with an exact JDK version rather than having to use one from a systems level. The different versions give developers more options.
* Incubating features are new libraries added to the JDK. They were offered starting with Java 9 as a way for people to test and offer feedback more rapidly. With Java 12, preview features will be released. Preview features are similar but are core platform and language features.
* Shenandoah and ZGC are both low latency garbage collectors. They originally came from different sources. While both garbage collectors are similar, each has different performance characteristics under different workloads. The two garbage collectors represent options available to JVM developers.
* Most non-trivial JDK features take more than six months to develop. Longer term side projects like Amber, Loom, Valhalla are where these features are developed prior to being released with a version of the JDK. The projects range from language enhancements to concurrency work.

Java Language Architect Brian Goetz on Java and the JDK

In this episode of the SuperDataScience Podcast, Hadelin de Ponteves and I discuss the accuracy rate of the predictions we made for 2018. We also discuss the key AI and technology trends to look out for in 2019 which can help you structure your carrier and design your path through technology.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/223

SDS 223: Data Science Trends for 2019

In this episode of the SuperDataScience Podcast, I chat with the Data Science Consultant, Nic Ryan, who does a great job in combining the technical and consulting/mentoring part of data science in his career. You will hear Nic's journey of creating his remote carrier and a balanced work-family relationship, along with some valuable tips you can apply to your own carrier. You will also hear about natural language processing and some examples of his work.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/235

SDS 235: Living the Dream With Data Science

This Extra Inning of the Ballpark features audio from the US Centre’s event “Conspiracy Theories in the Age of Trump,” which took place on 25 July 2018. Joseph Uscinski, associate professor of political science at University of Miami, speaks about his book American Conspiracy Theories and why President Trump might be America’s first conspiratorial president. Using an analysis of more than a hundred years of data taken from newspapers, surveys, and the internet, Professor Uscinski demonstrates that conspiracy theories follow a strategic logic: they are tools used by the powerless to attack and defend against the powerful. Our chair for this event, Ros Taylor, is Research Manager for the LSE Truth, Trust & Technology Commission, based in the Media Policy Project within the Department of Media and Communications at LSE.

The Ballpark | Extra Innings: Conspiracy Theories in the Age of Trump with Joe Uscinski

How can you use data science to help keep the energy industry helpful and ethical towards the planet and future generations?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/272

SDS 272: Data Science in Energy

In this episode of the SuperDataScience Podcast, I chat with Matthew Rosenquist, one of the top leading world experts in the space of cybersecurity. You will learn what balance in cybersecurity means and what the dark web is. You will hear how Matthew's career developed and how he thinks about the strategy of cybersecurity. You will also learn about the valuable role of data science in cybersecurity and the steps you can take to get into this space.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/273

SDS 273: Predict, Prevent, Detect: Cyber Security

What has happened to you recently that could call for clarity from a mentor or coach?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/274

SDS 274: Ask the Right Question

In this episode of the SuperDataScience Podcast, I chat with the Machine Learning Research Scientist, John Langford. You will hear about unsupervised, supervised learning and reinforcement learning, and the differences between the three. You will learn about applications of contextual bandits and reinforcement learning in general, YOLO style algorithms versus simulator algorithms, technics for avoiding local optimums. You will also learn about the balance between exploration and exploitation, learning to search and active learning.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/275

SDS 275: Machine Learning Through Reinforcement & Contextual Bandits

How can you use these processes in your industry? 

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/276

SDS 276: Data Science in Wealth Management

How does your inner voice compare to your passions?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/290

SDS 290: The Passion Paradox

In this episode of the SuperDataScience Podcast, I chat with founder and CEO at Daisy Intelligence, Gary Saarenvirta. You'll learn about dangerous implicit assumptions, the power of theory and theory versus data. You'll also learn about two types of decisions,  the spacial interaction model, traffic flow model, the concept of dividing the world in two and what humans should be doing, and what artificial intelligence should be doing. You will hear about the difference between artificial intelligence that leverages just data versus artificial intelligence that leverages theory and data, and what advantages that creates.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/291

SDS 291: Changing the World With Theory & Data

How can you find a way to balance your energy through recharging in the way that works best for you?  

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/292

SDS 292: Introverts and Extroverts

In this episode of the SuperDataScience Podcast, I chat with Data Scientist, Peyman Hesami. You will find out what reinforcement learning is and how it works on an intuitive level. You will hear about the differences between reinforcement learning versus classification, or other supervised learning methods, and how it's used for personalization specifically. You will learn about six distinct advantages of reinforcement learning, what role reinforcement learning is going to play in the future of machine learning and why. Also, you will find out how and why Peyman made a career transition to work for a startup, how he's using reinforcement learning, and what is the biggest mistake he has made with reinforcement learning.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/293

SDS 293: True Personalization Through Reinforcement Learning

In this episode of the SuperDataScience Podcast, I chat with my friend and business partner, Hadelin de Ponteves. You will hear what new exciting things are happening in Hadelin's life now. You will hear some preview of his upcoming presentation at DataScienceGO 2019, which will cover NLP, especially the BERT model, which raised a whole new level in NLP. You will also learn about reinforcement learning and Hadelin's new course on Twin-Delayed DDPG.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/295

SDS 295: A Deep Conversation About Tech & Life

Stratos Hadjioannou is a freshly hired data scientist who is self-taught and made the jump to visit DSGO. He talks about his learnings, putting himself in a data science ecosystem, and how to tackle interviews with little experience.

In this episode you will learn: 
• Where did Stratos start? [6:16]
• How to keep the momentum for learning [12:20]
• Stratos’s goals [19:35]
• Planning the steps to getting a data science job [23:01]
• Triad for successful interviews [32:47]
• Application process [34:53]
• Experiences from the first data science job [45:51]

Additional materials: www.superdatascience.com/351

SDS 351: Self-Starting In Data Science

DJ Patil talks about ethics in data science and the importance of data science communities working together to make sure data science is an accelerant of solutions for our children and our children’s children.

In this episode you will learn:
• How does it feel to be the person who created data science as we know it now? [3:17]
• What data science is not [6:01]
• Ethics and data science development in different countries [10:00]
• What is the “biorevolution”? [16:02]
• The importance of data sharing [20:10]
• The current state of Chief Data Scientist of USA [24:07]
• LinkedIn Q&A [26:03]
• What to think about when you think about data science [44:08]

Additional materials: www.superdatascience.com/355

SDS 355: DJ Patil on Harnessing the Power of Data Science Community

In this episode, I talk about an issue I’ve been having when it comes to phasing my mind out of work and into post-work activities, a concept called “attention residue”.

Additional materials: www.superdatascience.com/374

SDS 374: Remember to Wind Down

Greg Pavlik joins me for a great talk about the current state of the cloud and how single practitioners and small businesses can take advantage of cloud services.

In this episode you will learn:
• Will we have cloud-based solutions for VR and working from home? [8:15]
• Greg’s career journey [11:50]
• From Hadoop to Cloud [23:35]
• The cloud element in data science [30:17]
• Data science and AI in Oracle [33:00]
• Is Oracle more suited for larger companies only? [37:35]
• Fundamental differences between Oracle Cloud, Amazon, and Azure [42:12]
• Trends in data science and data management [45:14]
• Why should someone choose Oracle over any other open source? [52:50]
• How does the future of data management look like? [56:00]
• 5G and edge computing [1:01:36] 
• Greg’s recommendation to data scientists [1:04:28]

Additional materials: www.superdatascience.com/375

SDS 375: Utilizing Oracle Cloud as an Enterprise, Small Business, or Developer

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 235: Living the Dream With Data Science

Super Data Science: ML & AI Podcast with Jon Krohn