Cryptocurrency and blockchain take center stage this week as we welcome Chief Economist at Chainalysis, Philip Gradwell, to discuss the data science applications in this exciting field.

This episode is brought to you by Datalore (https://datalore.online/SDS), the collaborative data science platform, by Zencastr (zen.ai/sds), the easiest way to make high-quality podcasts, and by Bunch (superdatascience.com/bunch), the AI driven leadership coach. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• What the role of a chief economist entails [5:50]
• What are blockchains and cryptocurrency? [8:23]
• How analyzing cryptocurrencies differs from established fiat currencies [12:48]
• Philip's work at Chainalysis [26:07]
• Philip's crypto data analytics pipeline [34:48]
• How Philip develops data products for a wide range of users [46:18]
• How the blockchain facilitates innovative computing and machine learning technologies [51:52]
• What Philip looks for in the data scientists he hires [1:04:59]

Additional materials: www.superdatascience.com/621

The Super Data Science Podcast: Machine Learning | AI | Success

621: Blockchains and Cryptocurrencies: Analytics and Data Applications

Jai Ranganathan, senior director of product management at Cloudera, on trends in the Hadoop ecosystem, cloud computing, the recent surge in interest in all things real time, and hardware trends

Jai Ranganathan On Architecting Big Data Applications In The Cloud

In this episode of the O’Reilly Data Show, I spoke with Ira Cohen, co-founder and chief data scientist at Anodot (full disclosure: I’m an advisor to Anodot). Since my days in quantitative finance, I’ve had a longstanding interest in time-series analysis. Back then, I used statistical (and data mining) techniques on relatively small volumes of financial time series. Today’s applications and use cases involve data volumes and speeds that require a new set of tools for data management, collection, and simple analysis.

On the analytics side, applications are also beginning to require online machine learning algorithms that are able to scale, are adaptive, and free of a rigid dependence on labeled data. I talked with Cohen about the challenges in building an advanced analytics system for intelligent applications at extremely large scale.

Semi-Supervised, Unsupervised, and Adaptive Algorithms for Large-Scale Time Series

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.

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.

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