data

With the rise of mobile phones equipped with GPS, we're meeting many more data scientists at start-ups and large companies who specialize in spatio-temporal pattern recognition. Analyzing “moving dots” requires specialized tools and techniques. A few months ago, I sat down with Rajiv Maheswaran founder and CEO of Second Spectrum, a company that applies analytics to sports tracking data. Maheswaran talks about the tools and techniques required to analyze new kinds of sports data.

For more on our interview, visit O'Reilly Radar: http://oreil.ly/1t7EcOQ

The science of moving dots

In this episode, Ben Lorica chats with Jay Kreps, a highly regarded data engineer and former technical lead for Online Data Infrastructure at LinkedIn, and most recently CEO/co-founder of Confluent, about Apache Kafka, data integration, event data, and the Internet of Things.

Building Apache Kafka From Scratch

Intercom content marketing manager Adam Risman talks with Riley Newman about the importance of early data investment for startups, making data accessible at scale, and why data is "the voice of the customer."

Riley Newman, Head Of Data Science At Airbnb

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 podcast episode, I talk multi-model databases with Damon Feldman, solutions director at MarkLogic. If this conversation gets you interested in multi-model databases, be sure to check out his live webcast on December 1, 2016.

Damon Feldman on Multi-Model Databases

In this episode of the Data Show, I spoke with Grace Huang, data science lead at Pinterest. With its combination of a large social graph, enthusiastic users, and multimedia data, I’ve long regarded Pinterest as a fascinating lab for data science. Huang described the challenge of building a sustainable content ecosystem and shared lessons from the front lines of machine learning product launches. We also discussed recommenders, the emergence of deep learning as a technique used within Pinterest, and the role of data science within the company.

A Framework for Building and Evaluating Data Products

My guest in this podcast episode is Stewart Rogers, director of product management at Lambda Solutions, which offers an open source e-learning management system; its customers are organizations that deliver online learning and need to understand how their students are progressing. Rogers is thus doubly dependent on embedded analytics in his products: he uses analytics to manage his products, measuring customer response to new features, but he also needs to pass meaningful analytics through to his customers in order to let them assess their learners and their content.

data

Mircea Ionescu