Media Summary: "Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the ... Government Spending and Population Well-being Does government spending buy happiness? A common theme cutting across ... Conference Website: The shortage of qualified

Felix Cheung Scalable Data Science - Detailed Analysis & Overview

"Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the ... Government Spending and Population Well-being Does government spending buy happiness? A common theme cutting across ... Conference Website: The shortage of qualified Moon Lee of Zepl explains how the company uses Amazon ECS, Amazon EKS, and Amazon ECR to build a PyData Carolinas 2016 Processing large datasets in R have been limited by the amount of memory in the local system.

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Felix Cheung - Scalable Data Science in Python and R on Apache Spark
Scalable Data Science with SparkR: Spark Summit East talk by Felix Cheung
SSR: Structured Streaming on R for Machine Learning - Felix Cheung
Building Intelligent Apps, Experimental ML  (Felix Cheung & Atul Gupte)
Meet Felix Cheung of IXON
Government Spending and Population Well-being with Dr. Felix Cheung | Knowledge Café
Big Data and Scalable Data Science Challenges - Volker Markl, TU Berlin
Scalable Data Science from Atlantis - Day 16 - Part 2
Scalable Data Science from Atlantis, Student Course Projects, Set 2
Scaling Up Data Science Applications with Kexin Xie and Yacov Salomon
Zepl: Scalable Data Science Platform on Amazon Web Services
Scalable Data Science from Atlantis - Day 22 - Part 3 (end of Andrew Morgan hangout & into DL)
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Felix Cheung - Scalable Data Science in Python and R on Apache Spark

Felix Cheung - Scalable Data Science in Python and R on Apache Spark

Description In the world of

Scalable Data Science with SparkR: Spark Summit East talk by Felix Cheung

Scalable Data Science with SparkR: Spark Summit East talk by Felix Cheung

R is a very popular platform for

SSR: Structured Streaming on R for Machine Learning - Felix Cheung

SSR: Structured Streaming on R for Machine Learning - Felix Cheung

"Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the ...

Building Intelligent Apps, Experimental ML  (Felix Cheung & Atul Gupte)

Building Intelligent Apps, Experimental ML (Felix Cheung & Atul Gupte)

Felix Cheung

Meet Felix Cheung of IXON

Meet Felix Cheung of IXON

Meet our finalist,

Government Spending and Population Well-being with Dr. Felix Cheung | Knowledge Café

Government Spending and Population Well-being with Dr. Felix Cheung | Knowledge Café

Government Spending and Population Well-being Does government spending buy happiness? A common theme cutting across ...

Big Data and Scalable Data Science Challenges - Volker Markl, TU Berlin

Big Data and Scalable Data Science Challenges - Volker Markl, TU Berlin

Conference Website: http://saiconference.com/Computing The shortage of qualified

Scalable Data Science from Atlantis - Day 16 - Part 2

Scalable Data Science from Atlantis - Day 16 - Part 2

...

Scalable Data Science from Atlantis, Student Course Projects, Set 2

Scalable Data Science from Atlantis, Student Course Projects, Set 2

...

Scaling Up Data Science Applications with Kexin Xie and Yacov Salomon

Scaling Up Data Science Applications with Kexin Xie and Yacov Salomon

Krux, a Salesforce company, is a

Zepl: Scalable Data Science Platform on Amazon Web Services

Zepl: Scalable Data Science Platform on Amazon Web Services

Moon Lee of Zepl explains how the company uses Amazon ECS, Amazon EKS, and Amazon ECR to build a

Scalable Data Science from Atlantis - Day 22 - Part 3 (end of Andrew Morgan hangout & into DL)

Scalable Data Science from Atlantis - Day 22 - Part 3 (end of Andrew Morgan hangout & into DL)

...

Zeydy Ortiz  & Rob Montalvo | Scalable Data Science with Spark and R

Zeydy Ortiz & Rob Montalvo | Scalable Data Science with Spark and R

PyData Carolinas 2016 Processing large datasets in R have been limited by the amount of memory in the local system.