Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

In-Person Training
Data science at scale: Using Spark and Hadoop

Monday, March 13 & Tuesday, March 14, 9:00am - 5:00pm
See pricing & packages
Early Price ends January 20

This course will sell out—sign up today!

Participants should plan to attend both days of this 2-day training course. Platinum and Training passes do not include access to tutorials on Tuesday.

Learn how Spark and Hadoop enable data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities. Through in-class simulations and exercises, Bruce Martin walks you through applying data science methods to real-world challenges in different industries, offering preparation for data scientist roles in the field.

What you'll learn, and how you can apply it

  • Learn how Spark and Hadoop enable data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities

Data scientists build information platforms to provide deep insight and answer previously unimaginable questions. Spark and Hadoop are transforming how data scientists work by allowing interactive and iterative data analysis at scale. Learn how Spark and Hadoop enable data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities.

Bruce Martin explores what data scientists do, the problems they solve, and the tools and techniques they use. Through in-class simulations and exercises, Bruce walks you through applying data science methods to real-world challenges in different industries, offering preparation for and experience with data scientist roles in the field.

Topics include:

  • How to identify potential business use cases where data science can provide impactful results
  • How to obtain, clean, and combine disparate data sources to create a coherent picture for analysis
  • What statistical methods to leverage for data exploration that will provide critical insight into your data
  • Where and when to leverage Hadoop streaming and Apache Spark for data science pipelines
  • What machine-learning technique to use for a particular data science project
  • How to implement and manage recommenders using Spark’s MLlib and how to set up and evaluate data experiments
  • The pitfalls of deploying new analytics projects to production at scale

Conference registration

Get the Platinum pass or the Training pass to add this course to your package. Early Price ends January 20.

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