Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Strata Data Conference 2-day training courses

All training courses take place 9:00 - 17:00, Monday, 22 May through Tuesday, 23 May. In order to maintain a high level of hands-on learning and instructor interaction, each training is limited in size.

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

Monday, 22 May - Tuesday, 23 May

Add to your personal schedule
9:00 - 17:00 Monday, 22 May & Tuesday, 23 May
Location: Capital Suite 16
Kai Voigt (Cloudera)
Learn how Spark and Hadoop enable data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities. Using in-class simulations and exercises, Kai Voigt walks you through applying data science methods to real-world challenges in different industries, offering preparation for data scientist roles in the field. Read more.
Add to your personal schedule
9:00 - 17:00 Monday, 22 May & Tuesday, 23 May
Location: Capital Suite 17
Secondary topics:  Deep learning
Robert Schroll (The Data Incubator)
Robert Schroll demonstrates TensorFlow's capabilities through its Python interface, walking you through building machine-learning algorithms piece by piece and using the higher-level abstractions provided by TensorFlow. You'll then use this knowledge to build machine-learning models on real-world data. Read more.
Add to your personal schedule
9:00 - 17:00 Monday, 22 May & Tuesday, 23 May
Location: Capital Suite 7
Secondary topics:  Text Analysis and Mining
Zoltan Toth (Databricks)
Average rating: ****.
(4.00, 2 ratings)
The real power and value proposition of Apache Spark is in building a unified use case that combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. Zoltan Toth employs hands-on exercises using various Wikipedia datasets to illustrate the variety of ideal programming paradigms Spark makes possible. Read more.