Data science and advanced analytics
Machine learning with TensorFlow
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.
Spark & beyond
Text Analysis and Mining
Spark foundations: Prototyping Spark use cases on Wikipedia datasets
Zoltan Toth (Databricks)
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.
Data science and advanced analytics, Spark & beyond
Data science at scale: Using Spark and Hadoop
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.