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AGILE DATA SCIENCE 2.0
Full-Stack Analytics Application Development with Kafka and Spark

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by Russell Jurney download a PDF brochure Download Event Brochure

Description

Agile Data Science 2.0 covers the theory and practice of an Agile development methodology created to enable analytics application development. Students will learn the theory and application of Agile Data Science, a development methodology in which a Data Scientist uses Agile methods and a lightweight stack to perform full-stack analytics application development. Students will learn how to define, implement and use a Big Data full stack, and how to roll their own Big Data applications from the ground up. This will enable them to effectively present their findings as applications, helping them make change within technological organizations. Students will emerge from this course with skills and a technological template from which to derive their own applications using their own datasets.

What you will learn

1. Participants will understand…

  • How to define “full-stacks” of Big Data tools
  • How to apply Agile methods to Data Science
  • Python/Flask Web development
  • Exploratory data analysis against Big Data

2. Participants will be able to…

  • Use full-stacks of Big Data tools
  • Work with some of the most popular Big Data tools: Python, Spark, Kafka, Elasticsearch, MongoDB
  • Build full-stack analytics applications
  • Build visualizations in d3.js
  • Build and deploy complete Predictive Analytics applications and systems
  • Build Web applications using Python/Flask
  • Explore Big Data interactively

Main Topics

  • Lecture: Agile Data Science
  • Lecture: Introducing the Analytics Stack
  • Demo: Walking Through our Full Stack
  • Exercise: Data Processing in PySpark
  • Exercise: Querying Data in MongoDB
  • Exercise: Creating a Web Service
  • Demo: Hacking Charts in d3.js
  • Exercise: Hacking Charts in d3.js
  • Lecture/Demo: Predictive Modeling in PySpark
  • Exercise: Predictive Modeling in PySpark
  • Lecture/Demo: Deploying Spark Predictive Models
  • Exercise: Deploying Spark Predictive Models
  • Lecture/Demo: Predictions on the Web
  • Exercise: Predictions on the Web
  • Discussion: Lessons Learned