Apache Spark simplifies working with data at scale, making it faster to do machine learning on large data sets. Used by data professionals at Amazon, eBay, NASA, and 200+ other organizations, Spark’s community is one of the fastest growing in the world.
In this workshop, take your data skills to the next level by using Spark to build data pipelines. Workshop instructors will be on-hand all weekend to teach, live code, and help debug as we work through the course materials.
After completing this weekend workshop, you’ll be better prepared to use Spark for real projects and problems on your own. We’ll use Spark to power product recommendations and natural language processing tasks. It’s a great way to quickly ramp up your data skills in just 2 days.
This is an introductory course, so we don’t assume you know anything in particular about Spark. All you need to come to our workshop is a working knowledge of programming to get through the course materials, a laptop, and a readiness to learn. The more you know before the course, the more you’ll get out of it, so we do recommend the pre-course materials below:
December 3rd & 4th 2016, 9:00 AM – 5:00 PM (Lunch provided)
In this two-day, in-person, hands-on Spark course, we will:
Jean-François (Jeff) Omhover, Galvanize Data Science Instructor
Jeff is a Senior Data Scientist and Instructor in the Galvanize Data Science Immersive program. Prior to joining Galvanize, Jeff was an Assistant Professor at one of the leading engineering schools in France. He managed large-scale multidisciplinary research projects in partnership between industry and academia. He has used Spark and Natural Language Processing for mining consumer sentiment and brand perception from user comments, and for mining concepts from scientific papers.
Miles Erickson, Galvanize Data Science Associate Instructor
Miles is a Data Scientist and Associate Instructor in the Galvanize Data Science Immersive program. Before joining Galvanize, Miles worked as a systems/network engineering consultant and taught college-level classes in IT infrastructure and security. Miles has contributed to the development of widely recognized certification exams for server engineers. Miles is a graduate of the University of Washington and is a co-organizer of the local Python community in Seattle.
Spark is a powerful, open-source processing engine for data distributed across large clusters. Spark is optimized for speed and ease of use; it uses caching and memory to run distributed algorithms 100x faster than MapReduce. Spark can be used for batch processing and for processing data in near real-time.
Galvanize is the premiere dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:
Education – part-time and full-time training in web development, data science, and data engineering
Workspace – whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growth
Networking – events in the tech industry happen constantly in our campuses, ranging from popular Meetups to multi-day international conferences
To learn more about Galvanize, visit galvanize.com.
To learn more about our data science initiatives, please visit this link: http://www.galvanize.com/data-science/
This workshop series is for anyone who wants to use Spark to analyze data at scale.
Course examples and exercises will use Python and PySpark. Basic working knowledge of the Python programming language (i.e. the ability to write scripts and functions in Python) is required.
Basic knowledge of Unix commands (i.e. command line) is required.
We will use GitHub for sharing and maintaining code. If you do not already have a GitHub account, you will need to create one before class begins.
Students are expected to bring a personal computer running the Mac OS X or Linux operating system, with at least 4GB of RAM and at least 6GB of free disk space.
*Students are responsible for monitoring and controlling expenses incurred on their own Amazon Web Services accounts. A typical student is expected to spend less than $50 on AWS usage during this course. Amazon bills by the hour for usage of cloud resources and students are advised to shut down their Spark clusters at the end of the workshop. Accidentally leaving a Spark cluster running when it is not in use could result in significant overages.
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