Building Recommendation Systems in Python using Apache Spark
Details
IMPORTANT: RSVP to this free workshop via Eventbrite HERE. Meetup RSVP will not be counted. (https://www.eventbrite.com/e/building-recommendation-systems-in-python-using-apache-spark-tickets-29041582154?aff=de)
As data scientists, we find ourselves working with increasingly large and complex data in our day to day work. The standard toolset of a data scientist using R or Python on a single workstation has not evolved to meet this need. This talk will demonstrate that data scientists can work with large datasets using Python by leveraging the power of Apache Spark.
How do large companies like Amazon, Netflix, and Facebook choose what content to show to their users? Recommendation systems are an essential tool used by companies to improve customer engagement by choosing the right content to present to each user. The instructors will walk through an example of building a product recommendation system on big data using Python and PySpark.
Who is This Workshop For?
This workshop is for anyone with a strong personal or professional interest in data science. This is an introductory workshop, so we don’t expect you to know anything in particular about Spark. All you need to come to our workshop is a working knowledge of Python programming to understand the demonstration, a laptop, and a readiness to learn.
This beginner workshop is designed to help people develop a foundational knowledge of Apache Spark. By the end of this free course, students will be familiar with the basics of using PySpark to load product review data and build a simple recommendation system.
Why Spark?
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 up to 100x faster than MapReduce. Spark can be used for batch processing and for processing data in near real-time.
Meet Your Instructors
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.
About Galvanize 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 support 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 (http://galvanize.com/).
To learn more about our data science initiatives, please visit this link: http://www.galvanize.com/data-science/ (http://www.galvanize.com/courses/data-science/)
IMPORTANT: RSVP to this free workshop via Eventbrite HERE. Meetup RSVP will not be counted.
Building Recommendation Systems in Python using Apache Spark