The real power and value proposition of Apache Spark is its speed and platform to execute Data Science tasks. Spark's unique use case is that it combines ETL, batch analytic, real-time stream analysis, machine learning, graph processing, and visualizations to allow Data Scientists to tackle the complexities that come with raw unstructured data sets. Spark embraces this approach and has the vision to make the transition from working on a single machine to working on a cluster, something that makes data science tasks a lot more agile.

In this course, you’ll get a hands-on technical resource that will enable you to become comfortable and confident working with Spark for Data Science. We won't just explore Spark’s Data Science libraries, we’ll dive deeper and expand on the topics.

This course starts by taking you through Spark and the needed steps to build machine learning applications. You will learn to collect, clean, and visualize data coming from Twitter with Spark streaming. Then, you will get acquainted with Spark Machine learning algorithms and different machine learning techniques. You will also learn to apply statistical analysis and mining operations on our Tweet dataset. Finally, the course will end by give you some ideas on how to perform awesome analysis including graph processing. By the end of the course, you will be able to do your Data scientist job in a very visual way, comprehensive and appealing for business and other stakeholders.

Style and Approach

This practical hands-on tutorial covers the fundamentals of Spark needed to get grips with Data Science through a single data set. It expands on the next learning curve for those comfortable with Spark programming who are looking to apply Spark in the field of Data Science.