PySpark for Data Science – Advanced

Learn about how to use PySpark to perform data analysis, RFM analysis and Text mining

What you’ll learn

The skills related to development, big data, and the Hadoop ecosystem and the knowledge of Hadoop and analytics concepts are the tangible skills that you can learn from these PySpark Tutorials.
You will also learn how parallel programming and in-memory computation will be performed
Learn Recency Frequency Monetary segmentation (RFM). RFM analysis is typically used to identify outstanding customer groups further we shall also look at K-means clustering. Next up in these PySpark tutorials is learning Text Mining and using Monte Carlo Simulation from scratch.

The pre-requisite of these PySpark Tutorials is not much except for that the person should be well familiar and should have a great hands-on experience in any of the languages such as Java, Python or Scala or their equivalent. The other pre-requisites include the development background and the sound and fundamental knowledge of big data concepts and ecosystem as Spark API is based on top of big data Hadoop only. Others include the knowledge of real-time streaming and how big data works along with a sound knowledge of analytics and the quality of prediction related to the machine learning model.

The target audience for these PySpark Tutorials includes ones such as the developers, analysts, software programmers, consultants, data engineers, data scientists , data analysts, software engineers, Big data programmers, Hadoop developers. Other audience includes ones such as students and entrepreneurs who are looking to create something of their own in the space of big data.