PySpark for Data Science – Advanced

This module in the PySpark tutorials section will help you learn about certain advanced concepts of PySpark. In the first section of these advanced tutorials, we will be performing a 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.

Pyspark is a big data solution that is applicable for real–time streaming using Python programming language and provides a better and efficient way to do all kinds of calculations and computations. It is also probably the best solution in the market as it is interoperable i.e. Pyspark can easily be managed along with other technologies and other components of the entire pipeline. The earlier big data and Hadoop techniques included batch time processing techniques.

Pyspark is an open–source program where all the codebase is written in Python which is used to perform mainly all the data–intensive and machine learning operations. It has been widely used and has started to become popular in the industry and therefore Pyspark can be seen replacing other spark–based components such as the ones working with Java or Scala. One unique feature which comes along with Pyspark is the use of datasets and not data frames as the latter is not provided by Pyspark. Practitioners need more tools that are often more reliable and faster when it comes to streaming real–time data. The earlier tools such as Map–reduce made use of the map and the reduced concepts which included using the mappers, then shuffling or sorting, and then reducing them into a single entity. This MapReduce provided a way of parallel computation and calculation. The Pyspark makes use of in–memory techniques that don’t make use of the space storage being put into the hard disk. It provides a general purpose and a faster computation unit.