PySpark for Data Science – Beginners

Learn the basics of Apache Spark and how to use Python in PySpark to analyze big data for machine learning

What you’ll learn

  • Learn bout the concept of RDDs and other very basic features and terminologies being used in the case of Spark
  • You will also understand what are the benefits and disadvantages of using Spark
  • Use Python with Big Data on Apache Spark
  • These PySpark Tutorials aims to explain the basics of Apache Spark and the essentials related to it


  • This PySpark Tutorial does not have any pre-requisites other than that the individual should have good familiarity with a language such as Java, Python or Scala or its equivalent. Another prerequisite is that you have a background in development and a solid and fundamental understanding of big data concepts and ecosystems, since Spark API is built solely on Hadoop. A solid understanding of analytics and machine learning models is included as well as an understanding of real-time streaming and big data.


In these PySpark Tutorials, we will explain the basics of Apache Spark, as well as the essentials associated with it. In addition to that, it also discusses why Apache Spark is better than Hadoop and that it is the best system for real-time processing. Additionally, you will also learn about the advantages and disadvantages of using Spark with all of the above-mentioned languages. In addition, you will become familiar with the basic features and terms of Spark. The aim of this PySpark for Data Science – Beginners course is to give students, professionals, and aspiring data scientists hands-on training in PySpark (Python for Apache Spark) using real-world datasets and applicable coding skills they’ll need as a data scientist every day.

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Pyspark is a big data solution that is applicable for real-time streaming with Python programming language. It provides a more efficient and reliable way to do all kinds of calculations and computations. Moreover, it has the advantage of being interoperable, meaning it allows for easy integration with other systems on the market. Pyspark can be managed in conjunction with other technologies and other components of the entire pipeline. An earlier version of the big data and Hadoop techniques included batch processing techniques.

PythonSpark is an open-source program whose entire codebase is written in Python that is used to perform all kinds of data-intensive and machine learning tasks. Pyspark has been widely used and has started to become popular in the industry, which has resulted in it replacing other spark-based components such as those working with Java or Scala. Among the unique features of Pyspark is that datasets can be used as opposed to data frames since Pyspark doesn’t provide data frames. There is a need for tools that are more reliable and faster when streaming data in real-time. Prior tools, such as Map-Reduce, used the map and reduced concepts, which involved using the mappers, shuffling or sorting them, and then reducing them to a single entity. Parallel computation and calculation were made possible through MapReduce. This software uses in-memory computing techniques that do not require the hard disk space to be utilized. In addition to providing general purpose functionality, it provides a faster computation unit.

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Who this course is for:

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

Created by Exam Turf
Last updated 7/2021
Size: 878 MB

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