Pyspark Developer - Advanced

You get to learn about how to use spark python i.e PySpark to perform data analysis.

What you'll learn
The skills related to development, big data, and the Hadoop ecosystem and the knowledge of Hadoop and analytics concepts
You will also learn how parallel programming and in-memory computation will be performed.
we will be performing a Recency Frequency Monetary segmentation (RFM).
learning Text Mining and using Monte Carlo Simulation from scratch.
Requirements
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.
Description
What is PySpark?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 reduce 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.Which tangible skills will you learn in this Course?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. Apart from that, a different language Python will also be covered in this tutorial. Python is one of the most in-demand languages in the market today.

Overview

Section 1: Pyspark Advance

Lecture 1 Introduction to Pyspark Advance

Lecture 2 RFM Analysis

Lecture 3 RFM Analysis Continue

Lecture 4 K-Means Clustering

Lecture 5 K-Means Clustering Continue

Lecture 6 Image to Text

Lecture 7 PDF to Text

Lecture 8 Monte Carlo Simulation Part 1

Lecture 9 Monte Carlo Simulation Part 2

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.