Data Science with Python and PySpark.

In this course you'll learn how to use Spark from Python! Spark is a tool for doing parallel computation with large datasets and it integrates well with Python. PySpark is the Python package that makes the magic happen. You'll use this package to work with data about flights from Portland and Seattle. You'll learn to wrangle this data and build a whole machine learning pipeline to predict whether or not flights will be late. Get ready to put some Spark in your Python code and dive into the world of high performance machine learning!
  •   Certificate : by TechSim+

What is PySpark?
PySpark helps data scientists interface with Resilient Distributed Datasets in apache spark and python. Py4J is a popularly library integrated within PySpark that lets python interface dynamically with JVM objects (RDD's).

Why should I learn this PySpark Course?
One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!

Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!

What skills will you learn?
This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we've done that we'll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you'll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem!

We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume!

Key Features

Master the Concept of your Training Module

20+ Real-Time Industry-Based Projects

Instructor-led training

Hands on Practical Classes

One Year Training Menbership with TechSim+

What You Will Learn
In this Journey

  • Stage 1

    1 Understandind Spark

  • Stage 2

    2 . Resilient Distributed Datasets

  • Stage 3

    3 . DataFrames

  • Stage 4

    4 . Prepare Data for Modeling

  • Stage 5

    5 . Introducing MLlib

  • Stage 6

    6 . Introducing the ML Package

  • Stage 7

    7 . Graph Frames

  • Stage 8

    8 . Tenso Frames

  • Stage 9

    9 . Polyglot Persistence With Blaze

  • Stage 10

    10 . Structured Streaming .

  • Stage 11

    11 Packaging Spark Applications