Best PySpark Courses 2020
Best PySpark Tutorials 2020
Spark and Python for Big Data with PySpark
Discover the latest Big Data technology – Spark! And learn how to use it with one of the most popular programming languages, Python!
One of the most valuable technology skills is the ability to analyze huge datasets, and this course is specially designed to introduce you to one of the best technologies for this task, Apache Spark! The biggest tech companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA and many more are all using Spark to solve their big data problems!
Spark can run up to 100 times 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 opportunity to quickly become one of the most knowledgeable people in the job market!
This course will teach the basics with a crash course in Python, continuing to learn how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we’ve done that, we’ll see how to use the MLlib machine library with DataFrame and Spark syntax. Along the way you will have simulated exercises and counseling projects that will put you directly in a real situation where you have to use your new skills to solve a real problem!
We also cover the latest Spark technologies, such as Spark SQL, Spark Streaming, and advanced models such as Gradient Boosted Trees! After completing this course, you will feel comfortable putting Spark and PySpark on your CV! This course also offers a full 30 day money back guarantee and comes with a LinkedIn certificate of
Data Science:Hands-on Diabetes Prediction with Pyspark MLlib
Pyspark is the collaboration of Apache Spark and Python. PySpark is a tool used in Big Data Analytics.
Apache Spark is an open source, clustered compute framework, built around speed, ease of use, and streaming analysis, while Python is a general purpose, high-level programming language. It provides a wide range of libraries and is primarily used for machine learning and real-time broadcast analysis.
In other words, it’s a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark to tame big data. We will be using Big Data tools in this project.
You will learn more in this hour of practice than hundreds of hours of useless theory lessons.
Check out the most important aspect of Spark Machine Learning (Spark MLlib):
Pyspark Fundamentals and Spark Machine Learning Implementation
Importing and Using Datasets
Process data using a machine learning model using Spark MLlib
Build and train a logistic regression model
Test and analyze the model
We are going to build a model to predict diabetes. This is a one hour project. In this hands-on project, we will perform the following tasks:
Task 1: project overview
Task 2: Introduction to the Colab environment and installation of the dependencies to run Spark on Colab
Task 3: Clone and Explore the Diabetes Data Set
Task 4: Data cleansing
Check for missing values
Replace unnecessary values
Task 5: Correlate and Select Features
Task 6: Create and Train a Logistic Regression Model Using Spark MLlib
Task 7: Evaluate the Performance and Test the Model
Task 8: Save and Load the Model
Big Data with Apache Spark PySpark: Hands on PySpark, Python
Apache Spark can run up to 100 times faster than the Hadoop MapReduce data processing framework, which makes Apache Spark one of the most requested skills.
Bigger companies like Google, Facebook, Microsoft, Amazon, Airbnb use Apache Spark to solve their big data problems !. Data analysis, on huge amounts of data, is one of the most valuable skills these days and this course will teach these kinds of skills to be completed in the big data job market.
This course will teach
Introduction to Big Data and Apache Spark
Getting started with databricks
Detailed installation step on Ubuntu – Linux Machine
Python Refresh for beginners
Apache Spark Dataframe API
Apache Spark Structured Streaming with End-to-End Example
Fundamentals of machine learning and feature engineering with Apache Spark.
This course is not finished, will add new content related to Spark ML.
Note: This course will teach only the API based on Spark 2.0 Dataframe and not the API based on RDD. As the Dataframe based API is the future of Spark.