Course Outline
Introduction
Understanding Big Data
Overview of Spark
Overview of Python
Overview of PySpark
- Distributing Data Using the Resilient Distributed Datasets (RDD) Framework
- Distributing Computation Using Spark API Operators
Setting Up Python with Spark
Setting Up PySpark
Using Amazon Web Services (AWS) EC2 Instances for Spark
Setting Up Databricks
Setting Up the AWS EMR Cluster
Fundamentals of Python Programming
- Getting Started with Python
- Using the Jupyter Notebook
- Using Variables and Simple Data Types
- Working with Lists
- Using if Statements
- Using User Inputs
- Working with while Loops
- Implementing Functions
- Working with Classes
- Handling Files and Exceptions
- Working with Projects, Data, and APIs
Fundamentals of Spark DataFrames
- Getting Started with Spark DataFrames
- Implementing Basic Operations in Spark
- Using GroupBy and Aggregate Operations
- Working with Timestamps and Dates
Spark DataFrame Project Exercise
Machine Learning with MLlib
Applying MLlib, Spark, and Python for Machine Learning
Regressions Explained
- Linear Regression Theory
- Implementing Regression Evaluation Code
- Sample Linear Regression Exercise
- Logistic Regression Theory
- Implementing Logistic Regression Code
- Sample Logistic Regression Exercise
Random Forests and Decision Trees
- Tree Methods Theory
- Implementing Decision Tree and Random Forest Codes
- Sample Random Forest Classification Exercise
K-means Clustering
- K-means Clustering Theory
- Implementing K-means Clustering Code
- Sample Clustering Exercise
Recommender Systems
Natural Language Processing Implementation
- Understanding Natural Language Processing (NLP)
- NLP Tools Overview
- Sample NLP Exercise
Streaming with Spark on Python
- Streaming with Spark Overview
- Sample Spark Streaming Exercise
Requirements
- General programming proficiency
Audience
- Developers
- IT Professionals
- Data Scientists
Testimonials (6)
I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.
Aurelia-Adriana - Allianz Services Romania
Course - Python and Spark for Big Data (PySpark)
The course was about a series of very complex related topics & Pablo has in-depth expertise of each of them. Sometimes nuances were lost in communication and/or due to time pressures and possibly expectations were not quite met due to this. Also there were some UHG/Azure Databricks setup issues however Pablo / UHG resolved these quickly once they became apparent - this to me showed a high level of understanding and professionalism between UHG & Pablo,
Michael Monks - Tech NorthWest Skillnet
Course - Python and Spark for Big Data (PySpark)
Individual attention.
ARCHANA ANILKUMAR - PPL
Course - Python and Spark for Big Data (PySpark)
Hands on Training..
Abraham Thomas - PPL
Course - Python and Spark for Big Data (PySpark)
The lessons were taught in a Jupyter notebook. The topics were structured with a logical sequence and naturally helped develop the session from the easier parts to the more complex. I'm already an advanced user of Python with background in Machine Learning, so found the course easier to follow than, possibly, some of my classmates that took the training course. I appreciate that some of the most elementary concepts were skipped and that he focused on the most substantial matters.
Angela DeLaMora - ADT, LLC
Course - Python and Spark for Big Data (PySpark)
practice tasks
