Course Outline
Introduction
Understanding Big Data
Spark Overview
Python Overview
PySpark Overview
- Distributing Data Using the Resilient Distributed Datasets Framework
- Distributing Computation Using Spark API Operators
Configuring Python with Spark
Configuring PySpark
Using Amazon Web Services (AWS) EC2 Instances for Spark
Setting Up Databricks
Configuring the AWS EMR Cluster
Mastering the Fundamentals of Python Programming
- Getting Started with Python
- Utilising the Jupyter Notebook
- Managing Variables and Basic Data Types
- Handling Lists
- Implementing Conditional Statements (if)
- Processing User Input
- Using while Loops
- Defining Functions
- Creating Classes
- Managing Files and Exceptions
- Working with Projects, Data, and APIs
Mastering the Fundamentals of Spark DataFrames
- Getting Started with Spark DataFrames
- Performing Basic Operations in Spark
- Utilising Groupby and Aggregation Operations
- Handling Timestamps and Dates
Spark DataFrame Project Exercise
Comprehending Machine Learning with MLlib
Applying MLlib, Spark, and Python for Machine Learning
Understanding Regression
- Studying Linear Regression Theory
- Developing Regression Evaluation Code
- Completing a Sample Linear Regression Exercise
- Studying Logistic Regression Theory
- Developing Logistic Regression Code
- Completing a Sample Logistic Regression Exercise
Understanding Random Forests and Decision Trees
- Studying Tree Methods Theory
- Developing Code for Decision Trees and Random Forests
- Completing a Sample Random Forest Classification Exercise
Working with K-means Clustering
- Understanding K-means Clustering Theory
- Developing K-means Clustering Code
- Completing a Sample Clustering Exercise
Implementing Recommender Systems
Applying Natural Language Processing
- Understanding Natural Language Processing (NLP)
- Overview of NLP Tools
- Completing a Sample NLP Exercise
Streaming with Spark on Python
- Overview of Streaming with Spark
- Sample Spark Streaming Exercise
Requirements
- General programming skills
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
