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