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