Course Outline
Introduction
The Concept of Big Data
Introduction to Spark
Introduction to Python
Introduction to PySpark
- Data Distribution via the Resilient Distributed Datasets (RDD) Framework
- Distributing Computation Using Spark API Operators
Configuring Python for Spark
Setting Up the PySpark Environment
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Configuring Databricks
Establishing an AWS EMR Cluster
Foundations of Python Programming
- Starting with Python
- Utilizing the Jupyter Notebook
- Managing Variables and Basic Data Types
- Handling Lists
- Using Conditional Statements (if)
- Processing User Inputs
- Implementing while Loops
- Defining Functions
- Working with Classes
- Managing Files and Exceptions
- Working with Projects, Data, and APIs
Basics of Spark DataFrames
- Getting Started with Spark DataFrames
- Executing Basic Operations in Spark
- Performing Groupby and Aggregation Operations
- Handling Timestamps and Dates
Practical Spark DataFrame Project Exercise
Machine Learning Fundamentals with MLlib
Applying MLlib, Spark, and Python for Machine Learning
Exploring Regression Models
- Understanding Linear Regression Theory
- Writing Code for Regression Evaluation
- Completing a Linear Regression Sample Exercise
- Understanding Logistic Regression Theory
- Implementing Logistic Regression Logic
- Completing a Logistic Regression Sample Exercise
Random Forests and Decision Trees
- Reviewing Tree Method Theory
- Coding Decision Trees and Random Forests
- Completing a Random Forest Classification Sample Exercise
K-means Clustering
- Reviewing K-means Clustering Theory
- Implementing K-means Clustering Code
- Completing a Clustering Sample Exercise
Recommender Systems
Natural Language Processing Implementation
- Understanding Natural Language Processing (NLP)
- Overview of NLP Tooling
- Completing a Sample NLP Exercise
Streaming with Spark and Python
- Introduction to Spark Streaming
- Sample Spark Streaming Exercise
Requirements
- Fundamental programming knowledge
Intended Audience
- Software Developers
- IT Specialists
- 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
