Course Outline
Introduction
Concepts of Big Data
Spark Overview
Python Overview
Introduction to PySpark
- Distributing Data via Resilient Distributed Datasets (RDDs)
- Distributing Computation Using Spark API Operators
Configuring Python with Spark
Setting Up the PySpark Environment
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Databricks Configuration
Configuring the AWS EMR Cluster
Foundations of Python Programming
- Python Basics
- Working with Jupyter Notebook
- Managing Variables and Basic Data Types
- Handling Lists
- Conditional Logic with if Statements
- Processing User Inputs
- Looping with while Statements
- Defining and Using Functions
- Object-Oriented Programming with Classes
- File Handling and Exception Management
- Integrating Projects, Data, and APIs
Essentials of Spark DataFrames
- Getting Started with Spark DataFrames
- Performing Basic Operations in Spark
- GroupBy and Aggregation Techniques
- Handling Timestamps and Date Data
Practical Project: Spark DataFrames
Machine Learning Concepts with MLlib
Integrating MLlib, Spark, and Python for Machine Learning
Regression Analysis
- Linear Regression Theory
- Writing Code for Regression Evaluation
- Practical Linear Regression Exercise
- Logistic Regression Theory
- Implementing Logistic Regression Code
- Practical Logistic Regression Exercise
Random Forests and Decision Trees
- Tree-Based Methods Theory
- Implementing Decision Trees and Random Forests
- Practical Random Forest Classification Exercise
K-means Clustering
- K-means Clustering Theory
- Implementing K-means Clustering Algorithms
- Practical Clustering Exercise
Recommender Systems
Natural Language Processing (NLP)
- Core Concepts of NLP
- Overview of NLP Toolkits
- Practical NLP Exercise
Spark Streaming with Python
- Introduction to Spark Streaming
- Practical Spark Streaming Exercise
Requirements
- Foundational programming skills
Target Audience
- Software 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
