Course Outline
Introduction
Understanding Big Data
Spark Overview
Python Overview
PySpark Overview
- Data Distribution via the Resilient Distributed Datasets Framework
- Computation Distribution 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
- Python Essentials
- Utilizing the Jupyter Notebook
- Variables and Basic Data Types
- List Operations
- Conditional Statements (if)
- User Input Handling
- While Loops
- Function Implementation
- Class Construction
- File Handling and Exception Management
- Project Management, Data, and APIs
Spark DataFrame Fundamentals
- Introduction to Spark DataFrames
- Basic Operations in Spark
- Groupby and Aggregation Operations
- Handling Timestamps and Dates
Spark DataFrame Project Exercise
Machine Learning with MLlib
Machine Learning with MLlib, Spark, and Python
Regressions Explained
- Linear Regression Theory
- Regression Evaluation Code Implementation
- Sample Linear Regression Exercise
- Logistic Regression Theory
- Logistic Regression Code Implementation
- Sample Logistic Regression Exercise
Random Forests and Decision Trees
- Tree Methods Theory
- Implementing Decision Trees and Random Forests
- Sample Random Forest Classification Exercise
K-means Clustering
- K-means Clustering Theory
- Implementing K-means Clustering Code
- Sample Clustering Exercise
Recommender Systems
Natural Language Processing Implementation
- Understanding Natural Language Processing (NLP)
- Overview of NLP Tools
- Sample NLP Exercise
Streaming with Spark on Python
- Spark Streaming Overview
- Sample Spark Streaming Exercise
Requirements
- Basic programming proficiency
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
