Course Outline
Introduction
Comprehending Big Data
Spark Overview
Python Overview
PySpark Overview
- Data Distribution via the Resilient Distributed Datasets (RDD) Framework
- Distributing Computation through 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
- Getting Started with Python
- Utilizing the Jupyter Notebook
- Managing Variables and Basic Data Types
- Handling Lists
- Applying if Statements
- Incorporating User Inputs
- Implementing while Loops
- Defining Functions
- Working with Classes
- Managing Files and Handling Exceptions
- Integrating Projects, Data, and APIs
Basics of Spark DataFrames
- Intro to Spark DataFrames
- Executing Basic Operations in Spark
- Applying Groupby and Aggregation Operations
- Handling Timestamps and Dates
Spark DataFrame Project Exercise
Machine Learning Insights with MLlib
Implementing Machine Learning with MLlib, Spark, and Python
Regression Analysis
- Linear Regression Theory
- Developing Regression Evaluation Code
- Practical Linear Regression Exercise
- Logistic Regression Theory
- Implementing Logistic Regression Code
- Practical Logistic Regression Exercise
Random Forests and Decision Trees
- Tree Methods Theory
- Building Decision Trees and Random Forests
- Random Forest Classification Exercise
K-means Clustering
- K-means Clustering Theory
- Writing K-means Clustering Code
- Clustering Exercise
Recommender Systems
Natural Language Processing Implementation
- Foundations of Natural Language Processing (NLP)
- NLP Toolset Overview
- NLP Practical Exercise
Streaming with Spark in Python
- Introduction to Spark Streaming
- Spark Streaming Practical 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
