Course Outline
Introduction
Comprehending Big Data
Spark Overview
Python Overview
PySpark Overview
- Distributing Data Using the Resilient Distributed Datasets (RDD) Framework
- Distributing Computation Using Spark API Operators
Configuring Python with Spark
Setting Up PySpark
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Configuring Databricks
Setting Up the AWS EMR Cluster
Mastering Fundamentals of Python Programming
- Getting Started with Python
- Utilizing the Jupyter Notebook
- Working with Variables and Basic Data Types
- Manipulating Lists
- Implementing Conditional Statements
- Handling User Inputs
- Working with while Loops
- Implementing Functions
- Working with Classes
- Managing Files and Exceptions
- Working with Projects, Data, and APIs
Exploring Spark DataFrames
- Introduction to Spark DataFrames
- Executing Basic Operations in Spark
- Applying Groupby and Aggregate Operations
- Handling Timestamps and Date Data
Spark DataFrame Project Exercise
Machine Learning with MLlib
Integrating MLlib, Spark, and Python for Machine Learning
Regression Analysis
- Theory of Linear Regression
- Implementing Regression Evaluation Code
- Linear Regression Practical Exercise
- Theory of Logistic Regression
- Implementing Logistic Regression Code
- Logistic Regression Practical Exercise
Random Forests and Decision Trees
- Theory Behind Tree-Based Methods
- Implementing Decision Trees and Random Forest Algorithms
- Random Forest Classification Practical Exercise
K-means Clustering
- Understanding K-means Clustering Theory
- Implementing K-means Clustering Code
- Clustering Practical Exercise
Recommender Systems
Implementing Natural Language Processing
- Concepts of Natural Language Processing (NLP)
- NLP Tooling Overview
- NLP Practical Exercise
Spark Streaming with Python
- Overview of 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
