Course Outline
Introduction
Comprehending Big Data
Spark Overview
Python Overview
PySpark Overview
- Distributing Data via the Resilient Distributed Datasets Framework
- Distributing Computation through Spark API Operators
Configuring Python with Spark
Setting Up PySpark
Leveraging Amazon Web Services (AWS) EC2 Instances for Spark
Configuring Databricks
Setting Up the AWS EMR Cluster
Foundations of Python Programming
- Initiating Python Development
- Utilizing the Jupyter Notebook
- Employing Variables and Basic Data Types
- Managing Lists
- Using Conditional Statements
- Handling User Inputs
- Utilizing while Loops
- Defining Functions
- Implementing Classes
- Managing Files and Exceptions
- Working with Projects, Data, and APIs
Essentials of Spark DataFrame
- Getting Started with Spark DataFrames
- Performing Basic Operations in Spark
- Applying Groupby and Aggregate Operations
- Handling Timestamps and Dates
Spark DataFrame Project Exercise
Machine Learning Concepts with MLlib
Integrating MLlib, Spark, and Python for Machine Learning
Exploring Regressions
- Theory of Linear Regression
- Coding Regression Evaluation
- Linear Regression Practical Exercise
- Theory of Logistic Regression
- Implementing Logistic Regression Code
- Logistic Regression Practical Exercise
Random Forests and Decision Trees
- Tree Methods Theory
- Coding Decision Trees and Random Forests
- 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)
- NLP Tools Overview
- Sample NLP Exercise
Streaming with Spark on Python
- Overview of Streaming with Spark
- Spark Streaming Practical Exercise
Requirements
- General 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
