Course Outline
Introduction
Concepts of 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
Configuring PySpark
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Setting Up Databricks
Configuring the AWS EMR Cluster
Foundations of Python Programming
- Introduction to Python.
- Working with the Jupyter Notebook.
- Managing Variables and Basic Data Types.
- Handling Lists.
- Implementing Conditional Statements.
- Processing User Inputs.
- Utilizing While Loops.
- Defining Functions.
- Working with Classes.
- Handling Files and Exceptions.
- Interacting with Projects, Data, and APIs.
Spark DataFrame Fundamentals
- Introduction to Spark DataFrames.
- Executing Basic Operations in Spark.
- Applying Groupby and Aggregate Operations.
- Managing Timestamps and Dates.
Practical Spark DataFrame Project Exercise
Machine Learning Concepts with MLlib
Integrating MLlib, Spark, and Python for Machine Learning
Regression Analysis
- Linear Regression Theory.
- Implementing Regression Evaluation Code.
- 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 Tree and Random Forest Code.
- Random Forest Classification Exercise.
K-means Clustering
- K-means Clustering Theory.
- Implementing K-means Clustering Code.
- Clustering Exercise.
Recommender Systems
Natural Language Processing Implementation
- Natural Language Processing (NLP) Concepts.
- Overview of NLP Tools.
- NLP Practical Exercise.
Spark Streaming with Python
- Spark Streaming Overview.
- Spark Streaming Practical Exercise.
Requirements
- Fundamental programming skills.
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
