Course Outline
Introduction
Understanding Big Data
Introduction to Spark
Introduction to Python
Introduction to PySpark
- Distributing Data via 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
Fundamentals of Python Programming
- Getting Started with Python
- Using Jupyter Notebooks
- Managing Variables and Basic Data Types
- Handling Lists
- Using Conditional (if) Statements
- Processing User Input
- Utilizing while Loops
- Creating Functions
- Working with Classes
- Managing Files and Exceptions
- Interacting with Projects, Data, and APIs
Fundamentals of Spark DataFrames
- Getting Started with Spark DataFrames
- Performing Basic Operations in Spark
- Applying Groupby and Aggregation Operations
- Handling Timestamps and Dates
Spark DataFrame Project Exercise
Machine Learning Concepts with MLlib
Machine Learning with MLlib, Spark, and Python
Regression Analysis
- Linear Regression Theory
- Writing Regression Evaluation Code
- Linear Regression Practice Exercise
- Logistic Regression Theory
- Implementing Logistic Regression Code
- Logistic Regression Practice Exercise
Random Forests and Decision Trees
- Tree-Based Methodologies Theory
- Implementing Decision Trees and Random Forests
- Random Forest Classification Exercise
K-means Clustering
- K-means Clustering Theory
- Implementing K-means Clustering Code
- Clustering Practice Exercise
Recommender Systems
Natural Language Processing Implementation
- Concepts of Natural Language Processing (NLP)
- Overview of NLP Tools
- NLP Practice Exercise
Stream Processing with Spark and Python
- Overview of Spark Streaming
- Spark Streaming Practice Exercise
Requirements
- General programming proficiency
Audience
- Software Developers
- IT Specialists
- 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
