Course Outline
Introduction
The Concept of Big Data
Introduction to Spark
Introduction to Python
Introduction to PySpark
- Distributing data using the Resilient Distributed Datasets (RDD) framework
- Distributing computations via Spark API operators
Configuring Python for Spark
Setting up PySpark
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Configuring Databricks
Setting up an AWS EMR Cluster
Foundations of Python Programming
- Getting started with Python
- Working with Jupyter Notebook
- Managing variables and basic data types
- Handling lists
- Implementing conditional logic (if statements)
- Processing user input
- Using while loops
- Defining and using functions
- Object-oriented programming with classes
- Managing files and handling exceptions
- Working with projects, data, and APIs
Basics of Spark DataFrames
- Introduction to Spark DataFrames
- Performing fundamental operations in Spark
- Applying groupby and aggregate functions
- Handling timestamps and date data
Practical Project: Spark DataFrames
Machine Learning Concepts with MLlib
Integrating MLlib, Spark, and Python for Machine Learning
Regressions Explained
- Theory behind Linear Regression
- Coding regression evaluation metrics
- Practical exercise: Linear Regression
- Theory behind Logistic Regression
- Implementing Logistic Regression code
- Practical exercise: Logistic Regression
Random Forests and Decision Trees
- Theory of tree-based methods
- Coding Decision Trees and Random Forests
- Practical exercise: Random Forest Classification
K-means Clustering
- Theory of K-means Clustering
- Implementing K-means Clustering code
- Practical exercise: Clustering
Recommender Systems
Natural Language Processing (NLP)
- Understanding Natural Language Processing
- Overview of available NLP tools
- Practical exercise: NLP
Streaming with Spark and Python
- Overview of Spark Streaming
- Practical exercise: Spark Streaming
Requirements
- Foundational programming knowledge.
Target 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
