Course Outline
Introduction
Concepts of Big Data
Spark Overview
Python Overview
PySpark Overview
- Data distribution via Resilient Distributed Datasets (RDD) framework
- Computation distribution using Spark API operators
Configuring Python with Spark
Setting up the PySpark environment
Leveraging Amazon Web Services (AWS) EC2 instances for Spark
Databricks setup
Configuring AWS EMR clusters
Foundations of Python Programming
- Initiating Python development
- Utilizing Jupyter Notebook
- Managing variables and basic data types
- Handling lists
- Implementing conditional logic (if statements)
- Processing user input
- Executing while loops
- Defining and using functions
- Object-oriented programming with classes
- File handling and exception management
- Interacting with projects, data structures, and APIs
Basics of Spark DataFrames
- Introduction to Spark DataFrames
- Executing fundamental operations in Spark
- Applying GroupBy and aggregation functions
- Handling timestamps and date data
Practical Spark DataFrame Project
Machine Learning Fundamentals with MLlib
Integrating MLlib, Spark, and Python for ML Workflows
Regression Analysis
- Theoretical foundations of Linear Regression
- Coding regression evaluation models
- Practical Linear Regression exercise
- Theoretical foundations of Logistic Regression
- Coding Logistic Regression models
- Practical Logistic Regression exercise
Random Forests and Decision Trees
- Theory behind tree-based methods
- Implementation of Decision Trees and Random Forests
- Practical Random Forest classification exercise
K-means Clustering Implementation
- Theoretical basis of K-means Clustering
- Coding K-means Clustering algorithms
- Practical clustering exercise
Recommender Systems Development
Implementing Natural Language Processing
- Core concepts of Natural Language Processing (NLP)
- Survey of available NLP tools
- Practical NLP exercise
Real-time Streaming with Spark and Python
- Overview of Spark Streaming capabilities
- Practical Spark Streaming exercise
Requirements
- Foundational programming proficiency
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
