Course Outline
Introduction
Grasping the Concept of 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 for Spark
Setting Up PySpark
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Configuring Databricks
Setting Up the AWS EMR Cluster
Mastering Python Programming Fundamentals
- Getting Started with Python
- Utilizing the Jupyter Notebook
- Managing Variables and Basic Data Types
- Handling Lists
- Implementing Conditional Logic (if Statements)
- Processing User Inputs
- Executing while Loops
- Defining Functions
- Working with Object-Oriented Classes
- Handling Files and Exceptions
- Managing Projects, Data, and APIs
Exploring Spark DataFrame Essentials
- Introduction to Spark DataFrames
- Performing Core Operations in Spark
- Applying Groupby and Aggregate Functions
- Managing Timestamps and Date Data
Practical Spark DataFrame Project Exercise
Machine Learning Fundamentals with MLlib
Integrating MLlib, Spark, and Python for Machine Learning
Regression Analysis
- Understanding Linear Regression Concepts
- Developing Regression Evaluation Code
- Completing a Linear Regression Practical Exercise
- Understanding Logistic Regression Concepts
- Developing Logistic Regression Code
- Completing a Logistic Regression Practical Exercise
Random Forests and Decision Trees
- Understanding Tree-Based Methodologies
- Implementing Decision Tree and Random Forest Algorithms
- Completing a Random Forest Classification Exercise
K-means Clustering
- Understanding K-means Clustering Theory
- Implementing K-means Clustering Algorithms
- Completing a Clustering Practical Exercise
Recommender Systems
Natural Language Processing (NLP)
- Introduction to Natural Language Processing (NLP)
- Overview of NLP Toolsets
- Completing a Practical NLP Exercise
Streaming Data with Spark and Python
- Overview of Spark Streaming
- Practical Spark Streaming Exercise
Requirements
- Foundational programming experience.
Intended 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
