Course Outline
Introduction
Comprehending Big Data
Spark Overview
Python Overview
PySpark Overview
- Distributing Data via the Resilient Distributed Datasets Framework
- Distributing Computation 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
- Getting Started with Python
- Working with Jupyter Notebooks
- Using Variables and Simple Data Types
- Managing Lists
- Implementing if Statements
- Handling User Inputs
- Working with while Loops
- Defining Functions
- Working with Classes
- Handling Files and Exceptions
- Working with Projects, Data, and APIs
Essentials of Spark DataFrame
- Getting Started with Spark DataFrames
- Performing Basic Operations with Spark
- Applying Groupby and Aggregate Operations
- Handling Timestamps and Dates
Spark DataFrame Project Exercise
Machine Learning Concepts with MLlib
Machine Learning using MLlib, Spark, and Python
Regression Analysis
- Understanding Linear Regression Theory
- Writing Regression Evaluation Code
- Linear Regression Sample Exercise
- Understanding Logistic Regression Theory
- Implementing Logistic Regression Code
- Logistic Regression Sample Exercise
Random Forests and Decision Trees
- Tree Methods Theory
- Implementing Decision Trees and Random Forest Code
- Random Forest Classification Sample Exercise
K-means Clustering
- K-means Clustering Theory
- Implementing K-means Clustering Code
- Sample Clustering Exercise
Recommender Systems
Natural Language Processing Implementation
- Concepts of Natural Language Processing (NLP)
- NLP Tools Overview
- Sample NLP Exercise
Spark Streaming in Python
- Overview of Spark Streaming
- Sample Spark Streaming Exercise
Requirements
- Foundational programming skills.
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
