Course Outline
Introduction
Comprehending Big Data
Spark Overview
Python Overview
PySpark Overview
- Distributing Data via the Resilient Distributed Datasets (RDD) Framework
- Distributing Computation Through Spark API Operators
Configuring Python with Spark
Setting Up PySpark
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Setting Up Databricks
Configuring the AWS EMR Cluster
Mastering Python Programming Fundamentals
- Python Fundamentals
- Utilizing Jupyter Notebook
- Managing Variables and Basic Data Types
- Manipulating Lists
- Implementing if Statements
- Handling User Inputs
- Utilizing while Loops
- Creating and Using Functions
- Defining and Working with Classes
- File Handling and Exception Management
- Managing Projects, Data, and APIs
Essentials of Spark DataFrames
- Introduction to Spark DataFrames
- Performing Basic Operations in Spark
- Using Groupby and Aggregate Operations
- Handling Timestamps and Dates
Spark DataFrame Project Exercise
Machine Learning Concepts with MLlib
Integrating MLlib, Spark, and Python for Machine Learning
Regressive Analysis
- Linear Regression Theory
- Writing Regression Evaluation Code
- Sample Linear Regression Exercise
- Logistic Regression Theory
- Implementing Logistic Regression Code
- Sample Logistic Regression Exercise
Random Forests and Decision Trees
- Tree-Based Methods Theory
- Coding Decision Trees and Random Forests
- Sample Random Forest Classification Exercise
K-means Clustering
- K-means Clustering Theory
- Implementing K-means Clustering Code
- Sample Clustering Exercise
Recommender Systems
Natural Language Processing (NLP) Implementation
- Concepts of Natural Language Processing (NLP)
- Overview of NLP Toolsets
- Sample NLP Exercise
Python-based Spark Streaming
- Spark Streaming Overview
- 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
