Course Outline
Introduction
Concepts of Big Data
Spark Overview
Python Overview
Introduction to PySpark
- Data Distribution via Resilient Distributed Datasets (RDDs)
- Computation Distribution Using Spark API Operators
Configuring Python for Spark
Initializing PySpark
Deploying Spark on Amazon Web Services (AWS) EC2 Instances
Configuring Databricks
Setting Up the AWS EMR Cluster
Foundations of Python Programming
- Python Fundamentals
- Utilizing the Jupyter Notebook
- Variables and Basic Data Types
- Handling Lists
- Conditional Logic with if Statements
- Processing User Inputs
- Loop Construction with while Statements
- Defining and Using Functions
- Object-Oriented Programming with Classes
- File Management and Exception Handling
- Working with Projects, Data Structures, and APIs
Core Concepts of Spark DataFrames
- Getting Started with Spark DataFrames
- Executing Basic Operations in Spark
- Grouping and Aggregation Operations
- Managing Timestamps and Dates
Spark DataFrame Project Exercise
Machine Learning Fundamentals with MLlib
Applying MLlib, Spark, and Python for Machine Learning
Regressive Analysis
- Theory of Linear Regression
- Developing Regression Evaluation Code
- Practical Linear Regression Exercise
- Theory of Logistic Regression
- Developing Logistic Regression Code
- Practical Logistic Regression Exercise
Decision Trees and Random Forests
- Theory Behind Tree-Based Methods
- Implementing Decision Trees and Random Forests
- Random Forest Classification Exercise
K-means Clustering
- Understanding K-means Clustering Theory
- Implementing K-means Clustering Algorithms
- Clustering Practical Exercise
Recommender Systems
Natural Language Processing Implementation
- Foundations of Natural Language Processing (NLP)
- Survey of NLP Tools
- NLP Practical Exercise
Streaming with Spark and Python
- Overview of Spark Streaming
- Spark Streaming Practical Exercise
Requirements
- Fundamental 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
