Course Outline
Introduction
Concepts of Big Data
Spark Fundamentals
Python Essentials
PySpark Overview
- Distributing data via the Resilient Distributed Datasets framework.
- Distributing computation using Spark API operators.
Configuring Python with Spark
Setting Up the PySpark Environment
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Initializing Databricks
Configuring the AWS EMR Cluster
Foundations of Python Programming
- Introduction to Python
- Working with Jupyter Notebooks
- Variables and Basic Data Types
- Handling Lists
- Conditional Logic with if Statements
- Processing User Input
- Iterating with while Loops
- Creating Functions
- Object-Oriented Programming with Classes
- Managing Files and Handling Exceptions
- Developing Projects with Data and APIs
Essentials of Spark DataFrames
- Introduction to Spark DataFrames
- Performing Basic Operations in Spark
- Applying Groupby and Aggregate Functions
- Managing Timestamps and Dates
Practical Spark DataFrame Project
Machine Learning Concepts with MLlib
Applying MLlib, Spark, and Python for Machine Learning
Exploring Regression Models
- Theoretical Basis of Linear Regression
- Writing Code for Regression Evaluation
- Practicing Linear Regression Exercises
- Theoretical Basis of Logistic Regression
- Implementing Logistic Regression Logic
- Practicing Logistic Regression Exercises
Random Forests and Decision Trees
- Understanding Tree-Based Methodologies
- Coding Decision Trees and Random Forests
- Practicing Random Forest Classification Exercises
K-means Clustering
- Theoretical Understanding of K-means Clustering
- Implementing K-means Clustering Algorithms
- Practicing Clustering Exercises
Recommender Systems
Implementing Natural Language Processing
- Foundations of Natural Language Processing (NLP)
- Survey of NLP Tools
- Practicing NLP Exercises
Streaming Data with Spark and Python
- Overview of Spark Streaming
- Sample Spark Streaming Project
Requirements
- Foundational programming proficiency.
Target 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
