Course Outline
Introduction
Foundations of Big Data
Introduction to Spark
Introduction to Python
Introduction to PySpark
- Data Distribution via the Resilient Distributed Datasets (RDD) Framework
- Computation Distribution Through Spark API Operators
Configuring Python with Spark
Setting Up the PySpark Environment
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Configuring Databricks
Establishing the AWS EMR Cluster
Core Python Programming Concepts
- Initiating Python Development
- Working with Jupyter Notebook
- Managing Variables and Basic Data Types
- Handling Lists
- Implementing Conditional Statements (if)
- Processing User Input
- Using While Loops
- Creating and Using Functions
- Working with Object-Oriented Classes
- Managing Files and Exception Handling
- Working with Projects, Data Structures, and APIs
Essentials of Spark DataFrames
- Initiating Work with Spark DataFrames
- Performing Basic Operations in Spark
- Executing Groupby and Aggregate Functions
- Handling Timestamps and Date Data
Practical Spark DataFrame Project
Machine Learning Fundamentals with MLlib
Integrating MLlib, Spark, and Python for Machine Learning
Regressive Analysis
- Linear Regression Theory
- Coding Regression Evaluation Metrics
- Linear Regression Practice Exercise
- Logistic Regression Theory
- Implementing Logistic Regression Code
- Logistic Regression Practice Exercise
Random Forests and Decision Trees
- Theoretical Foundations of Tree-Based Methods
- Implementing Code for Decision Trees and Random Forests
- Random Forest Classification Practice Exercise
K-means Clustering
- Theory of K-means Clustering
- Implementing K-means Clustering Algorithms
- Clustering Practice Exercise
Recommender Systems
Implementing Natural Language Processing
- Foundations of Natural Language Processing (NLP)
- Overview of NLP Toolkits
- NLP Practice Exercise
Spark Streaming with Python
- Introduction to Spark Streaming
- Spark Streaming Practice Exercise
Requirements
- Fundamental 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
