Course Outline
Introduction
The Concept of Big Data
Introduction to Spark
Introduction to Python
Introduction to PySpark
- Data Distribution via the Resilient Distributed Datasets (RDD) Framework
- Distributed Computation Using Spark API Operators
Configuring Python with Spark
Setting Up the PySpark Environment
Deploying Spark Using Amazon Web Services (AWS) EC2 Instances
Configuring Databricks
Establishing an AWS EMR Cluster
Foundations of Python Programming
- Introduction to Python
- Utilizing the Jupyter Notebook
- Managing Variables and Basic Data Types
- Handling Lists
- Conditional Logic with if Statements
- Processing User Input
- Loop Control with while Statements
- Defining and Using Functions
- Object-Oriented Programming with Classes
- File Management and Exception Handling
- Managing Projects, Data, and APIs
Foundations of Spark DataFrames
- Introduction to Spark DataFrames
- Executing Basic Operations in Spark
- Applying GroupBy and Aggregation Functions
- Managing Timestamps and Dates
Practical Exercise: Spark DataFrame Project
Machine Learning Principles with MLlib
Applying MLlib, Spark, and Python for Machine Learning
Regression Analysis
- Theoretical Underpinnings of Linear Regression
- Developing Regression Evaluation Code
- Practical Exercise: Linear Regression
- Theoretical Underpinnings of Logistic Regression
- Developing Logistic Regression Code
- Practical Exercise: Logistic Regression
Decision Trees and Random Forests
- Theory of Tree-Based Methods
- Implementing Decision Tree and Random Forest Algorithms
- Practical Exercise: Random Forest Classification
K-means Clustering
- Theoretical Framework of K-means Clustering
- Implementing K-means Clustering Code
- Practical Exercise: Clustering Analysis
Recommender Systems
Natural Language Processing Implementation
- Concepts of Natural Language Processing (NLP)
- Survey of NLP Tools
- Practical Exercise: NLP Application
Real-Time Streaming with Spark and Python
- Overview of Spark Streaming
- Practical Exercise: Spark Streaming
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
