Course Outline
Introduction
Concepts of 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
Configuring Databricks
Setting Up an AWS EMR Cluster
Foundations of Python Programming
- Introduction to Python
- Utilizing the Jupyter Notebook
- Handling Variables and Basic Data Types
- Managing Lists
- Implementing Conditional Logic (if Statements)
- Processing User Inputs
- Implementing Loop Structures (while Loops)
- Defining Functions
- Object-Oriented Programming with Classes
- File Handling and Exception Management
- Integrating Projects, Data, and APIs
Essentials of Spark DataFrames
- Introduction to Spark DataFrames
- Executing Basic Operations in Spark
- Applying Groupby and Aggregation Functions
- Handling Timestamps and Dates
Project Exercise: Spark DataFrame
Machine Learning Concepts with MLlib
Practical Machine Learning with MLlib, Spark, and Python
Regressive Analysis
- Linear Regression Theory
- Writing Code for Regression Evaluation
- Practical Exercise: Linear Regression
- Logistic Regression Theory
- Implementing Logistic Regression Code
- Practical Exercise: Logistic Regression
Ensemble Methods: Random Forests and Decision Trees
- Theory of Tree-Based Methods
- Coding Decision Trees and Random Forests
- Practical Exercise: Random Forest Classification
K-means Clustering
- K-means Clustering Theory
- Implementing K-means Clustering Code
- Practical Exercise: Clustering
Recommender Systems
Implementing Natural Language Processing
- Concepts of Natural Language Processing (NLP)
- Survey of NLP Tools
- Practical Exercise: NLP
Streaming Data with Spark on Python
- Introduction to Spark Streaming
- Practical Exercise: Spark Streaming
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
