Course Outline
Introduction
The Landscape of Big Data
Introduction to Spark
Introduction to Python
Introduction to PySpark
- Handling Data Distribution via the Resilient Distributed Datasets Framework
- Distributing Computation through Spark API Operators
Configuring Python for Spark
Setting Up the PySpark Environment
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Configuring Databricks
Establishing an AWS EMR Cluster
Foundations of Python Programming
- Python Essentials
- Utilizing Jupyter Notebooks
- Handling Variables and Basic Data Types
- Manipulating Lists
- Implementing Conditional Logic (If Statements)
- Managing User Inputs
- Using While Loops
- Defining and Using Functions
- Object-Oriented Programming with Classes
- File Handling and Exception Management
- Interacting with Projects, Data Structures, and APIs
Essentials of Spark DataFrames
- Getting Started with Spark DataFrames
- Performing Core Operations in Spark
- Applying Groupby and Aggregation Techniques
- Managing Timestamps and Date Objects
Practical Application: Spark DataFrame Project
Machine Learning Fundamentals with MLlib
Integrating MLlib, Spark, and Python for Machine Learning Tasks
Exploring Regression Analysis
- Theoretical Basis of Linear Regression
- Writing Code for Regression Evaluation
- Practical Exercise: Linear Regression
- Concepts Behind Logistic Regression
- Implementing Logistic Regression Algorithms
- Practical Exercise: Logistic Regression
Random Forests and Decision Tree Algorithms
- Theoretical Framework for Tree-based Methods
- Coding for Decision Trees and Random Forests
- Practical Exercise: Random Forest Classification
Implementing K-means Clustering
- Theory of K-means Clustering
- Developing K-means Clustering Solutions
- Practical Exercise: Clustering Algorithms
Building Recommender Systems
Implementing Natural Language Processing
- Core Concepts of Natural Language Processing (NLP)
- Review of NLP Toolsets
- Practical Exercise: NLP Application
Streaming Data with Spark and Python
- Introduction to Spark Streaming
- Practical Exercise: Spark Streaming Implementation
Requirements
- Fundamental programming skills
Intended 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
