Course Outline
Introduction
Comprehending Big Data
Spark Fundamentals
Python Fundamentals
PySpark Essentials
- Managing Data Distribution via Resilient Distributed Datasets (RDDs)
- Managing Computational Distribution using Spark API Operators
Configuring Python with Spark
Configuring the PySpark Environment
Utilizing Amazon Web Services (AWS) EC2 Instances for Spark
Databricks Setup
AWS EMR Cluster Configuration
Foundations of Python Programming
- Introduction to Python
- Utilizing Jupyter Notebook
- Managing Variables and Basic Data Types
- Handling Lists
- Implementing Conditional Statements
- Processing User Input
- Utilizing While Loops
- Defining and Using Functions
- Working with Object-Oriented Classes
- Handling Files and Exceptions
- Integrating Projects, Data, and APIs
Essentials of Spark DataFrames
- Getting Started with Spark DataFrames
- Performing Basic Spark Operations
- Using GroupBy and Aggregation Functions
- Handling Timestamps and Date Data
Practical Project: Spark DataFrames
Machine Learning Concepts with MLlib
Implementing Machine Learning with MLlib, Spark, and Python
Regressive Analysis
- Theory of Linear Regression
- Writing Regression Evaluation Logic
- Exercise: Sample Linear Regression
- Theory of Logistic Regression
- Writing Logistic Regression Logic
- Exercise: Sample Logistic Regression
Random Forests and Decision Tree Algorithms
- Theory Behind Tree-Based Methods
- Coding Decision Trees and Random Forests
- Exercise: Random Forest Classification
K-means Clustering Implementation
- Theory of K-means Clustering
- Coding K-means Clustering Logic
- Exercise: Sample Clustering Task
Building Recommender Systems
Applying Natural Language Processing
- Concepts of Natural Language Processing (NLP)
- Survey of NLP Toolkits
- Exercise: Sample NLP Task
Real-Time Streaming with Spark and Python
- Overview of Spark Streaming
- Exercise: Sample Spark Streaming Task
Requirements
- General 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
