Formation Leveraging Python and Spark for Big Data (PySpark)

Introduction

Concepts of Big Data

Spark Fundamentals

Python Essentials

PySpark Overview

• Distributing data via the Resilient Distributed Datasets framework.
• Distributing computation using Spark API operators.

Configuring Python with Spark

Setting Up the PySpark Environment

Utilizing Amazon Web Services (AWS) EC2 Instances for Spark

Initializing Databricks

Configuring the AWS EMR Cluster

Foundations of Python Programming

• Introduction to Python
• Working with Jupyter Notebooks
• Variables and Basic Data Types
• Handling Lists
• Conditional Logic with if Statements
• Processing User Input
• Iterating with while Loops
• Creating Functions
• Object-Oriented Programming with Classes
• Managing Files and Handling Exceptions
• Developing Projects with Data and APIs

Essentials of Spark DataFrames

• Introduction to Spark DataFrames
• Performing Basic Operations in Spark
• Applying Groupby and Aggregate Functions
• Managing Timestamps and Dates

Practical Spark DataFrame Project

Machine Learning Concepts with MLlib

Applying MLlib, Spark, and Python for Machine Learning

Exploring Regression Models

• Theoretical Basis of Linear Regression
• Writing Code for Regression Evaluation
• Practicing Linear Regression Exercises
• Theoretical Basis of Logistic Regression
• Implementing Logistic Regression Logic
• Practicing Logistic Regression Exercises

Random Forests and Decision Trees

• Understanding Tree-Based Methodologies
• Coding Decision Trees and Random Forests
• Practicing Random Forest Classification Exercises

K-means Clustering

• Theoretical Understanding of K-means Clustering
• Implementing K-means Clustering Algorithms
• Practicing Clustering Exercises

Recommender Systems

Implementing Natural Language Processing

• Foundations of Natural Language Processing (NLP)
• Survey of NLP Tools
• Practicing NLP Exercises

Streaming Data with Spark and Python

• Overview of Spark Streaming
• Sample Spark Streaming Project