Python and Spark for Big Data (PySpark) 교육 과정

Introduction

Comprehending Big Data

Spark Overview

Python Overview

PySpark Overview

• Distributing Data Using the Resilient Distributed Datasets (RDD) Framework
• Distributing Computation Using Spark API Operators

Configuring Python with Spark

Setting Up PySpark

Utilizing Amazon Web Services (AWS) EC2 Instances for Spark

Configuring Databricks

Setting Up the AWS EMR Cluster

Mastering Fundamentals of Python Programming

• Getting Started with Python
• Utilizing the Jupyter Notebook
• Working with Variables and Basic Data Types
• Manipulating Lists
• Implementing Conditional Statements
• Handling User Inputs
• Working with while Loops
• Implementing Functions
• Working with Classes
• Managing Files and Exceptions
• Working with Projects, Data, and APIs

Exploring Spark DataFrames

• Introduction to Spark DataFrames
• Executing Basic Operations in Spark
• Applying Groupby and Aggregate Operations
• Handling Timestamps and Date Data

Spark DataFrame Project Exercise

Machine Learning with MLlib

Integrating MLlib, Spark, and Python for Machine Learning

Regression Analysis

• Theory of Linear Regression
• Implementing Regression Evaluation Code
• Linear Regression Practical Exercise
• Theory of Logistic Regression
• Implementing Logistic Regression Code
• Logistic Regression Practical Exercise

Random Forests and Decision Trees

• Theory Behind Tree-Based Methods
• Implementing Decision Trees and Random Forest Algorithms
• Random Forest Classification Practical Exercise

K-means Clustering

• Understanding K-means Clustering Theory
• Implementing K-means Clustering Code
• Clustering Practical Exercise

Recommender Systems

Implementing Natural Language Processing

• Concepts of Natural Language Processing (NLP)
• NLP Tooling Overview
• NLP Practical Exercise

Spark Streaming with Python

• Overview of Spark Streaming
• Spark Streaming Practical Exercise