Python est un langage de programmation de haut niveau réputé pour sa syntaxe claire et sa lisibilité du code. Spark est un moteur de traitement de données utilisé pour interroger, analyser et transformer le Big Data. PySpark permet aux utilisateurs d’interfacer Spark avec Python .
Au cours de cette formation en direct animée par un instructeur, les participants apprendront à utiliser Python et Spark ensemble pour analyser des données volumineuses au cours d’exercices pratiques.
À la fin de cette formation, les participants seront en mesure de:
- Apprenez à utiliser Spark avec Python pour analyser des Big Data .
- Travaillez sur des exercices qui imitent les circonstances réelles.
- Utilisez différents outils et techniques d'analyse de données PySpark aide de PySpark .
Format du cours
- Partie de conférence, partie de discussion, exercices et exercices intensifs
Machine Translated
Introduction
Understanding Big Data
Overview of Spark
Overview of Python
Overview of PySpark
- Distributing Data Using Resilient Distributed Datasets Framework
- Distributing Computation Using Spark API Operators
Setting Up Python with Spark
Setting Up PySpark
Using Amazon Web Services (AWS) EC2 Instances for Spark
Setting Up Databricks
Setting Up the AWS EMR Cluster
Learning the Basics of Python Programming
- Getting Started with Python
- Using the Jupyter Notebook
- Using Variables and Simple Data Types
- Working with Lists
- Using if Statements
- Using User Inputs
- Working with while Loops
- Implementing Functions
- Working with Classes
- Working with Files and Exceptions
- Working with Projects, Data, and APIs
Learning the Basics of Spark DataFrame
- Getting Started with Spark DataFrames
- Implementing Basic Operations with Spark
- Using Groupby and Aggregate Operations
- Working with Timestamps and Dates
Working on a Spark DataFrame Project Exercise
Understanding Machine Learning with MLlib
Working with MLlib, Spark, and Python for Machine Learning
Understanding Regressions
- Learning Linear Regression Theory
- Implementing a Regression Evaluation Code
- Working on a Sample Linear Regression Exercise
- Learning Logistic Regression Theory
- Implementing a Logistic Regression Code
- Working on a Sample Logistic Regression Exercise
Understanding Random Forests and Decision Trees
- Learning Tree Methods Theory
- Implementing Decision Trees and Random Forest Codes
- Working on a Sample Random Forest Classification Exercise
Working with K-means Clustering
- Understanding K-means Clustering Theory
- Implementing a K-means Clustering Code
- Working on a Sample Clustering Exercise
Working with Recommender Systems
Implementing Natural Language Processing
- Understanding Natural Language Processing (NLP)
- Overview of NLP Tools
- Working on a Sample NLP Exercise
Streaming with Spark on Python
- Overview Streaming with Spark
- Sample Spark Streaming Exercise
Closing Remarks
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