课程大纲
介绍
了解 Big Data
Spark概述
Python概述
PySpark概述
- 使用弹性分布式数据集框架分发数据
- 使用 Spark API 运算符分发计算
使用 Spark 设置 Python
设置 PySpark
使用适用于 Spark 的 Amazon Web Services (AWS) EC2 实例
设置 Databricks
设置 AWS EMR 集群
学习 Python 编程的基础知识
- 开始使用 Python
- 使用 Jupyter Notebook
- 使用变量和简单数据类型
- 使用列表
- 使用 if 语句
- 使用用户输入
- 使用 while 循环
- 实现函数
- 使用类
- 使用文件和异常
- 使用项目、数据和 API
了解 Spark DataFrame 的基础知识
- Spark DataFrames 入门
- 使用 Spark 实现基本操作
- 使用 Groupby 和聚合操作
- 使用时间戳和日期
处理 Spark DataFrame 项目练习
使用 MLlib 了解 Machine Learning
使用 MLlib、Spark 和 Python 进行机器学习
了解回归
- 学习线性回归理论
- 实现回归评估代码
- 进行示例线性回归练习
- 学习逻辑回归理论
- 实现逻辑回归代码
- 进行示例逻辑回归练习
了解 Random Forest 和决策树
- 学习树方法理论
- 实现决策树和 Random Forest 代码
- 处理样本 Random Forest 分类练习
使用 K-means 聚类分析
- 了解 K 均值聚类理论
- 实现 K-means 聚类代码
- 处理示例聚类分析练习
使用推荐系统
实现自然语言处理
- 了解 Natural Language Processing (NLP)
- NLP工具概述
- 进行示例 NLP 练习
在 Python 上使用 Spark 进行流式处理
- 概述:使用 Spark 进行流式处理
- 示例 Spark Streaming 练习
闭幕致辞
要求
- 一般编程技能
观众
- 开发 人员
- IT 专业人员
- 数据科学家
客户评论 (5)
I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.
Aurelia-Adriana - Allianz Services Romania
课程 - 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
课程 - Python and Spark for Big Data (PySpark)
Individual attention.
ARCHANA ANILKUMAR - PPL
课程 - Python and Spark for Big Data (PySpark)
Hands on Training..
Abraham Thomas - PPL
课程 - 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.