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所在平台: Udemy |
课程主页: https://www.udemy.com/course/pyspark-for-beginners/
课程评论：没有评论
课程名称：PySpark 初学者课程 课程概述：本课程由Tomasz Drabas讲授，他是微软的数据科学家，拥有13年以上的数据分析和数据科学经验。课程主要介绍Apache Spark这一开源框架，强调其在集群计算中的高效性、数据并行处理及故障容错功能。学员将学习如何利用Python在Spark生态系统中进行数据处理，首先会深入了解Spark 2.0架构及如何设置Python环境。课程内容涵盖PySpark的各个模块，数据抽象技术（包括RDD和DataFrame），以及PySpark的流处理能力。此外，学员还将了解PySpark在机器学习（使用ML和MLlib）、图形处理（使用GraphFrames）以及多种持久性方案（使用Blaze）方面的应用。最后，还会学习如何通过spark-submit命令将应用程序部署到云端。完成本课程后，学员将能掌握Spark Python API，并能够构建数据密集型应用程序。 讲师简介：Tomasz Drabas是微软的一名数据科学家，目前居住在西雅图地区。他在多个领域（如先进技术、航空公司、电信、金融和咨询）有着广泛的经验，并曾在欧洲、澳大利亚和北美三个大洲工作。他在澳大利亚时正在攻读运筹学博士，重点研究航空行业的选择建模和收益管理应用。Tomasz每天都在微软处理大数据，解决机器学习相关的问题，如异常检测、客户流失预测和模式识别。他还曾于2016年出版了《实用数据分析食谱》。
omasz has also authored the Practical Data Analysis Cookbook published by Packt Publishing in 2016.Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This course will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this course, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.About the AuthorTomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 13 years of experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting he gained while working on three continents: Europe, Australia, and North America. While in Australia, Tomasz has been working on his PhD in Operations Research with a focus on choice modeling and revenue management applications in the airline industry.At Microsoft, Tomasz works with big data on a daily basis, solving machine learning problems such as anomaly detection, churn prediction, and pattern recognition using Spark. Tomasz has also authored the Practical Data Analysis Cookbook published by Packt Publishing in 2016.
