|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/mastering-big-data-analytics-with-pyspark/
课程评论：没有评论
**课程名称： Mastering Big Data Analytics with PySpark** **课程概述：** 本课程旨在教授学员如何利用PySpark进行大规模数据分析，构建可扩展的数据分析和处理流程。课程将从PySpark在大数据分析中的潜力入手，重点讲解如何从Python交互Spark、连接Jupyter进行数据可视化。随后，课程将深入探讨Spark的组件和架构。 学员将学习使用Spark SQL进行数据收集和查询，应对数据读取方面的挑战。此外，课程还将教授如何利用DataFrame API操作Spark MLlib，并了解Pipeline API。最后，课程将提供代码部署和性能调优的技巧。 完成本课程后，学员将能够进行高效的数据分析，并熟练运用PySpark轻松处理组织内的大型数据集。 **讲师介绍：** Danny Meijer是荷兰一家领先的运动品零售商数据与分析部门的首席数据工程师。他拥有业务流程专家、大数据科学家和数据工程师的多重身份，这使得他具备独特的技能组合，并能以业务为先的理念来解决数据科学和数据工程问题。 Danny拥有超过13年的IT经验，涵盖了大数据建模、架构、设计、开发以及项目和流程管理等多个领域。他还在流程挖掘、大数据数据工程和流程改进方面拥有丰富的经验。 作为一名认证的数据科学家和大数据专业人士，Danny精通各种编程语言，并且在大数据技术方面造诣深厚，包括NoSQL、Hadoop、Python以及Spark。他热衷于数据和大数据领域，对数学、机器学习和解决复杂问题充满热情。
PySpark helps you perform data analysis at-scale; it enables you to build more scalable analyses and pipelines. This course starts by introducing you to PySpark's potential for performing effective analyses of large datasets. You'll learn how to interact with Spark from Python and connect Jupyter to Spark to provide rich data visualizations. After that, you'll delve into various Spark components and its architecture.You'll learn to work with Apache Spark and perform ML tasks more smoothly than before. Gathering and querying data using Spark SQL, to overcome challenges involved in reading it. You'll use the DataFrame API to operate with Spark MLlib and learn about the Pipeline API. Finally, we provide tips and tricks for deploying your code and performance tuning.By the end of this course, you will not only be able to perform efficient data analytics but will have also learned to use PySpark to easily analyze large datasets at-scale in your organization.About the AuthorDanny Meijer works as the Lead Data Engineer in the Netherlands for the Data and Analytics department of a leading sporting goods retailer. He is a Business Process Expert, big data scientist and additionally a data engineer, which gives him a unique mix of skills-the foremost of which is his business-first approach to data science and data engineering.He has over 13-years' IT experience across various domains and skills ranging from (big) data modeling, architecture, design, and development as well as project and process management; he also has extensive experience with process mining, data engineering on big data, and process improvement.As a certified data scientist and big data professional, he knows his way around data and analytics, and is proficient in various types of programming language. He has extensive experience with various big data technologies and is fluent in everything: NoSQL, Hadoop, Python, and of course Spark.Danny is a driven person, motivated by everything data and big-data. He loves math and machine learning and tackling difficult problems.
