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Working with Big Data in Python

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Working with Big Data in Python
Working with Big Data in Python
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours 41M | 522 MB
Genre: eLearning | Language: English

This course is a comprehensive, practical guide to using MongoDB and Spark in Python, learning how to store and make sense of huge data sets, and performing basic machine learning tasks to make predictions.

MongoDB is one of the most powerful non-relational database systems available offering robust scalability and expressive operations that, when combined with Python data analysis libraries and distributed computing, represent a valuable set of tools for the modern data scientist. NoSQL databases require a new way of thinking about data and scalable queries. Once Mongo queries have been mastered, it is necessary to understand how we can leverage this API in Python's rich analysis and visualization ecosystem. This course will cover how to use MongoDB, particularly if you are used to SQL databases, with a focus on scalability to large datasets. pyMongo is introduced as the means to interact with a MongoDB database from within Python code and the data structures used to do so are explored. MongoDB uniquely allows for complex operations and aggregations to be run within the query itself and we will cover how to use these operators. While MongoDB itself is built for easy scalability across many nodes as datasets grow, Python is not. Therefore, we cover how we can use Spark with MongoDB to handle more complex machine learning techniques for extremely large datasets. This learning will be applied to several examples of real-world datasets and analyses that can form the basis of your own pipelines, allowing you to quickly get up-and-running with a powerful data science toolkit.

Working with Big Data in Python

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  1. 本课程是在Python中使用MongoDB 和Spark全面、实用的指南, 学习如何存储和理解海量数据集, 以及执行基本的机器学习任务进行预测。 MongoDB是最强大的非关系数据库系统之一, 可提供强健的可伸缩性和表达式操作, 当与 Python 数据分析库和分布式计算相结合时, 为现代数据科学家提供了一组有价值的工具。NoSQL 数据库需要一种新的思考数据和可伸缩查询的方法。一旦已经掌握了MongoDB查询, 就有必要了解如何在 Python 的丰富分析和可视化生态系统中利用此 API。本课程将介绍如何使用 MongoDB, 特别是当您使用 SQL 数据库时, 重点是对大型数据集的可伸缩性。pyMongo 被介绍为从 Python 代码中与 MongoDB 数据库进行交互的方法, 并探讨了用于此目的的数据结构。MongoDB 唯一允许在查询本身内运行复杂的操作和聚合, 我们将介绍如何使用这些运算符。但随着数据集的增长,虽然MongoDB 本身是为便于扩展性而构建的, Python 并不是。因此, 我们将介绍如何使用Spark与 MongoDB 处理更复杂的机器学习技术, 用于庞大的数据集。这种学习将被应用到一些真实世界的数据集和分析中, 这些实例可以形成您自己的管道的基础, 让您能够快速地使用强大的数据科学工具包来运行。
    sunsource(特殊组-翻译)5个月前 (02-24)