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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-python-for-data-apis-web-scraping/
课程评论：没有评论
课程名称：实用Python数据API与网络爬虫 概述：在数据科学领域，往往将重点放在构建和调整机器学习模型上，而忽视了获取和预处理数据的重要步骤，尤其是当数据来自API时。数据科学家通常获得的是已经清理和组织良好的数据，通常以Excel或CSV格式呈现，得益于数据工程师的努力。这种情况可能会导致技能的脱节，因为获取和准备原始数据是任何数据专业人员的关键能力。本课程旨在填补这一空白，教授您如何使用Python从各种类型的API中获取数据。您将学习如何处理无法通过API轻松获取的数据，并通过网络爬虫技术直接从网站上收集所需信息。 原始数据往往以杂乱的格式呈现，需要大量的清理和处理。在整个实用课程中，我将逐步指导您使用Python完成这一过程，运用Selenium、Requests和Pandas等强大库。完成数据清洗后，将把清理后的数据导出为CSV或Excel表格，随后我们将使用Python的图形库对这些数据进行可视化。除了获取和处理数据，您还将掌握如何将您的代码重构为可重复使用的函数和自定义模块，确保您的工作高效且适应未来项目的需求。
In the field of Data Science, much of the focus is placed on building and fine-tuning Machine Learning models, often overlooking the crucial steps of obtaining and preprocessing data, especially when it comes from APIs.Data scientists typically receive data that is already clean and well-organized, often in Excel or CSV formats, thanks to the efforts of Data Engineers. This can lead to a disconnect, as the ability to acquire and prepare raw data is a vital skill for any data professional.This course is designed to bridge that gap by teaching you how to source data from any type of API using Python. You'll learn how to handle situations where data isn't readily available through APIs by employing Web Scraping techniques to gather the information you need directly from websites.Raw data often comes in messy formats that require significant cleaning and processing. Throughout this practical course, I will guide you through each step of this process using Python, leveraging powerful libraries such as Selenium, Requests, and Pandas.After exporting the cleaned data to CSV or Excel tables, we will delve into visualizing this data using Python's graphical libraries. Beyond just obtaining and processing data, you'll also master how to refactor your code into reusable functions and custom modules, ensuring that your work is efficient and adaptable to future projects.
