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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/python-project-for-data-engineering
课程评论：没有评论
课程名称：数据工程的Python项目 课程概述：本迷你课程旨在通过实施不同的数据收集与处理技巧，应用基础的Python技能。您将扮演数据工程师的角色，从多种文件格式中提取数据，将其转化为特定的数据类型，然后加载到单一数据源中进行分析。课程将通过多个实践实验来测试您的知识，您将学习如何进行网页抓取和使用API提取数据。完成本课程后，您将具备收集来自多个来源的大型数据集并转换为一个主要数据源的信心，或者开始进行网页抓取，以利用Python获得有价值的商业洞察。 先决条件：完成IBM的“数据科学、AI与开发的Python”课程，或具备相应的Python和数据处理能力。 注意：本课程不旨在教授Python，内容主要是应用已有的Python知识。 课程大纲： 1. **提取、转换和加载（ETL）** 介绍ETL操作的基础知识。您将学习如何使用网页抓取技术和API提取所需信息，以及如何使用Python访问数据库并将处理过的信息保存为数据库中的表。 2. **期末项目** 在本课程的最后，您将完成两个项目，一个是实践项目，另一个是评估项目，以应用您在之前课程中所学到的内容。项目将涉及使用网页抓取和通过REST API和Python访问数据库来实施ETL过程。 3. **[可选] Python编码实践和打包概念** 在本附加模块中，您将了解PEP8风格指南所记录的编码最佳实践，并学习静态代码分析，确保您的代码遵循编码规则。此外，您将学习如何创建和运行单元测试，最后了解如何创建、验证和运行Python包。
Name:Extract, Transform, Load (ETL)
Description:Module 1 introduces you to Extract, Transform, and Load operations basics. You will learn to extract required information from web pages using web scraping techniques and APIs. You will also access databases using Python and save the processed information as a table in a database.
Name:Final Project
Description:In this lesson, you will complete two projects, one for practice and one for assessment to apply what you’ve learned. These projects have you implement your skills learned in the previous course and the last module regarding the Extract, Transform, and Load process using web scraping and accessing databases using REST APIs and Python.
Name:[Optional] Python Coding Practices and Packaging Concepts
Description:In this bonus module, you will become familiar with the best practices for coding as documented in the Python Enhancement Proposal (PEP8) style guide. You will learn about static code analysis, ensuring that your code adheres to the coding rules. Next, you will learn how to create and run unit tests. Finally, you will learn how to create, verify, and run Python packages.
This mini-course is intended to apply foundational Python skills by implementing different techniques to collect and work with data. Assume the role of a Data Engineer and extract data from multiple file formats, transform it into specific datatypes, and then load it into a single source for analysis. Continue with the course and test your knowledge by implementing webscraping and extracting data with APIs all with the help of multiple hands-on labs. After completing this course you will have acquired the confidence to begin collecting large datasets from multiple sources and transform them into one primary source, or begin web scraping to gain valuable business insights all with the use of Python. PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.
