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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke
课程评论：没有评论
课程名称：数据工程的Web应用程序和命令行工具 概述：在"Python、Bash和SQL数据工程基础"特 specialization的第四门课程中，您将进一步学习数据工程的概念，将Python、Bash和SQL技术应用于解决实际问题。首先，我们将深入探索如何利用Jupyter Notebooks创建和部署机器学习模型。接着，我们将探讨如何使用Python微服务将数据仓库拆解成可扩展的小型便携解决方案。最后，您将构建一个强大的命令行工具，以自动化测试和质量控制，便于发布和与数据注册中心共享您的工具。 课程大纲： 1. **Jupyter Notebooks** - 描述：本周，您将学习如何在本地计算机上安装和运行Jupyter。同时，您将探讨在Jupyter Notebook中使用代码和文本单元格的策略。 2. **云托管笔记本** - 描述：本周，您将学习如何在Google Colab和AWS Sagemaker中创建和使用基于云的笔记本。 3. **Python微服务** - 描述：本周，您将学习如何构建使用FastAPI的Python微服务，并为数据工程部署一个容器化的机器学习微服务。 4. **Python打包和命令行工具** - 描述：本周，您将学习如何组织Python项目，以便构建一个强大的命令行工具。您将使用Click，一个有用的命令行工具框架，以增强您的工具。最后，您将实现测试和质量控制的自动化，以便将您的工具发布并与注册中心共享。
Name:Jupyter Notebooks
Description:This week, you will learn how to install and run Jupyter on your local machine. Additionally, you will explore strategies to use code and text cells in a Jupyter notebook.
Name:Cloud-Hosted Notebooks
Description:This week, you will learn how to create and use a Cloud-based notebook in Google Colab and AWS Sagemaker.
Name:Python Microservices
Description:This week, you will learn how to build a Python Microservice with FastAPI and deploy a containerized machine learning Microservice for data engineering.
Name:Python Packaging and Command Line Tools
Description:This week, you will learn how to organize a Python project so you can build a powerful command-line tool. You will use Click, a useful command-line tool framework to enhance your tool. Finally, you will automate testing and quality control for publishing and sharing your tool with a registry.
In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale. Finally, you will build a powerful command-line tool to automate testing and quality control for publishing and sharing your tool with a data registry.
