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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-data-science-with-tclab/
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**课程名称：Python 数据科学与 TCLab 实战** **课程概述：** 本课程旨在帮助您在 Python 中学习数据科学与机器学习技能。课程包含 12 个模块，每个练习都有视频教程和解决方案。课程的独特之处在于，它从基础知识入手，并通过使用真实数据进行热传导设计项目来检验您的知识。您将通过设计新产品的材料，直观地看到 Python 代码的实际应用。通过项目来学习或复习编程语言是重要途径之一。这些练习旨在教授数据科学 Python 编程技能。数据科学应用几乎遍及所有行业，将原始数据转化为驱动科学发现、商业创新和发展的可操作信息。本课程的最终项目是确定几种材料的导热系数。导热系数是指材料传导或绝缘热量的能力。该热传导项目展示了如何应用数据科学来解决重要问题，其方法可适用于许多不同的应用。 **课程目标：** 收集并分析 TCLab 的数据，以确定放置在两个温度传感器之间的三种材料（金属、塑料和纸板）的导热系数。创建能够预测热传递和温度的数字孪生模型。 为了使问题更贴近实际应用，假设您正在设计下一代手机。手机的电池和处理器会产生大量热量。您需要确保它们之间的材料能够防止处理器过热导致电池损坏。本研究将帮助您解答有关材料特性以预测电池和处理器温度的问题。 **课程主题：** 课程包含 12 个章节，帮助您实现学习 Python 数据科学的目标。首先，您需要安装 Python 来打开和运行 Jupyter 中的 IPython notebook 文件。此外，还将提供有关如何安装 Python 和管理模块的说明。可以使用任何 Python 发行版或集成开发环境（IDE）（如 IDLE、Spyder、PyCharm 等），但打开和运行 IPython notebook (.ipynb) 文件需要 Jupyter notebook 或 VSCode。所有 IPython notebook (.ipynb) 文件均可下载。请记住在使用前解压缩文件夹（提取存档）并将其复制到方便的位置。 * 数据导入与导出 * 数据分析 * 数据可视化 * 数据准备（清洗、缩放、划分） * 回归 * 特征工程 * 分类 * 插值 * 求解方程 * 微分方程 * 时间序列 这些技能将为您完成最终项目打下基础。在最终项目中，将金属硬币、塑料和纸板插入两个加热器之间，以便在两个传感器之间形成热传导路径。温差和温度水平会受到材料将热量从加热器 1 和温度传感器 T1 传导到另一个温度传感器 T2 能力的影响。 您可能不总是知道如何解决问题或构建算法。您可能不知道需要使用的函数或与对象相关的属性名称。这是一种刻意设计。您需要通过帮助资源、在线资源、教科书等来查找所需信息。 您的评分将不仅基于程序给出正确输出的能力，还将考虑良好的编程实践，例如易用性、代码可读性和简洁性、模块化编程以及充分、有用的注释。请记住，注释、缩进和模块化编程在回顾代码时可以极大地帮助您和他人。 **温度控制实验室 (TCLab)：** 项目将通过来自温度控制实验室（TCLab）的真实数据，对所有课程内容进行回顾。温度由 TCLab 的加热器进行调节。如果您没有 TCLab 模块，请使用数字孪生模拟器，将 `TCLab()` 替换为 `TCLabModel()`。
These modules are intended to help you develop data science and machine learning skills in Python. The 12 modules have video tutorials for each exercise with solutions for each exercise. One of the unique things about these modules is that you work on basic elements and then test your knowledge with real data exercises with a heat transfer design project. You will see your Python code have a real impact by designing the materials for a new product. One of the best ways to start or review a programming language is to work on a project. These exercises are designed to teach data science Python programming skills. Data science applications are found across almost all industries where raw data is transformed into actionable information that drives scientific discovery, business innovations, and development. This project is to determine the thermal conductivity of several materials. Thermal conductivity is how well a material conducts or insulates against heat transfer. The specific heat transfer project shows how to apply data science to solve an important problems with methods that are applicable to many different applications.Objective: Collect and analyze data from the TCLab to determine the thermal conductivity of three materials (metal, plastic, and cardboard) that are placed between two temperature sensors. Create a digital twin that predicts heat transfer and temperature.To make the problem more applicable to a real situation, suppose that you are designing a next-generation cell phone. The battery and processor on the cell phone generate a lot of heat. You want to make sure that the material between them will prevent over-heating of the battery by the processor. This study will help you answer questions about material properties for predicting the temperature of the battery and processor.TopicsThere are 12 lessons to help you with the objective of learning data science in Python. The first thing that you will need is to install Python to open and run the IPython notebook files in Jupyter. There are additional instructions on how to install Python and manage modules. Any Python distribution or Integrated Development Environment (IDE) can be used (IDLE, Spyder, PyCharm, and others) but Jupyter notebook or VSCode is required to open and run the IPython notebook (.ipynb) files. All of the IPython notebook (.ipynb) files can be downloaded. Don't forget to unzip the folder (extract the archive) and copy it to a convenient location before starting.OverviewData Import and ExportData AnalysisVisualize DataPrepare (Cleanse, Scale, Divide) DataRegressionFeaturesClassificationInterpolationSolve EquationsDifferential EquationsTime SeriesThey give the skills needed to work on the final project. In the final project, metal coins, plastic, and cardboard are inserted in between the two heaters so that there is a conduction path for heat between the two sensors. The temperature difference and temperature levels are affected by the ability of the material to conduct heat from heater 1 and temperature sensor T1 to the other temperature sensor T2.You may not always know how to solve the problems initially or how to construct the algorithms. You may not know the function that you need or the name of the property associated with an object. This is by design. You are to search out the information that you might need using help resources, online resources, textbooks, etc.You will be assessed not only on the ability of the program to give the correct output, but also on good programming practices such as ease of use, code readability and simplicity, modular programming, and adequate, useful comments. Just remember that comments, indentation, and modular programming can really help you and others when reviewing your code.Temperature Control LabThe projects are a review of all course material with real data from temperature sensors in the Temperature Control Lab (TCLab). The temperatures are adjusted with heaters that are adjusted with the TCLab. If you do not have a TCLab module, use the digital twin simulator by replacing TCLab() with TCLabModel().
