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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-beginners-z/
课程评论：没有评论
课程名称：初学者的Python编程 课程概述：本课程旨在帮助没有编程经验的学生学习Python语言。完成本课程后，学生将对Python编程语言有一个全面的了解。为了更好地掌握Python，学生需要更多的实践和在特定领域的更深入培训。然而，通过本课程，学生将熟悉Python环境中的大部分元素。 课程内容从介绍如何安装和设置Python环境开始，接着学习变量、循环、数字和函数的定义。我们将回顾Numpy的基础知识，这是Python数学中一个重要的包。随后，课程将介绍Python标准库的基本元素，例如从网络拉取数据、存储持久数据、处理十进制数字以及创建可视化应用。 由于分析原始数据有时是一项复杂的任务，我们还将展示如何利用强大的Matplotlib包进行绘图。课程中重点讲解Python的另一个重要元素：类。我们将从一个简单的类开始，逐步构建更复杂的类，解释不同的方面。 对于使用Python的开发者来说，构建处理数据的应用程序是非常重要的。鉴于Python是使用最广泛的统计和机器学习编程语言，我们最后将回顾Python中的数据科学三大包：Pandas（数据处理）、Scikit-learn（机器学习）和Statsmodels（统计分析）。这一部分的目的是介绍这些包的基础知识。 到课程结束时，学生应该能够： - 编写自己的Python程序 - 定义和使用类 - 使用数据结构 - 处理日期、字符串、Numpy数组和数字 - 在Python中读写文件 - 利用标准库中的元素 - 理解Pandas、Scikit-learn和Statsmodels的基础知识 - 通过Pandas加载数据、进行预处理，并使用Scikit-learn和Statsmodels进行建模 课程中包含许多练习和测验！我们努力保持课程内容的最新性，学生可随时提出问题，我们会尽快进行答复。
This course is intended for students aiming to learn Python, with no previous programming experience. After this course, the student will have a general overview of the Python programming language. In order to master Python, the student will need more practice, and more specific training in some areas. Nevertheless, with this course, the student will be familiar with most elements in the Python environment.We start by explaining how to install and set up the Python environment, and then how to define variables, loops, numbers, and functions. We then review the basics behind Numpy, which is a critical package for mathematics in Python. We then explain the fundamental elements of the Python standard library such as pulling data from the web, storing persistent data, working with decimal numbers, and creating visual applications. Because analysing raw numbers is sometimes a complicated task, we also show how to leverage the powerful Matplotlib package for creating plots. We then review one of the most important elements of Python: Classes. We start with a very simple class, and we then build more complicated ones explaining different aspects. People working with Python, will most likely need to build applications processing data. And because Python is the most used statistical and machine learning programming language, we finally review the data science packages triad in Python: Pandas (data processing), Scikit-learn (machine-learning), and Statsmodels (statistics). The idea of this part is to introduce the basics behind these packages.At the end of the course, the student should be able to:Code his own Python programs Define and use classesUse data structuresWork with dates, strings, Numpy arrays, numbersWrite and read files in PythonLeverage the elements in the standard libraryUnderstand the basics behind Pandas, Scikit-learn and StatsmodelsLoad data via Pandas, pre-process it, and model it using Scikit-learn and StatsmodelsYou will find lots of exercises and quizzes!We try to keep this course as updated as possible, and the student is welcome to formulate questions, as we try to answer them promptly.
