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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-time-series/
课程评论：没有评论
**课程名称：** Python 时间序列分析 - 数据分析与预测 **课程概述：** 本课程面向希望学习 Python 在时间序列数据集上应用的学员。课程假设学员具备 Python 编程基础知识，鼓励学员动手实践课程中的代码，以更深入地理解概念。课程不提供预先写好的代码，教学过程将直接在视频中展示代码编写。 课程内容涵盖了 Python 在时间序列数据分析中的应用，包括： * **基础回顾：** 简要讲解统计学和 Python 库的基础知识，帮助学员巩固基本功。 * **Python 库应用：** 重点介绍用于时间序列数据分析的 Python 库。 * **核心技能掌握：** * 使用 Pandas 库处理时间序列数据。 * 检测时间序列数据的季节性。 * 执行 Augmented Dickey-Fuller (ADF) 检验，判断时间序列的平稳性。 * 构建 ARIMA 模型进行时间序列预测。 * 完成一个完整的时间序列项目。 * 可视化时间序列数据。 * 使用时间序列模型进行预测。 **目标学员：** 对使用 Python 进行时间序列数据分析和预测感兴趣的学习者。 **讲师支持：** 学员可以通过 Udemy 的问答区随时与讲师交流。讲师将持续检查和更新课程代码。
Welcome to the Python for Time Series - Data Analysis & Forecasting course. This course is designed for students who want to learn Python applications for time series datasets. This course assumes that you have basic level of knowledge on Python Programming. For getting most from the course you can apply the codes by yourself. All the codes in the course are typed in the videos so with non pre-written codes you are going to understand concepts better. The course covers the usage of Python libraries for time series data. There will be short lectures on statistics and Python library fundamentals at the beginning of the course to help you remember the basics. Then, the Python libraries used for time series data will be covered. After completing this course, you will be able to use the Pandas library for Time Series Data, check for seasonality in Time Series Data, perform a Dickey-Fuller test (a test for stationarity) on Time Series Data, build an ARIMA model for Time Series Data, and complete a Time Series project. Additionally, you will be able to visualize Time Series Data and forecast using Time Series Models. If you are interested in Python for Time Series, you can enroll in my course. You can reach me about the course anytime through the Q & A section on Udemy. I will be constantly checking the code and keeping it updated in the course.
