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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/time-series-survival-analysis
课程评论：没有评论
课程名称：专门模型：时间序列与生存分析 概述：本课程将为您介绍机器学习中的额外主题，这些主题补充了预测和分析截尾数据等基本任务。您将学习如何分析具有时间成分的数据以及需要结果推断的截尾数据。课程将涵盖一些时间序列分析和生存分析的技术。实践部分专注于采用最佳实践并验证源自统计学习的假设。 课程目标：完成本课程后，您将能够： - 确定时间序列数据中的常见建模挑战 - 解释如何分解时间序列数据：趋势、季节性和残差 - 理解自回归、移动平均和ARIMA模型的工作原理 - 学会选择和实施各种时间序列模型 - 描述生存分析的危害和生存建模方法 - 确定适合生存分析的问题类型 适合人群：本课程专为有志于获得时间序列分析和生存分析实践经验的数据科学初学者设计。 所需技能：为了更好地学习本课程，您应具备Python开发环境下的编程基础，以及数据清洗、探索性数据分析、微积分、线性代数、监督式机器学习、无监督式机器学习、概率论和统计学的基本知识。 课程大纲： 1. 时间序列分析入门：介绍预测的概念，并解释为什么时间序列分析在预测中更为合适，学习时间序列的主要组成部分及其分解模型的使用。 2. 平稳性与时间序列平滑：介绍平稳性和时间序列平滑的概念，学习如何识别和解决非平稳性问题，以及平滑如何提高模型准确性。 3. ARMA与ARIMA模型：介绍移动平均模型，学习自回归模型的理论，并实践编写ARMA模型，扩展知识至SARMA和SARIMA模型。 4. 深度学习与生存分析预测：介绍用于预测的两种额外工具：深度学习和生存分析，讨论其在统计学中的应用及在药品行业等商业场景中的广泛使用。 本课程将为您提供时间序列分析与生存分析的坚实基础，助力您在数据科学领域的进一步发展。
Name:Introduction to Time Series Analysis
Description:This module introduces the concept of forecasting and why Time Series Analysis is best suited for forecasting, compared to other regression models you might already know. You will learn the main components of a Time Series and how to use decomposition models to make accurate time series models.
Name:Stationarity and Time Series Smoothing
Description:This module introduces you to the concepts of stationarity and Time Series smoothing. Having a Time Series that is stationary is easy to model. You will learn how to identify and solve non-stationarity. Smoothing is relevant to you as it will help improve the accuracy of your models.
Name:ARMA and ARIMA Models
Description:This module introduces moving average models, which are the main pillar of Time Series analysis. You will first learn the theory behind Autoregressive Models and gain some practice coding ARMA models. Then you will extend your knowledge to use SARMA and SARIMA models as well.
Name:Deep Learning and Survival Analysis Forecasts
Description:This module introduces two additional tools for forecasting: Deep Learning and Survival Analysis. In addition to AI and Machine Learning applications, Deep Learning is also used for forecasting. Survival Analysis is a branch of Statistics first ideated to analyze hazard functions and the expected time for an event such as mechanical failure or death to happen. Survival Analysis is still used widely in the pharmaceutical industry and also in other business scenarios with limited data related to censoring, the lack of information on whether an event occurred or not for a certain observation.
This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices and verifying assumptions derived from Statistical Learning. By the end of this course you should be able to: Identify common modeling challenges with time series data Explain how to decompose Time Series data: trend, seasonality, and residuals Explain how autoregressive, moving average, and ARIMA models work Understand how to select and implement various Time Series models Describe hazard and survival modeling approaches Identify types of problems suitable for survival analysis Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics.
