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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/introduction-to-predictive-modeling
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课程名称：预测建模入门 课程概述：欢迎参加《预测建模入门》课程，这是明尼苏达大学“决策分析”专业的第一门课程。本课程将介绍预测建模的概念、流程和应用，重点关注线性回归和时间序列预测模型及其在Microsoft Excel中的实际应用。课程结束时，您将能够： - 理解预测建模的概念、流程和应用。 - 理解线性回归模型的结构和直觉。 - 能够为数据拟合简单和多元线性回归模型，解读结果，评估拟合优度，并使用拟合模型进行预测。 - 理解过拟合和欠拟合的问题，并能够进行简单的模型选择。 - 理解时间序列预测作为预测建模的一种特殊类型的概念、流程和应用。 - 能够在Excel中拟合多种时间序列预测模型（例如，指数平滑法和霍尔特-温特法），评估拟合优度，并使用拟合模型进行预测。 - 理解不同类型的数据及其在预测模型中的应用。 - 使用Excel准备数据进行预测建模，包括探索数据模式、数据转换和处理缺失值。 本课程是预测建模的入门课程，结合了概念学习和实践操作。在课程中，您将有机会使用Excel在真实世界的数据集上实践预测建模技术。 成功完成本课程需要具备基本数学知识（如函数、变量和基本数学符号的概念，包括求和和索引），以及基本统计知识（例如，相关性、样本均值、标准差和方差）。本课程不要求编程背景，但您需要熟悉基本的Excel操作（如基本公式和图表制作）。为了获得更好的学习体验，建议您在计算机上安装最新版本的Microsoft Excel（如Excel 2013、2016、2019或Office 365）。 课程大纲： 1. 第一周/模块：简单线性回归 - 概述预测建模问题及其广泛应用，介绍简单线性回归的基本结构和Ordinary Least Squares的直觉，展示如何使用Excel工具拟合模型。 2. 第二周/模块：多元线性回归 - 介绍多元线性回归及其应用，使用Excel的回归工具拟合模型，并讨论过拟合/欠拟合问题及模型选择方法。 3. 第三周/模块：数据准备 - 学习如何为预测建模准备数据，讨论不同变量的类型以及如何处理缺失值，介绍Excel中的数据处理工具。 4. 第四周/模块：时间序列预测 - 专注于时间序列预测，讨论时间序列数据的性质，介绍适合在Excel中实现的时间序列模型，包括移动平均法和指数平滑法。
Part: 1
Title:Week/Module 1: Simple Linear Regression
Description:This module provides a brief overview of predictive modeling problems, illustrating their broad applications. It then focuses on the simplest form of predictive models: simple linear regression. The module follows a graphical approach to illustrate the structure of a simple linear regression model, the intuition for Ordinary Least Squares, and related concepts. Finally, we demonstrate how to use various Excel tools, including trendlines, the Regression tool, and the Trend() function, to fit a simple linear regression model and use it to form predictions.
Part: 2
Title:Week/Module 2: Multiple Linear Regression
Description:Building on Week 1, in this week we introduce multiple linear regression and its broad applications. Then, we cover how to fit a multiple linear regression model using Excel’s Regression tool and Trend() function and use the resulting model for predictions. The module further discusses the overfitting/underfitting problems and the basic principles of a good regression model. The module also introduces one approach for selecting a good model: backward elimination that can be implemented in Excel.
Part: 3
Title:Week/Module 3: Data Preparation
Description:In this week, we will learn how to prepare a dataset for predictive modeling and introduce Excel tools that can be leveraged to fulfill this goal. We will discuss different types of variables and how categorical, string, and datetime values may be leveraged in predictive modeling. Furthermore, we will discuss the intuition for including high-order and interaction variables in regression models, the issue of multicollinearity, and how to handle missing values. We will also introduce several handy Excel tools for data handling and exploration, including Pivot Table, IF() function, VLOOKUP function, and relative reference.
Part: 4
Title:Week/Module 4: Time Series Forecasting
Description:This module focuses on a special subset of predictive modeling: time series forecasting. We discuss the nature of time-series data and the structure of time series forecasting problems. We then introduce a host of time series models for stationary data and data with trends and seasonality, with a focus on techniques that are easily implemented within Excel, including moving average, exponential smoothing, double moving average, Holt’s method, and Holt-Winters’ method. The module also covers linear-regression-based forecasting and a composite forecasting technique for boosting accuracy.
Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization. This course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel. By the end of the course, you will be able to: - Understand the concepts, processes, and applications of predictive modeling. - Understand the structure of and intuition behind linear regression models. - Be able to fit simple and multiple linear regression models to data, interpret the results, evaluate the goodness of fit, and use fitted models to make predictions. - Understand the problem of overfitting and underfitting and be able to conduct simple model selection. - Understand the concepts, processes, and applications of time series forecasting as a special type of predictive modeling. - Be able to fit several time-series-forecasting models (e.g., exponential smoothing and Holt-Winter’s method) in Excel, evaluate the goodness of fit, and use fitted models to make forecasts. - Understand different types of data and how they may be used in predictive models. - Use Excel to prepare data for predictive modeling, including exploring data patterns, transforming data, and dealing with missing values. This is an introductory course to predictive modeling. The course provides a combination of conceptual and hands-on learning. During the course, we will provide you opportunities to practice predictive modeling techniques on real-world datasets using Excel. To succeed in this course, you should know basic math (the concept of functions, variables, and basic math notations such as summation and indices) and basic statistics (correlation, sample mean, standard deviation, and variance). This course does not require a background in programming, but you should be familiar with basic Excel operations (e.g., basic formulas and charting). For the best experience, you should have a recent version of Microsoft Excel installed on your computer (e.g., Excel 2013, 2016, 2019, or Office 365).
