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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/predictive-modeling-analytics
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课程名称：预测建模与分析 课程概述： 欢迎参加商业数据分析专业化的第二门课程！本课程将介绍一些最广泛使用的预测建模技术及其核心原理。通过学习本课程，您将建立预测分析的坚实基础，预测分析是指利用统计或机器学习模型根据数据进行预测的工具和技术。您将学习如何进行探索性数据分析，从而获取洞察并为预测建模准备数据，这是在商业领域中备受重视的一项技能。 您还将学习如何使用图表对数据集进行总结和可视化，以便以引人入胜和有意义的方式展示您的结果。我们将使用实用的预测建模软件XLMiner，这是一个流行的Excel插件。本课程旨在为任何希望利用数据获取洞察并做出更好商业决策的人士提供指导。所讨论的技术适用于商业组织的所有职能领域，包括会计、金融、人力资源管理、市场营销、运营和战略规划。 本课程的预期先决条件包括具备Excel的工作知识、初级代数和基础统计学知识。 课程大纲： 1. 探索性数据分析与可视化 - 描述：在本模块结束时，学生将能够进行探索性数据分析以获取洞察并为预测建模准备数据；使用适当工具对数据集进行总结和可视化；识别连续和离散结果的建模技术；使用Excel探索数据集；解释和执行多个常见数据预处理步骤；选择合适的图表来探索和展示数据集。 2. 连续变量的预测 - 描述：本模块介绍回归技术以预测连续变量的值。涉及预测建模的一些基本概念，包括交叉验证、模型选择和过拟合。您还将学习如何使用软件工具XLMiner构建预测模型。 3. 二元结果的预测 - 描述：本模块介绍逻辑回归模型以预测二元变量的值。与连续变量不同，二元变量只能取两个不同的值，预测其值通常称为分类。讨论几个与分类有关的重要概念，包括交叉验证和混淆矩阵、成本敏感分类和ROC曲线。您将学习如何使用软件工具XLMiner构建分类模型。 4. 树与其他预测模型 - 描述：本模块介绍更高级的预测模型，包括树和神经网络。树和神经网络都可以用来预测连续或二元变量。您还将学习如何使用软件工具XLMiner构建树和神经网络。
Name: Exploratory Data Analysis and Visualizations
Description:At the end of this module students will be able to: 1. Carry out exploratory data analysis to gain insights and prepare data for predictive modeling 2. Summarize and visualize datasets using appropriate tools 3. Identify modeling techniques for prediction of continuous and discrete outcomes. 4. Explore datasets using Excel 5. Explain and perform several common data preprocessing steps 6. Choose appropriate graphs to explore and display datasets
Name:Predicting a Continuous Variable
Description:This module introduces regression techniques to predict the value of continuous variables. Some fundamental concepts of predictive modeling are covered, including cross-validation, model selection, and overfitting. You will also learn how to build predictive models using the software tool XLMiner.
Name:Predicting a Binary Outcome
Description:This module introduces logistic regression models to predict the value of binary variables. Unlike continuous variables, a binary variable can only take two different values and predicting its value is commonly called classification. Several important concepts regarding classification are discussed, including cross validation and confusion matrix, cost sensitive classification, and ROC curves. You will also learn how to build classification models using the software tool XLMiner.
Name:Trees and Other Predictive Models
Description:This module introduces more advanced predictive models, including trees and neural networks. Both trees and neural networks can be used to predict continuous or binary variables. You will also learn how to build trees and neural networks using the software tool XLMiner.
Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business. You’ll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way. We will use a practical predictive modeling software, XLMiner, which is a popular Excel plug-in. This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning. The expected prerequisites for this course include a prior working knowledge of Excel, introductory level algebra, and basic statistics.
