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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/business-analytics-excel
课程评论：没有评论
课程名称：Excel商业分析：从初级到高级 课程概述：在数据驱动的世界中，领导者需要掌握数据相关（统计）方法和使用适当模型的知识。本课程专注于分析框架，帮助学生通过Excel建模进行决策。内容包括线性和整数优化、决策分析以及风险模型。课程首先介绍基本原理，然后将理论应用于真实商业案例。 课程大纲： 1. **Excel基础与最佳实践** 课程旨在介绍管理科学方法的多种问题，并在Excel中建模。从电子表格设计基础开始，逐步深入到更复杂的数学优化建模。例如，航空公司、银行和科技公司在运营中使用的技能和技术。 2. **Excel中的假设分析** 这一模块引入更复杂的电子表格模型，回顾基本函数和高级技巧，关注如何利用Excel功能深入分析实际商业问题。 3. **通过回归和净现值进行决策分析** 本模块使用回归分析来估算变量之间的关系，解决现实商业问题，并分析最佳曲线模型，以理解模型使用的合理性。 4. **线性规划** 介绍电子表格优化，特别是线性规划（LP），一种在各种组织中解决多种问题的强大方法。涵盖劳动调度、库存管理等应用。 5. **运输和分配问题** 提供更多线性规划（LP）的建模例子，解决运输问题（优化货物分配）和分配问题（个体或设备的一对一最佳分配）。 6. **整数规划与非线性规划** 介绍在LP模型基本假设变化时产生的重要数学模型，如整数规划（决策变量为整数）和二元编程，以及如何解决涉及非线性模型的复杂问题。 本课程通过Excel工具，教授学生如何在实际商业环境中应用数据分析方法，以做出更好的决策。
Name:Introduction to Excel: Basics and Best Practices
Description:The purpose of this course is to expose you to a variety of problems that can be solved using management science methods and modelled in Excel. In this course, we start from the basics of spreadsheet design and work our way up to broader mathematical optimization modelling. Many airlines, banks, and technology companies could not operate today as they do without the skills and techniques taught in this course. In this first module, we begin by introducing a relatively simple example of a mathematical model which we will use as our platform to build off of for more complicated applications later in the course. Many problems used in the video lectures come from the text Business Analytics: Data Analysis & Decision Making by Albright & Winston (Cengage Learning, 2014), ISBN 1285965523
Name:What-If Analysis in Excel
Description:We are now ready to introduce more complexity to our spreadsheet models. Since everyone comes from different Excel backgrounds, we will review some basic functions and features as well as more advanced techniques. This module covers more of the modelling process and includes some of the less-well known, but particularly helpful, Excel functions and tools that are available. Remember though that this course's objective is not to be a "how-to" of Excel. Instead, the focus and intent is to use these features to provide insights into real business problems.
Name:Decision Analysis through Regression and NPV
Description:In this module the modeling concept of estimating relationships between variables by curve fitting, or regression analysis, is used to solve realistic business problems. Different regression curves are introduced and a mathematical analysis of which curve is best to help defend the model is presented. This allows not only an understanding of the techniques of modelling but also the rational behind which model to use.
Name:Linear Programming
Description:In this module we introduce spreadsheet optimization, one of the most powerful and flexible methods of quantitative analysis. The specific type of optimization presented here is linear programming (LP) which is used in all types of organizations to solve a wide variety of problems. As you will see through the examples presented in this course, LP is used in problems of labor scheduling, inventory management, advertising, finance, transportation, staffing, and many others. The goal of this module is to introduce you to the basic elements of LP, the types of problems it can solve, and how to model an LP problem in excel.
Name:Transportation and Assignment Problems
Description:This module provides even more examples of problems that can be modeling using linear programming (LP), in particular Transportation and Assignment problems. The basic transportation problem is concerned with finding the best (usually the least cost) way to distribute the good from sources such as factories, to final destinations such as retail outlets. The assignment problem involves finding the best (usually the least cost) way to assign individuals or pieces of equipment to projects or jobs on a one-to-one basis. Using Solver, we will take advantage of the special structure of these LP problems to find the best solutions to complex business problems in an efficient way.
Name:Integer Programming and Nonlinear Programming
Description:This module presents yet another subset of important mathematical linear programming models that arise when some of the basic assumptions of an LP model are made more or less restrictive. For example, restricting the decision variables to be whole numbers leads to the process of Integer Programming. Restricting the decision variables to be either 0 or 1 leads to binary programming. Lastly, we will see how the skills in this course can be used to solve more complex problems that involve nonlinear models.
A leader in a data driven world requires the knowledge of both data-related (statistical) methods and of appropriate models to use that data. This Business Analytics class focuses on the latter: it introduces students to analytical frameworks used for decision making though Excel modeling. These include Linear and Integer Optimization, Decision Analysis, and Risk modeling. For each methodology students are first exposed to the basic mechanics, and then apply the methodology to real-world busines
