内容和教授

本课程将介绍如何使用Python构建分析模型. The session will focus on how Python code snippets can be edited to instantiate different types of models.

Once we are familiar with the basic concepts of descriptive statistics we can take on more complex tasks. In this session students investigate how to implement these concepts in Excel and how to use statistics to generate conclusions. The first topic covered here is hypothesis tests; the primary decision-making tool in statistics. 我们首先将假设检验应用于单变量问题. 更现实的模型需要多个变量. 在处理多个变量时，我们建立回归模型. The objective is to develop a model where the dependent variable is explained by one or more independent variables. 这允许识别决定因变量结果的因素. Students will become familiar with implementing regression in Excel and interpreting the Excel output.

Linear programming (LP) is a tool for the mathematical optimization of complex decision problems. 这节课从一个有趣的开始, 亲自动手的, 从生产计划中抽取的交互式问题解决练习. This example is used to introduce the basic concepts behind LP as a mathematical model of the exercise is developed and then solved using the Solver in Microsoft Excel. The session concludes with students building small models in Excel using examples drawn from distribution planning and financial management.

一些现实世界的问题不能用纯粹的优化技术来建模, 尤其是那些有偶然因素的. 在这些情况下，我们经常求助于模拟，这是一种结合决策的方法.g.例如，要雇用多少服务代表)和不确定性(例如.g.，服务需求). Simulation involves building a computer model that generates probabilistic outcomes based on the decisions we make and the uncertainties we face. One can use simulation models to determine how specific decisions will perform in practice without experiencing their effects firsthand. 在这个讲座中, we will demonstrate how many complicated problems can be analyzed using simulation models built in spreadsheets.

This lecture is designed to introduce you to some of the models and methods used in the emerging field of revenue management (RM). The problem that motivates RM is ancient: how does a company manage its capacity and/or prices to extract the greatest possible revenue from the marketplace? RM方法是利用客户群体的差异和他们的支付意愿. 例如, a person who books a room in an upscale hotel three days in advance is typically willing to pay much more than someone booking the same room three months in advance. RM专注于如何管理这些不同类型的客户需求以最大化收入.

这次班会的重点是不确定条件下的决策. 对于一类很大的决策问题, 决定的结果是不确定的, but the decision-maker is able to list the possible outcomes of a decision and assign a probability to each of them. 不确定性的来源包括消费者需求等因素, 竞争对手的行为, 自然的行为. 在决策过程中处理这些不确定因素, 决策树已被证明是一种强大的图形工具. Decision trees allow a decision-maker to consider the possible outcomes of his decisions in a systematic way and to draw correct conclusions with respect to the best course of action. Spreadsheet implementation of decision trees further extend to the decision-maker the ability to perform sensitivity analysis to gain a deeper understanding of how sensitive the optimal solution is to changes or inaccuracies in model assumptions.

无论您和您的团队专注于什么行业，都可以是制造业, 金融服务, 生命科学或电子项目是创新的关键载体, 生产力的提高, 和经济增长. The ability to manage the crisp and consistent execution of projects is becoming a crucial capability for the success of most ventures. 事实上, 作为一家公司，其产品变得更加数字化和/或以服务为导向, 制造业和组装工作减少, 而项目的规划和执行起着至关重要的作用. 本课程将讨论项目管理的要点, 广泛地涵盖项目执行的技术和社会方面.

预测几乎是制定任何商业计划的必要步骤. 例如, 没有良好的销售预测, 库存管理和产能规划可能会严重出错. And almost any reliable budget depends on a reliable forecast of the variables influencing cost. 因此，分析师们开发了许多不同的预测技术. 本节将探讨几种最常用的预测方法. A special emphasis is placed on how to integrate forecasting into the business planning process. 练习将使用excel和专门的预测软件.

本课程将讨论数据库管理系统的概述, 包括关系型和联机分析处理系统. A real world example will show managing data for a web site; from online ordering to Pick and Pack. 本课程还将介绍基本的结构化查询语言(SQL)语句. 课堂实验室将采用一系列平面文件并将它们转换为数据库.

几乎每家公司都花时间检索数据并将其放入电子表格中.这可能会浪费大量的时间，而且往往是一个机器人式的过程.The module will discuss ways to automate this process with Visual Basic for Applications (VBA).A real world corporate example will show how one company retrieves data to help facilitate the management of data from four different reporting methods. 该实验将包含一个将数据导入电子表格的编码练习.

在本课程中, students learn to effectively communicate the results of business analytics and business decisions in written and oral presentations, 包括分析沟通的关键问题:发生了什么? 为什么会这样?? 下一步是什么? 本课程涵盖数据可视化方法, as well as how data visualization software such as Tableau can be used to dramatically improve data analysis  and managerial communications.

本部分提供了数据仓库和OLAP技术的路线图, 强调他们的新要求. 我们描述了用于提取的后端工具, cleaning and loading data into a data warehouse; multi-dimensional data models and OLAP operation; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. 我们调查了目前的技术状况，并提出了代表性的产品.

本课程涵盖基于人工智能和机器学习的两种类型的数据挖掘技术, 这对预测建模很有用. 它们是CART(分类和回归树)和神经网络. 本次会议的目标不仅是了解如何构建这样的模型, but also gain an appreciation for the particular circumstances in which each modeling technique is appropriate.

在本节中，我们将重点讨论数据挖掘的另外两种方法. 第一个, 我们研究聚类, 哪一种数据挖掘方法可以在广泛的数据集中发现不同的部分. We also discuss how clustering can be a useful step in developing multiple target marketing or segmented modeling strategies. 会议的后半部分考察了使用关联规则的市场篮子分析. 这是挖掘大型事务数据库以发现关联模式的有用方法. e.g.包括一起购买的产品，或同一买家在一段时间内购买的产品.

本次会议分为两个部分. 向学生介绍网络分析, the first half of the class explores how quantitative Internet data to optimize websites and web marketing initiatives is collected, 分析报告. 这些主题都是通过谷歌分析来探索的. Particular attention will be paid to the meaning of key metrics included in the Google Analytics suite based on an understanding of how the data from which they were derived. The second half of the class introduces students to the challenges and opportunities of social media. 网页和社交媒体分析之间的关键区别将被绘制出来.