Business analysts need to be able to prescribe optimal solution to problems. But analytics courses are often focused on training students in data analysis and visualization, not so much in helping them figure out how to take the available data and pair that with the right mathematical model to formulate a solution. This course is designed to connect data and models to real world decision-making scenarios in manufacturing, supply chain, finance, human resource management, etc. In particular, we understand how linear optimization - a prescriptive analytics method - can be used to formulate decision problems and provide data-based optimal solutions. Throughout this course we will work on applied problems in different industries, such as:
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第 3 门课程（共 4 门）
初级
Familiarity with Excel spreadsheets
完成时间大约为14 小时
英语（English）
字幕：英语（English）
您将获得的技能
AnalyticsOperations ManagementLinear Programming (LP)Mathematical Optimization
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 4 门）
初级
Familiarity with Excel spreadsheets
完成时间大约为14 小时
英语（English）
字幕：英语（English）
提供方
明尼苏达大学
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
授课大纲 - 您将从这门课程中学到什么
完成时间为 4 小时
Module 1: LPs for Financial Decisions
In this module we will look at examples illustrating the application of linear optimization in finance. In particular, we will learn to formulate problems in investment portfolio optimization and multi-period cash flow management.
完成时间为 4 小时
14 个视频 （总计 111 分钟）, 1 个阅读材料, 2 个测验
完成时间为 3 小时
Module 2: LP for Supply Chain Decisions
This module explores the use of linear optimization in supply chain decisions, particularly in the context of inventory transportation or logistics problems as well as in production and inventory management.
完成时间为 3 小时
10 个视频 （总计 53 分钟）
完成时间为 3 小时
Module 3: LP for Staffing Decisions
This module explores how human resource managers can use optimization as a prescriptive analytics tool to plan staff schedules, room allocation, and workforce size management.
完成时间为 3 小时
11 个视频 （总计 67 分钟）, 1 个阅读材料, 2 个测验
完成时间为 4 小时
Module 4: LP for Production Decisions
Linear optimization plays an important role in the decision making process in the manufacturing sector. This module explores how optimization can be used to prescribe product mix and blending decisions.
完成时间为 4 小时
11 个视频 （总计 56 分钟）, 3 个阅读材料, 2 个测验
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来自ADVANCED MODELS FOR DECISION MAKING的热门评论
由 NR 提供Sep 18, 2021
The concepts are explained well. The lectures are very detail oriented. I just wish there were more examples to follow.
由 JH 提供Jun 2, 2021
All of the courses in this series are very practical based and give a lot of practice in using the tools.
由 CL 提供May 18, 2021
Really enjoyed the lesson. Learned application of optimization analytics in various setting.
关于 Analytics for Decision Making 专项课程
The field of analytics is typically built on four pillars: Descriptive Analytics, Predictive Analytics, Causal Analytics, and Prescriptive Analytics. Descriptive analytics (e.g., visualization, BI) deal with the exploration of data for patterns, predictive analytics (e.g., data mining, time-series forecasting) identifies what can happen next, causal modeling establishes causation, and prescriptive analytics help with formulating decisions. This specialization focuses on the Prescriptive Analytics (the final pillar). This specialization will review basic predictive modeling techniques that can be used to estimate values of relevant parameters, and then use optimization and simulation techniques to formulate decisions based on these parameter values and situational constraints. The specialization will teach how to model and solve decision-making problems using predictive models, linear optimization, and simulation methods.
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