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 business problems using Excel.
Business Analytics with Excel: Elementary to Advanced
Business Analytics with Excel: Elementary to Advanced
Instructor: Joseph W. Cutrone, PhD
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Intermediate level
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Approx. 23 hours
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There are 6 modules in this course
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
What's included
3 videos6 readings1 assignment1 discussion prompt
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.
What's included
2 videos5 readings4 assignments1 discussion prompt
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.
What's included
2 videos6 readings5 assignments1 discussion prompt
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.
What's included
4 videos7 readings5 assignments1 discussion prompt
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.
What's included
4 videos4 readings3 assignments1 discussion prompt
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.
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5 videos10 readings5 assignments
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