Introduction to the Course

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En provenance du cours de University of Colorado Boulder

Business Analytics for Decision Making

139 notes

University of Colorado Boulder

Business Analytics for Decision Making

139 notes

Cours 3 sur 5 dans Specialization Advanced Business Analytics

In this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation. You will then learn the basics of Monte Carlo simulation that will help you model the uncertainty that is prevalent in many business decisions. A key element of decision making is to identify the best course of action. Since businesses problems often have too many alternative solutions, you will learn how optimization can help you identify the best option. What is really exciting about this course is that you won’t need to know a computer language or advanced statistics to learn about these predictive and prescriptive analytic models. The Analytic Solver Platform and basic knowledge of Excel is all you’ll need. Learners participating in assignments will be able to get free access to the Analytic Solver Platform.

À partir de la leçon

Data Exploration and Reduction — Cluster Analysis

Introduction to the Course3:26

0. What is Cluster Analysis7:42

Rencontrer les enseignants

Manuel Laguna
Professor
Leeds School of Business

Welcome to the third course of the Data Analytics for Business Specialization.

In this course,

we describe how data analytics is used to create models for decision making.

The goal for

this course is to make the transition from predictive models to prescriptive models.

As the name indicates,

prescriptive analytic models prescribe courses of action.

This is done by establishing an objective in the set of constraints.

The course creates a bridge between predicting outcomes, and

prescribing solutions to business problems.

In Module one, we started with cluster analysis.

As my colleagues Dave and Dan did in previous courses,

I will follow the same approach of introducing modeling and

analysis techniques within the context of business applications.

For cluster analysis, we will focus on market segmentation.

The main idea in market segmentation is to divide a broad market into segments or

clusters.

The technique produces groups whose members have similar needs,

interests, and priorities.

These help companies design a specific strategies.

For example, an advertising campaign, or

a set of promotions, to target each market segment.

In the second model,

we examine the main techniques to deal with uncertainty in business problems.

We will discuss why using average values for

something that we don't know, for example demand, is not always the best idea.

Overbooking will be the main business application in this module.

This is the perfect example for this module,

because the uncertainty of whether a customer will show up for an appointment,

or a hotel reservation, or a flight, has immediate revenue implications.

In the third module, we will learn how to develop and solve optimization models.

Optimization is a core technique in prescriptive analytics.

This makes a lot of sense, because you want models that are able to find

the best possible course of action for a given objective.

The beauty of optimization is that it is capable of examining

a very large number of possible solutions to a problem.

So it does something that would require a lot of effort

if we had to do it by trial and error.

We will use a transportation problem and a decision problem in digital advertising,

to illustrate how optimization models work.

Simulation optimization, the topic of module four,

is one of my favorite things to teach.

In the Masters of Business Analytics here at the University of Colorado,

Leeds School of Business, we spend more time in this topic

than the time that we're going to be able to spend in this course.

However, you will get a good overview of the main concepts.

The reason this is so exciting, is that it combines simulation and optimization.

Simulation allows you to model uncertainty, and

when you combine it with optimization,

you're able to determine the best course of action in the face of uncertainty.

I am glad that you're doing this specialization, and that you're ready for

this third course.

I hope that you enjoyed taking it as much as I have enjoyed putting it together.

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