Business Analytics for Decision Making

Business Analytics for Decision Making

This course is part of Advanced Business Analytics Specialization

Instructor: Manuel Laguna

Access provided by Coursera Learning Team

89,752 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

4.6

(1,850 reviews)

Beginner level

No prior experience required

Flexible schedule

Approx. 8 hours

Learn at your own pace

95%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

4.6

(1,850 reviews)

Beginner level

No prior experience required

Flexible schedule

Approx. 8 hours

Learn at your own pace

95%

Most learners liked this course

What you'll learn

Describe the significance of optimization in identifying optimal solutions for complex business problems
Implement optimization techniques to identify the best possible course of action among multiple alternatives
Judge the appropriateness of optimization techniques in finding optimal solutions for practical business challenges

Skills you'll gain

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Assessments

8 assignments

Taught in English

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There are 4 modules in this course

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.

What's included

7 videos1 reading2 assignments1 discussion prompt

7 videosTotal 53 minutes

Introduction to the Course3 minutesPreview module
0. What is Cluster Analysis7 minutes
1. Data Reduction and Unsupervised Learning7 minutes
2. Preparing Data and Measuring Dissimilarities8 minutes
3. Hierarchical and k-Means Clustering7 minutes
4. Cluster Analysis with Excel8 minutes
5. Cluster Analysis with XLMiner9 minutes

1 readingTotal 10 minutes

2 assignmentsTotal 60 minutes

Week 1 Quiz30 minutes
Week 1 Application Assignment - Clustering30 minutes

1 discussion promptTotal 20 minutes

Week 1 Discussion: Cluster Analysis in Nilsen Analytics Tools20 minutes

What's included

7 videos2 assignments1 discussion prompt

7 videosTotal 51 minutes

0. Risk Analysis and Monte Carlo Simulation7 minutesPreview module
1. Adding Uncertainty to a Spreadsheet Model9 minutes
2. Defining Output Variables and Analyzing the Results7 minutes
3. Using Historical Data to Model Uncertainty7 minutes
4. Models with Correlated Uncertain Variables5 minutes
5. Creating and Interpreting Charts8 minutes
6. Using Average Values versus Simulation5 minutes

2 assignmentsTotal 60 minutes

Week 2 Quiz30 minutes
Week 2 Application Assignment - Monte Carlo Simulation30 minutes

1 discussion promptTotal 10 minutes

Week 2 Discussion: How Betterment Holdings Inc. Used Monte Carlo Simulation10 minutes

At the end of this module students should be able to: 1. Develop a spreadsheet model for an optimization problem 2. Use Excel to solve optimization models 3. Interpret solutions and conduct what-if analysis

What's included

7 videos2 assignments1 discussion prompt

7 videosTotal 54 minutes

0. Optimization and Decision Making5 minutesPreview module
1. Formulating an Optimization Problem7 minutes
2. Developing a Spreadsheet Model7 minutes
3. Adding Optimization to a Spreadsheet Model5 minutes
4. What-if Analysis and the Sensitivity Report9 minutes
5. Evaluating Scenarios and Visualizing Results to Gain Practical Insights9 minutes
6. Digital Marketing Application of Optimization8 minutes

2 assignmentsTotal 75 minutes

Week 3 Quiz30 minutes
Week 3 Application Assignment - Linear Optimization45 minutes

1 discussion promptTotal 10 minutes

Week 3 Discussion: How Amazon Uses Optimization for Its Supply Chain10 minutes

At the end of this module students should be able to: 1. Given a business situation, apply an appropriate technique to identify the best solution alternatives 2. Formulate and solve models for business problems that requires yes/no decisions and logical constraints 3. Create models that mix techniques and tools such as simulation and optimizationAnalyze and interpret results to make informed decisions

What's included

6 videos2 assignments1 discussion prompt

6 videosTotal 39 minutes

0. Advanced Models for Better Decisions3 minutesPreview module
1. Business Problems with Yes/No Decisions5 minutes
2. Formulation and Solution of Binary Optimization Problems7 minutes
3. Metaheuristic Optimization8 minutes
4. Chance Constraints and Value At Risk6 minutes
5. Simulation Optimization6 minutes

2 assignmentsTotal 75 minutes

Week 4 Quiz30 minutes
Week 4 Application Assignment - Simulation Optimization45 minutes

1 discussion promptTotal 10 minutes

Week 4 Discussion: How Uber Optimized Its Dispatch System10 minutes

Instructor

Instructor ratings

4.6 (473 ratings)

Manuel Laguna

University of Colorado Boulder

4 Courses101,680 learners

Offered by

University of Colorado Boulder

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Learner reviews

4.6

1,850 reviews

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Showing 3 of 1850

Reviewed on Jul 11, 2017

The instructor is great. However, I would like to know the solutions for the exam. I sometimes pass the exam but struggle with certain questions but had no clue how to resolve it.

Reviewed on Jun 27, 2020

Through this course now i m able to to understand difference in all aspects, when to use which model ,how to perform simulation and optimization using Analytic Solver Platform.

Reviewed on Jul 17, 2019

Training course was very useful in decision making with analytical tools to cope up with risk analysis, data optimization, evaluating scenarios, binary model decision making etc.

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