University of Colorado Boulder - Business Analytics for Decision Making
4.5 /5- Offered byCoursera
Business Analytics for Decision Making at Coursera Overview
Duration | 11 hours |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
Future job roles | Appraisal |
Business Analytics for Decision Making at Coursera Highlights
- Requires effort of 3-4 hours per week
- Learn from expert faculty of University of Colorado Boulder
- Earn a certificate from University of Colorado Boulder
- Transcripts available in English, Korean and Arabic subtitles
Business Analytics for Decision Making at Coursera Course details
- 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.
- In this course you will learn how to create models for decision making.
Business Analytics for Decision Making at Coursera Curriculum
Data Exploration and Reduction Cluster Analysis
Introduction to the Course
What is Cluster Analysis
Data Reduction and Unsupervised Learning
Preparing Data and Measuring Dissimilarities
Hierarchical and k-Means Clustering
Cluster Analysis with Excel
Cluster Analysis with XLMiner
Dealing with Uncertainty and Analyzing Risk
Risk Analysis and Monte Carlo Simulation
Adding Uncertainty to a Spreadsheet Model
Defining Output Variables and Analyzing the Results
Using Historical Data to Model Uncertainty
Models with Correlated Uncertain Variables
Creating and Interpreting Charts
Using Average Values versus Simulation
Identifying the Best Options Optimization
Optimization and Decision Making
Formulating an Optimization Problem
Developing a Spreadsheet Model
Adding Optimization to a Spreadsheet Model
What-if Analysis and the Sensitivity Report
Evaluating Scenarios and Visualizing Results to Gain Practical Insights
Digital Marketing Application of Optimization
Decision Analytics
Advanced Models for Better Decisions
Business Problems with Yes/No Decisions
Formulation and Solution of Binary Optimization Problems
Metaheuristic Optimization
Chance Constraints and Value At Risk
Simulation Optimization
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