Advanced Business Analytics Capstone
- Module 1 - Understand the data and prepare your data for analysis
- This week your goal is to understand the data and prepare the data for analysis. As we discussed in this specialization, data preprocessing and cleanup is often the first step in data analytics projects. Needless to say, this step is crucial for the success of this project. We've selected a few videos from Courses 2 and 4 for you to review before completing this week's assignments. Dealing With Missing Values and Dealing with Outliers videos will remind you how to perform preliminary data cleanups. The last part of the assignments ask you to construct data visualizations. You may find the ideas discussed in What is Good Data Visualization? and Graphical Excellence useful.
- Module 2 - Perform predictive analytics tasks
- This week you will perform some predictive analytics tasks, including classifying loans and predicting losses from defaulted loans. You will try a variety of tools and techniques this week, as the predictive accuracy of different tools can vary quite a bit. It is rarely the case that the default model produced by ASP is the best model possible. Therefore, it is important for you to tune the different models in order to improve the performance.This week’s assignments require you to build predictive models for both classification and regression tasks.
Before working on the assignments, you may review a few videos to remind yourself several important concepts, such as cross validation. These concepts are discussed in the videos Cross Validation and Confusion Matrix and Assessing Predictive Accuracy Using Cross-Validation. You may also find a refresher on XLMiner useful. The videos Building Logistic Regression Models using XLMiner and How to Build a Model using XLMiner discuss how to build logistic regression and linear regression models. Depending on your needs, you may also go back to the videos that discuss how to build trees and neural networks.
- Module 3 - Provide suggestions on how to allocate investment funds using prescriptive analytics tools
- This week we turn our attention to prescriptive analytics, where you will provide some concrete suggestions on how to allocate investment funds using analytics tools, including clustering and simulation-based optimization. You will see that allocating funds wisely is crucial for the financial return of the investment portfolio.
The relevant videos for this week are from Course 3: Week 1: Cluster analysis with XLMiner, Week 2: Adding uncertainty to spreadsheet model, Week 2: Defining output variables and analyzing results.
- Module 4 - Present your analytics results to your clients
- You have done a lot so far! In this last week, you will present to your analytics results to your clients. Since you have many results in your project, it is important for you to judiciously choose what to include in your presentation. Several videos in Course 4 offer some guidelines on communicating analytics results. This assignment will give you an opportunity to apply the skills you learned there.
- Fecha Incio:16/08/2021
- Idioma: Inglés
- Universidad: University of Colorado Boulder
- Profesores: Manuel Laguna, Dan Zhang and David Torgerson
- Certificado: Si