The analytics process is a collection of interrelated activities that lead to better decisions and to a higher business performance. The capstone of this specialization is designed with the goal of allowing you to experience this process. The capstone project will take you from data to analysis and models, and ultimately to presentation of insights.
Advanced Business Analytics Capstone
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Advanced Business Analytics Capstone
This course is part of Advanced Business Analytics Specialization
Instructors: Manuel Laguna
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Gain insight into a topic and learn the fundamentals.
4.3
(81 reviews)
Intermediate level
Some related experience required
19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Create a well-structured and compelling presentation that showcases the most relevant and impactful insights from the analytics project
Design and customize predictive analytics models for loan classification and loss prediction
Devise investment fund allocation recommendations based on clustering and simulation-based optimization techniques
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Taught in English
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This course is part of the Advanced Business Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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.
What's included
5 videos2 readings1 peer review
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. <p> 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. </p>
What's included
4 videos1 peer review
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. <p>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. </p>
What's included
1 peer review
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. Good luck!
What's included
1 peer review
Instructors
Instructor ratings
4.3 (11 ratings)Offered by
Recommended if you're interested in Data Analysis
University of California, Irvine
Wesleyan University
Corporate Finance Institute
University of Pennsylvania
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