University of Colorado Boulder
Advanced Business Analytics Capstone
University of Colorado Boulder

Advanced Business Analytics Capstone

This course is part of Advanced Business Analytics Specialization

Taught in English

Some content may not be translated

Manuel Laguna
Dan Zhang
David Torgerson

Instructors: Manuel Laguna

9,015 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.3

(79 reviews)

Intermediate level
Some related experience required
19 hours (approximately)
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

Details to know

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Course

Gain insight into a topic and learn the fundamentals

4.3

(79 reviews)

Intermediate level
Some related experience required
19 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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 (10 ratings)
Manuel Laguna
University of Colorado Boulder
4 Courses96,118 learners

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