Predictive Modeling and Analytics

About this course: Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business. You’ll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way. We will use a practical predictive modeling software, XLMiner, which is a popular Excel plug-in. This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning. The expected prerequisites for this course include a prior working knowledge of Excel, introductory level algebra, and basic statistics.

Who is this class for: This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning. This course is primarily aimed at professionals who have a bachelor’s degree and/or some exposure to the business world. The software tool used in the course is based on Microsoft Excel and therefore is accessible for anyone with some prior exposure to Excel. Those with technical degrees or more advanced business degrees like an MBA will find certain areas easier to absorb, and may get maximum value from the course. However, even undergraduates in non-technical fields or advanced high-school students pursuing internships will be able to follow most concepts and get value from the course. Finally, even professionals who have had deep experiences in methods will likely find value in this course.

Created by:  University of Colorado Boulder

• Taught by:  Dan Zhang, Professor

  Leeds School of Business

Basic Info Course 2 of 5 in the Advanced Business Analytics Specialization Language English Hardware Req As this course uses the XLMiner Data Mining Add-In for Excel extensively, please refer to this link if you are using a Mac: http://www.solver.com/using-frontline-solvers-macintosh How To Pass Pass all graded assignments to complete the course. User Ratings 3.6 stars Average User Rating 3.6See what learners said 课程作业

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Syllabus


WEEK 1


Exploratory Data Analysis and Visualizations



At the end of this module students will be able to: 1. Carry out exploratory data analysis to gain insights and prepare data for predictive modeling 2. Summarize and visualize datasets using appropriate tools 3. Identify modeling techniques for prediction of continuous and discrete outcomes. 4. Explore datasets using Excel 5. Explain and perform several common data preprocessing steps 6. Choose appropriate graphs to explore and display datasets


8 videos, 1 reading, 1 practice quiz expand

1. Video: Introduction to the Course
2. Video: 0. Introduction to the Module. Why Exploratory Data Analysis is Important
3. Video: 1. Data Cleanup and Transformation
4. Video: 2. Dealing With Missing Values
5. Video: 3. Dealing with Outliers
6. Video: 4. Adding and Removing Variables
7. Video: 5. Common Graphs
8. Video: 6. What is Good Data Visualization?
9. 讨论提示: Data Exploration
10. 阅读: Register for Analytic Solver Platform for Education (ASPE)
11. 练习测验: Week 1 Application Assignment 1 (optional): Data Cleanup

Graded: Week 1 Quiz
Graded: Week 1 Application Assignment 2: Data Visualization

WEEK 2


Predicting a Continuous Variable



This module introduces regression techniques to predict the value of continuous variables. Some fundamental concepts of predictive modeling are covered, including cross-validation, model selection, and overfitting. You will also learn how to build predictive models using the software tool XLMiner.


8 videos expand

1. Video: 0. Introduction to Predictive Modeling
2. Video: 1. Introduction to Linear Regression
3. Video: 2. Assessing Predictive Accuracy Using Cross-Validation
4. Video: 3. Multiple Regression
5. Video: 4. Improving Model Fit
6. Video: 5. Model Selection
7. Video: 6. Challenges of Predictive Modeling
8. Video: 7. How to Build a Model using XLMiner
9. 讨论提示: Reflection on Statistical Techniques

Graded: Week 2 Quiz
Graded: Week 2 Application Assignment

WEEK 3


Predicting a Binary Outcome



This module introduces logistic regression models to predict the value of binary variables. Unlike continuous variables, a binary variable can only take two different values and predicting its value is commonly called classification. Several important concepts regarding classification are discussed, including cross validation and confusion matrix, cost sensitive classification, and ROC curves. You will also learn how to build classification models using the software tool XLMiner.


8 videos expand

1. Video: 0. Introduction to classification
2. Video: 1. Introduction to Logistic Regression
3. Video: 2. Building Logistic Regression Model
4. Video: 3. Multiple Logistic Regression
5. Video: 4. Cross Validation and Confusion Matrix
6. Video: 5. Cost Sensitive Classification
7. Video: 6. Comparing Models Independent of Costs and Cutoffs
8. Video: 7. Building Logistic Regression Models using XLMiner
9. 讨论提示: The Best Prediction Method

Graded: Week 3 Quiz
Graded: Week 3 Application Assignment

WEEK 4


Trees and Other Predictive Models



This module introduces more advanced predictive models, including trees and neural networks. Both trees and neural networks can be used to predict continuous or binary variables. You will also learn how to build trees and neural networks using the software tool XLMiner.


8 videos, 1 practice quiz expand

1. Video: 0.Introduction to Advanced Predictive Modeling Techniques
2. Video: 1. Introduction to Trees
3. Video: 2. Classification Trees
4. Video: 3. Regression Trees
5. Video: 4. Bagging, Boosting, Random Forest
6. Video: 5. Neural Networks
7. Video: 6. Building Trees with XLMiner
8. Video: 7. Building Neural Networks using XLMiner
9. 讨论提示: Reflection: Trees & Neural Networks
10. 练习测验: Final Course Assignment Quiz

Graded: Week 4 Quiz
Graded: Week 4 Application Assignment
Graded: Final Course Assignment Peer Review