Predictive Modeling and Analytics

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Predictive Modeling and Analytics

This course is part of Advanced Business Analytics Specialization

Instructor: Dan Zhang

38,185 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

3.6

(597 reviews)

Intermediate level

Some related experience required

Flexible schedule

Approx. 10 hours

Learn at your own pace

62%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

3.6

(597 reviews)

Intermediate level

Some related experience required

Flexible schedule

Approx. 10 hours

Learn at your own pace

62%

Most learners liked this course

What you'll learn

Apply exploratory data analysis to gain insights and prepare data for predictive modeling
Summarize and visualize datasets using appropriate tools
Identify modeling techniques for prediction of continuous and discrete outcomes
Identify appropriate graphs to explore and display datasets

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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This course is part of the Advanced Business Analytics Specialization

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There are 4 modules in 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.

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

What's included

8 videos1 reading2 assignments1 peer review1 discussion prompt

8 videosTotal 37 minutes

Introduction to the Course1 minutePreview module
0. Introduction to the Module. Why Exploratory Data Analysis is Important3 minutes
1. Data Cleanup and Transformation4 minutes
2. Dealing With Missing Values6 minutes
3. Dealing with Outliers3 minutes
4. Adding and Removing Variables4 minutes
5. Common Graphs7 minutes
6. What is Good Data Visualization?4 minutes

1 readingTotal 10 minutes

2 assignmentsTotal 60 minutes

Week 1 Application Assignment 1 (optional): Data Cleanup30 minutes
Week 1 Quiz30 minutes

1 peer reviewTotal 60 minutes

Week 1 Application Assignment 2: Data Visualization60 minutes

1 discussion promptTotal 10 minutes

Data Exploration10 minutes

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.

What's included

8 videos2 assignments1 discussion prompt

8 videosTotal 40 minutes

0. Introduction to Predictive Modeling2 minutesPreview module
1. Introduction to Linear Regression8 minutes
2. Assessing Predictive Accuracy Using Cross-Validation5 minutes
3. Multiple Regression4 minutes
4. Improving Model Fit3 minutes
5. Model Selection3 minutes
6. Challenges of Predictive Modeling5 minutes
7. How to Build a Model using XLMiner8 minutes

2 assignmentsTotal 70 minutes

Week 2 Quiz30 minutes
Week 2 Application Assignment40 minutes

1 discussion promptTotal 10 minutes

Reflection on Statistical Techniques10 minutes

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.

What's included

8 videos2 assignments1 discussion prompt

8 videosTotal 32 minutes

0. Introduction to classification1 minutePreview module
1. Introduction to Logistic Regression4 minutes
2. Building Logistic Regression Model6 minutes
3. Multiple Logistic Regression3 minutes
4. Cross Validation and Confusion Matrix5 minutes
5. Cost Sensitive Classification2 minutes
6. Comparing Models Independent of Costs and Cutoffs3 minutes
7. Building Logistic Regression Models using XLMiner6 minutes

2 assignmentsTotal 60 minutes

Week 3 Quiz30 minutes
Week 3 Application Assignment30 minutes

1 discussion promptTotal 10 minutes

The Best Prediction Method10 minutes

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.

What's included

8 videos3 assignments1 peer review1 discussion prompt

8 videosTotal 31 minutes

0.Introduction to Advanced Predictive Modeling Techniques1 minutePreview module
1. Introduction to Trees2 minutes
2. Classification Trees5 minutes
3. Regression Trees2 minutes
4. Bagging, Boosting, Random Forest4 minutes
5. Building Trees with XLMiner5 minutes
6. Neural Networks5 minutes
7. Building Neural Networks using XLMiner4 minutes

3 assignmentsTotal 90 minutes

Final Course Assignment Quiz30 minutes
Week 4 Quiz30 minutes
Week 4 Application Assignment30 minutes

1 peer reviewTotal 120 minutes

Final Course Assignment Peer Review120 minutes

1 discussion promptTotal 10 minutes

Reflection: Trees & Neural Networks10 minutes

Instructor

Instructor ratings

3.0 (110 ratings)

Dan Zhang

University of Colorado Boulder

3 Courses51,960 learners

Offered by

University of Colorado Boulder

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Frequently asked questions

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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

Predictive Modeling and Analytics

What you'll learn

Skills you'll gain

Details to know

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

Build your subject-matter expertise

Earn a career certificate

There are 4 modules in this course

Exploratory Data Analysis and Visualizations

What's included

Predicting a Continuous Variable

What's included

Predicting a Binary Outcome

What's included

Trees and Other Predictive Models

What's included

Instructor

Offered by

Recommended if you're interested in Data Analysis

Predictive Modeling with Logistic Regression using SAS

Digital Marketing Analytics

Data Modeling and Applications

Regression Models

Why people choose Coursera for their career

Learner reviews

New to Data Analysis? Start here.

Open new doors with Coursera Plus

Advance your career with an online degree

Join over 3,400 global companies that choose Coursera for Business

Frequently asked questions

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