Introduction to Predictive Modeling
Learn essential predictive modeling techniques using Excel, from linear regression to time series forecasting for data-driven decisions.
Learn essential predictive modeling techniques using Excel, from linear regression to time series forecasting for data-driven decisions.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Analytics for Decision Making Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.8
(110 ratings)
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English
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What you'll learn
Master simple and multiple linear regression techniques
Develop skills in data preparation and transformation
Learn time series forecasting methods including Holt-Winters
Understand model selection and evaluation
Apply predictive modeling to real-world business problems
Skills you'll gain
This course includes:
6.2 Hours PreRecorded video
20 assignments
Access on Mobile, Desktop, Tablet
FullTime access
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There are 4 modules in this course
This comprehensive course introduces students to predictive modeling concepts and their practical applications using Microsoft Excel. Starting with linear regression fundamentals, the curriculum progresses through multiple regression, data preparation techniques, and time series forecasting. Students learn to build, evaluate, and select appropriate models while gaining hands-on experience with real-world datasets. The course emphasizes both theoretical understanding and practical implementation, making it ideal for business analysts and decision-makers seeking to leverage data for predictions.
Simple Linear Regression
Module 1 · 2 Hours to complete
Multiple Linear Regression
Module 2 · 2 Hours to complete
Data Preparation
Module 3 · 2 Hours to complete
Time Series Forecasting
Module 4 · 5 Hours to complete
Fee Structure
Instructor
Digital Economics Expert and Business Analytics Scholar
Dr. De Liu serves as Xian Dong Eric Jing Professor of Information and Decision Sciences at the University of Minnesota's Carlson School of Management. His academic credentials include a Ph.D. in Management Science and Information Systems from the University of Texas at Austin, and both Master's and Bachelor's degrees from Tsinghua University. Through his Coursera courses in business analytics and digital economics, he helps students understand complex market mechanisms and data analysis. His research focuses on digital economics, particularly examining mechanism design in auctions, gamification, crowdsourcing, crowdfunding, and social commerce. As Academic Director of the Master of Science in Business Analytics program, he shapes the next generation of business analytics professionals. His work has appeared in leading journals including MIS Quarterly, Management Science, and Information Systems Research. His expertise spans artificial intelligence applications, digital engagement strategies, and platform economics, combining theoretical frameworks with practical business applications
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4.8 course rating
110 ratings
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