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Learner Reviews & Feedback for Introduction to Predictive Modeling by University of Minnesota

4.8
stars
127 ratings

About the Course

Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization. This course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel. By the end of the course, you will be able to: - Understand the concepts, processes, and applications of predictive modeling. - Understand the structure of and intuition behind linear regression models. - Be able to fit simple and multiple linear regression models to data, interpret the results, evaluate the goodness of fit, and use fitted models to make predictions. - Understand the problem of overfitting and underfitting and be able to conduct simple model selection. - Understand the concepts, processes, and applications of time series forecasting as a special type of predictive modeling. - Be able to fit several time-series-forecasting models (e.g., exponential smoothing and Holt-Winter’s method) in Excel, evaluate the goodness of fit, and use fitted models to make forecasts. - Understand different types of data and how they may be used in predictive models. - Use Excel to prepare data for predictive modeling, including exploring data patterns, transforming data, and dealing with missing values. This is an introductory course to predictive modeling. The course provides a combination of conceptual and hands-on learning. During the course, we will provide you opportunities to practice predictive modeling techniques on real-world datasets using Excel. To succeed in this course, you should know basic math (the concept of functions, variables, and basic math notations such as summation and indices) and basic statistics (correlation, sample mean, standard deviation, and variance). This course does not require a background in programming, but you should be familiar with basic Excel operations (e.g., basic formulas and charting). For the best experience, you should have a recent version of Microsoft Excel installed on your computer (e.g., Excel 2013, 2016, 2019, or Office 365)....

Top reviews

NR

Sep 18, 2021

Loved the forecasting lecture. I've used other forecasting methods but learned the composite method first time. Highly recommended course for supply chain and manufacturing students and professionals.

KK

Oct 16, 2021

T​his course is amazing. very well structured and logical teaching sequence and explaination. I've learned through this course more than the lectures from my university. thanks a lot !

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26 - 35 of 35 Reviews for Introduction to Predictive Modeling

By Khubaib K

•

Sep 10, 2021

best instructor

By Raihan M

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Nov 18, 2023

Very good!

By Nazar K

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Apr 17, 2022

i like it

By AHMAD N A A

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Dec 12, 2023

great

By Toby B

•

Jul 12, 2023

I really enjoyed how the course was geared towards applying the theory. Very useful practical information and well presented!

By Vishwanath S

•

Aug 22, 2022

Contents presentation is very good.

Given 1 star less due to non inclusion of ARIMA models.

By sayan g

•

Nov 6, 2024

Great

By Kelly P

•

Mar 14, 2025

This course is a comprehensive introduction to predictive modelling: professor De Liu does an excellent job in presenting difficult and counter-intuitive ideas in a relatively simple way. What I mostly appreciated was his step-by-step approach in EXCEL which made miracles in practicing the modelling techniques. The major drawback of this course (and the reason for my 3-stars) is that there is no follow-up when a question rises up in the DISCUSSION FORUM; as a result if a student needs some support, it is quite unlikely that the course supporting team will actually react. As seen in the DISCUSSION FORUM, there are also technical errors in the teaching material/quizes that remain unresolved for years and this is obnoxious. Students would also gain from extra optional solved exercises and some further review reading on the presented methodologies.

By J S B

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Mar 11, 2024

It was great until the last week, making you interpret excell formulas instad of calculating values makes it harder in an useless way. It could have asked the values so we had to create the model instead.

By Michael O

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May 25, 2022

I found that the course exams questions were difficult to read for a dyslexic because of the cell format and font