Name: Introduction to Predictive Modeling
Brand: University of Minnesota
Availability: OnlineOnly
Rating: 4.8 (36 reviews)

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学生对 University of Minnesota 提供的 Introduction to Predictive Modeling 的评价和反馈

4.8

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137 个评分

课程概述

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)....

26 - Introduction to Predictive Modeling 的 36 个评论（共 36 个）

创建者 Khubaib K

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Sep 10, 2021

best instructor

创建者 Raihan M

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Nov 18, 2023

Very good!

创建者 Nazar K

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Apr 17, 2022

i like it

创建者 AHMAD N A A

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Dec 12, 2023

great

创建者 Toby B

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Jul 12, 2023

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

创建者 Siddh D

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May 1, 2025

Great course. Easy to follow through. A suggestion would be to make a compilation of all the formulas on a doc for ease.

创建者 Vishwanath S

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Aug 22, 2022

Contents presentation is very good.

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

创建者 sayan g

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Nov 6, 2024

Great

创建者 Kelly P

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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.

创建者 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.