Top Daily Deal: Cometeer5% offShop Now
Introduction to Predictive Modeling

Introduction to Predictive Modeling

$49.00
Buy at Coursera

You check out on Coursera's own site. Members earn 30 points for heading to buy.

Affiliate link: if you complete a purchase, we may earn a small commission at no extra cost to you.

About this item

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).

Brand
Coursera
Type
Tech, Data & Science Courses (1012)
Item no.
crse:gXoO7BhYEeqmcg5cP6lwPw

Copies the link with your caption. Grab a username to bank points across devices.

More about the merchant on its brand profile.

Boost this product

See what's trending

Trade the points you've earned to push this up Trending and the homepage, and to the top of its category, where more readers will find it.

Be the first to review

Your rating:
Attach a gift:
Every comment or review earns 1 point.

Loading comments…

More tech, data & science courses

See all 1012 →
Responsible AI for Developers: Fairness & Bias - 日本語版

Responsible AI for Developers: Fairness & Bias - 日本語版

このコースでは、責任ある AI および AI に関する原則のコンセプトを紹介します。AI / ML の実践における公平性とバイアスを特定し、バイアスを軽減するための実践的な手法を取り扱います。具体的には、Google Cloud プロダクトとオープンソース ツールを使用して責任ある AI のベスト プラクティスを…

0 boosts in the last 24h

$49.00

Affiliate link: if you complete a purchase, we may earn a small commission at no extra cost to you.

More from Coursera

Shop the brand →

Prices and conditions are as listed by each merchant and may change. If you complete a purchase or form, we may earn a small commission at no extra cost to you.

Following an offer here banks 10 points on this device — once per page, within the 500 points a day anything on the site can earn.

Recommended For You

Explore curated deals from top brands across every category.