Impute Data to Forecast Demand in Google Sheets

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在此免费的指导 项目中,您将:

Understand why and how imputing missing values supports an accurate analysis.

Replace missing data with three simple imputation methods in Google Sheets.

Understand uses for moving averages techniques, how to evaluate effectiveness of imputation methods, and how to conduct a demand forecast.

在面试中展现此实践经验

Clock2 hours
Beginner面向初学者
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

This course will introduce you to cleaning data and replacing missing values with imputed data to support demand forecasting. Demand forecasts are used to maximize revenue, build efficiencies in operational planning, and to drive future growth. Forecasting techniques can be applied to make realistic predictions of outcomes of everything from how demand affects pricing and sales opportunities to operational planning for electrical utilities and healthcare facilities. We can only have confidence in the demand predictions we produce, when we also have confidence in the data quality feeding those predictions. Ensuring that confidence requires using clean data with no missing values for our forecast models. Handling missing data is an essential part of prepping clean data for a demand forecast. In this course, we will review the principles of applying central measures of tendency and regression techniques to impute missing values. As you clean the data, you will visualize it with charts, replace inconsistent values and impute values while comparing the outcomes of the statistical techniques you have applied. When your data is clean, you will create a demand forecast. You will do this as we work side-by-side in the free-to-use software Google Sheets. By the end of this course, you will understand use cases for imputing missing values and be able to confidently apply multiple statistical imputation techniques in any spreadsheet software. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

必备条件

Some familiarity with spreadsheet software is helpful, but not required.

您要培养的技能

Machine LearningForecasting DemandFeature EngineeringData AnalysisBusiness Intelligence

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Access Google Sheets.

  2. Import data into Google Sheets.

  3. Impute data with three simple imputation methods in Google Sheets.

  4. Impute data with linear and exponential regression, and harmonic means.

  5. Impute data with moving averages techniques, evaluate the results of all imputation methods, and conduct a demand forecast.

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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