Impute Data to Forecast Demand in Google Sheets

Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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.

Clock2 hours
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

Machine LearningForecasting DemandFeature EngineeringData AnalysisBusiness Intelligence

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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.

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.