edu磨課師+ 應用預測方法於商業分析 影音教學 中文發音 繁體中文版(DVD版)
edu磨課師+應用預測方法於商業分析影音教學中文發音繁體中文版(DVD版)內容說明:
【課程簡介】
開課學校/機構:國立清華大學
課程發展年度:2015
課程類別:商學管理_市場調查

Learnhowtousedatatocreatepowerfulbusinessforecasts
Organisationscurrentlycollectavastquantityofdataaboutsuppliers,clients,employees,citizens,transactions,andmuchmore.However,manyareunawareofthepredictivepowerthis‘bigdata’hasifanaylsedcorrectly.

Onthiscourse,you’lllearnaboutforecastingusingbigdata,exploringhowit’susedbybusinessasanimportantcomponentofdecisionmaking.

You’llexaminehowtodefineaforecastingtaskandworkflow.You’llunderstandhowtoevaluateforecastingperformance,analysingdifferentforecastingmethods.Ultimately,you’llbeabletoimplementyourownpracticalforecastingprocess.

【先備能力】
Youwillneedtobefamiliarwithbasicstatisticalmethods,includinglinearregression,aswellashavebasicknowledgeofExcelandRsoftware.

【學習目標】
Bytheendofthecourse,you‘llbeableto...
Describebusinesschallengesandopportunitiesthatcallforforecasting
Evaluateperformanceofaforecastingsolution
Applyandbefamiliarwithpopularforecastingmethods
Explore,identifyandmodeldifferenttypesofpatternsintimeseries
Developaforecastingsolutionusingforecastingmethods
課程內容:
001_1-1-Bike-sharing---Interview.mp4
002_1-2-University-operations---Interview.mp4
003_1-3-Library-usage---Interview.mp4
004_1-4-Load-forecasting---Interview.mp4
005_1-5-Forecasting-language-and-notation.mp4
006_2-1-Visualization-screencast.mp4
007_2-2-Forecasting-past-+-future.mp4
008_3-1-Data-partitioning.mp4
009_3-2-Software-for-partitioning.mp4
010_3-3-Naive-forecasts.mp4
011_3-4-Performance-metrics-+-charts.mp4
012_3-5-Load-forecasting---Interview.mp4
013_4-1-MA-for-visualization.mp4
014_4-2-MA-for-forecasting.mp4
015_4-3-Differencing.mp4
016_4-4-Simple-exponential-smoothing.mp4
017_4-5-Holt's-exponential-smoothing.mp4
018_4-6-Holt-Winter's-exponential-smoothing.mp4
019_4-7-Prediction-intervals-and-automation.mp4
020_5-1-Regression-models-for-forecasting.mp4
021_5-2-Regression-linear-trend-models.mp4
022_5-3-Regression_-Other-trend-models.mp4
023_5-4-Regression-models-for-capturing-seasonality.mp4
024_6-1-Autocorrelation.mp4
025_6-2-Autocorrelation.mp4
026_6-3-Autoregressive-Integrated-Moving-Average-(ARIMA)-Models.mp4
027_6-4-Interview-with-Professor-Rob-Hyndman.mp4
028_6-5-Including-external-information.mp4
029_6-6-Including-external-information_-Correlated-series.mp4
030_7-1-Binary-Forecasts-(Part-A).mp4
031_7-2-Binary-Forecasts_-Part-B.mp4
032_7-3-Forecasting-with-Logistic-Regression-(Part-A).mp4
033_7-4-Forecasting-with-Logistic-Regression-(Part-B).mp4
034_7-5-Forecasting-with-Neural-Networks_-Part-A.mp4
035_7-6-Forecasting-with-Neural-Networks_-Part-B.mp4
036_7-7-Forecasting-with-Neural-Networks_-Part-C.mp4
037_7-8-Communication-and-Maintenance.mp4
038_7-9-Prof-Shmueli's-Flipped-Classroom.mp4
039_7-10-Conclusion.mp4
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