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商品編號:
DMC0446
商品名稱:
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)
語系版本:
中文發音繁體中文版
運行平台:
官方原版畫質MP4檔,沒有任何平台限制,終身使用
官方網站:
https://xyz88.app
更新日期:
2025-05-01
碟片數量:
1片
銷售價格:
100
瀏覽次數:
24

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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

Learn how to use data to create powerful business forecasts
Organisations currently collect a vast quantity of data about suppliers, clients, employees, citizens, transactions, and much more. However, many are unaware of the predictive power this ‘big data’ has if anaylsed correctly.

On this course, you’ll learn about forecasting using big data, exploring how it’s used by business as an important component of decision making.

You’ll examine how to define a forecasting task and workflow. You’ll understand how to evaluate forecasting performance, analysing different forecasting methods. Ultimately, you’ll be able to implement your own practical forecasting process.

【先備能力】
You will need to be familiar with basic statistical methods, including linear regression, as well as have basic knowledge of Excel and R software.

【學習目標】
By the end of the course, you‘ll be able to...
Describe business challenges and opportunities that call for forecasting
Evaluate performance of a forecasting solution
Apply and be familiar with popular forecasting methods
Explore, identify and model different types of patterns in time series
Develop a forecasting solution using forecasting methods
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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