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# System prepended metadata

title: '大數據的分析與應用 老師:吳帆 教授'
tags: [選修]

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###### tags: `選修`


# 大數據的分析與應用 老師:吳帆 教授


:::spoiler 考試必考

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:::spoiler 作業



1. 分析 3C
2. 上台5分

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:::spoiler 基礎知識
[數據分析入門：了解分析的本質與基本思維能力](https://procrustes.info/analysis-basic/)
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## 章節

### 2022/02/19 Chap1-大數據分析與應用序論

### 2022/03/05 Chap2-Database vs. Datawarehouse

### 2022/03/19 Chap3 

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:::spoiler 2022/02/19 Chap1-大數據分析與應用序論

#### Mental models:如何看世界(同樣的資料，會有不同的解讀)

* Your statistical model depends on your mental model.

![](https://i.imgur.com/iAtLJHA.png)

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#### data analysis activities

1. Define：定義問題
2. Disassemble：拆解
3. Evaluate：評價
4. Decide：決定

![](https://i.imgur.com/q3wKGD7.png)

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#### Furthermore Row Data

![](https://i.imgur.com/FqTpySx.png)

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:::spoiler 2022/03/05 Chap2 Database vs. Datawarehouse

### Data Warehouses

![](https://i.imgur.com/8e3e9mi.png)

* Time-variant:

    * Data stored to provide information from a historical perspective

* Nonvolatile

    * requiring two operations: initial loading of data and access of data

### Dataware Architecture

![](https://i.imgur.com/hMIDBcw.png)

### The lattice of a 4-dimensional cube. 
 
![](https://i.imgur.com/sFY74zf.png)

[數據立方體](https://www.twblogs.net/a/5c8f4f70bd9eee35fc154770)

### Methods of data reduction

* Strategies of data reductions

    * data aggregation (e.g., sum, mean)

    * attribute subset selection (e.g., removing irrelevant attributes)
      
    * dimensionality reduction (e.g., using encoding schemes)
    
    * numerosity reduction (e.g., “replacing” the data by smaller representations such as clusters or parametric models)


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:::spoiler 2022/03/19 Chap3 

## Data reduction：attribute ssubset sselection

![](https://i.imgur.com/4nu0eWg.png)

## 決策樹

![](https://i.imgur.com/NLqiagn.png)

![](https://i.imgur.com/AT5icbl.png)

![](https://i.imgur.com/86wi4id.png)

![](https://i.imgur.com/gyePXxz.png)

![](https://i.imgur.com/HWdd8BO.png)



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