A瞭解商業大數據分析生命週期(Understanding the Business Data Analytics Life Cycle)
B確定研究問題(Identify the Research Questions )
B-1定義商業問題(Defining the business problem(s))
B-2將商業問題表述為分析問題(Articulating the business problem as an analytical problem )
B-3定義成功 KPI(Defining success KPIs)
B-4構建假設,並界定研究問題(Building hypothesis, and framing the research question(s))
B-5型態 I 與類型 II 錯誤(Type I and Type II errors)
B-6使用 DMN(決策模型和符號)建構決策需求模型
(Using DMN (Decision Model and Notation) to build a Decision Requirements Model)
C數據來源(Source Data)
C-1數據類型(Types of data)
C-2定義數據要求(Defining data requirements)
C-3制定數據收集計劃(Developing a Data Collection Plan )
C-4識別數據來源(Identifying data sources)
C-5收集數據(Collecting data)
C-6瞭解數據建模(Understanding data modeling)
D分析數據(Analyze Data)
D-1機器學習基礎知識(Machine Learning Fundamentals)
D-2監督學習演演算法(Supervised Learning Algorithms)
D-3無監督學習演演算法(Unsupervised Learning Algorithms)
D-4"過度擬合"的概念(The Concept of “Over-fitting”)
D-5偏置誤差和方差誤差(Bias Error and Variance Error )
D-6解決"過度擬合"(Addressing “over-fitting”)
D-7數據準備:預處理數據(Data Preparation: Pre-processing data )
D-8格式化數據(Formatting data)
D-9清潔數據(Cleaning data)
D-10取樣數據(Sampling data)
D-11數據準備 : 轉換數據(Data Preparation: Transforming data (Feature Engineering) )
D-12測試與選擇演算法(Testing and selecting algorithms )
D-13建築模型(Building models)
D-14評估模型(Evaluating models )
|