Art of Data Science (Nghệ thuật của Khoa học dữ liệu) - Roger D. Peng và Elizabeth Matsui
Sách hướng dẫn toàn diện về quy trình phân tích dữ liệu, bao gồm các bước từ đặt câu hỏi, khám phá dữ liệu, mô hình hóa đến diễn giải kết quả.
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The Art of Data Science A Guide for Anyone Who Works with Data Roger D. Peng and Elizabeth Matsui This book is for sale at http://leanpub.com/artofdatascience This version was published on 2016-07-20 This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do. © 2015 - 2016 Skybrude Consulting, LLC Also By Roger D. Peng R Programming for Data Science Exploratory Data Analysis with R Executive Data Science Report Writing for Data Science in R Conversations On Data Science Special thanks to Maggie Matsui, who created all of the artwork for this book. Contents 1. Data Analysis as Art . . . . . . . . . . . . . . . . . 1 2. Epicycles of Analysis . . . . . . . . . . . . . . . . . 2.1 Setting the Scene . . . . . . . . . . . . . . . . 2.2 Epicycle of Analysis . . . . . . . . . . . . . . 2.3 Setting Expectations . . . . . . . . . . . . . . 2.4 Collecting Information . . . . . . . . . . . . 2.5 Comparing Expectations to Data . . . . . . 2.6 Applying the Epicyle of Analysis Process . . 4 5 6 8 9 10 11 3. Stating and Refining the Question . . . . . . . . 3.1 Types of Questions . . . . . . . . . . . . . . . 3.2 Applying the Epicycle to Stating and Refining Your Question . . . . . . . . . . . . . . . 3.3 Characteristics of a Good Question . . . . . 3.4 Translating a Question into a Data Problem 3.5 Case Study . . . . . . . . . . . . . . . . . . . . 3.6 Concluding Thoughts . . . . . . . . . . . . . 16 16 4. 20 20 23 26 30 Exploratory Data Analysis . . . . . . . . . . . . . 31 4.1 Exploratory Data Analysis Checklist: A Case Study . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Formulate your question . . . . . . . . . . . 33 4.3 Read in your data . . . . . . . . . . . . . . . . 35 4.4 Check the Packaging . . . . . . . . . . . .
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- Dokumentenname
- Art of Data Science (Nghệ thuật của Khoa học dữ liệu) - Roger D. Peng và Elizabeth Matsui
- Inhalt
- Cuốn sách hướng dẫn quy trình khoa học dữ liệu, từ việc đặt câu hỏi, khám phá dữ liệu, sử dụng mô hình, đến suy luận và diễn giải kết quả, nhấn mạnh cách tiếp cận có hệ thống và lặp đi lặp lại.
- Inhaltsverzeichnis
- 1. Data Analysis as Art
- 2. Epicycles of Analysis
- 2.1 Setting the Scene
- 2.2 Epicycle of Analysis
- 2.3 Setting Expectations
- 2.4 Collecting Information
- 2.5 Comparing Expectations to Data
- 2.6 Applying the Epicyle of Analysis Process
- 3. Stating and Refining the Question
- 3.1 Types of Questions
- 3.2 Applying the Epicycle to Stating and Refining Your Question
- 3.3 Characteristics of a Good Question
- 3.4 Translating a Question into a Data Problem
- 3.5 Case Study
- 3.6 Concluding Thoughts
- 4. Exploratory Data Analysis
- 4.1 Exploratory Data Analysis Checklist: A Case Study
- 4.2 Formulate your question
- 4.3 Read in your data
- 4.4 Check the Packaging
- 4.5 Look at the Top and the Bottom of your Data
- 4.6 ABC: Always be Checking Your “n”s
- 4.7 Validate With at Least One External Data Source
- 4.8 Make a Plot
- 4.9 Try the Easy Solution First
- 4.10 Follow-up Questions
- 5. Using Models to Explore Your Data
- 5.1 Models as Expectations
- 5.2 Comparing Model Expectations to Reality
- 5.3 Reacting to Data: Refining Our Expectations
- 5.4 Examining Linear Relationships
- 5.5 When Do We Stop?
- 5.6 Summary
- 6. Inference: A Primer
- 6.1 Identify the population
- 6.2 Describe the sampling process
- 6.3 Describe a model for the population
- 6.4 A Quick Example
- 6.5 Factors Affecting the Quality of Inference
- 6.6 Example: Apple Music Usage
- 6.7 Populations Come in Many Forms
- 7. Formal Modeling
- 7.1 What Are the Goals of Formal Modeling?
- 7.2 General Framework
- 7.3 Associational Analyses
- 7.4 Prediction Analyses
- 7.5 Summary
- 8. Inference vs. Prediction: Implications for Modeling Strategy
- 8.1 Air Pollution and Mortality in New York City
- 8.2 Inferring an Association
- 8.3 Predicting the Outcome
- 8.4 Summary
- 9. Interpreting Your Results
- 9.1 Principles of Interpretation
- Seiten
- 162 Seiten
- Hochgeladen von
- Giang Le
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