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Handbook for Data Scientists.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Somerset :
John Wiley & Sons, Incorporated,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright; Dedication; Contents; Preface; Chapter 1 Introduction: Becoming a Unicorn; 1.1 Aren't Data Scientists Just Overpaid Statisticians?; 1.2 How Is This Book Organized?; 1.3 How toÜse This Book?; 1.4 Why Is It All inÈPython"! Anyway?; 1.5 Example Code andÈDatasets; 1.6 Parting Words; Part 1 The Stuff You'll Always Use; Chapter 2 The Data Science Road Map; 2.1 Frame theÈProblem; 2.2 Understand theÈData: Basic Questions; 2.3 Understand theÈData: Data Wrangling; 2.4 Understand theÈData: Exploratory Analysis; 2.5 Extract Features; 2.6 Model; 2.7 Present Results.
- 2.8 Deploy Code2.9 Iterating; 2.10 Glossary; Chapter 3 Programming Languages; 3.1 Why Use aÈProgramming Language? WhatÄreẗheÖther Options?; 3.2 A Survey ofÈProgramming Languages forÈDataÈScience; 3.3 Python Crash Course; 3.4 Strings; 3.5 Defining Functions; 3.6 Python's Technical Libraries; 3.7 Other Python Resources; 3.8 Further Reading; 3.9 Glossary; Interlude: My Personal Toolkit; Chapter 4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning; 4.1 The Worst Dataset inẗheẄorld; 4.2 How toÏdentify Pathologies; 4.3 Problems withÈData Content; 4.4 Formatting Issues.
- 4.5 Example Formatting Script4.6 Regular Expressions; 4.7 Life inẗheÈTrenches; 4.8 Glossary; Chapter 5 Visualizations andÈSimple Metrics; 5.1 A Note onÈPython's Visualization Tools; 5.2 Example Code; 5.3 Pie Charts; 5.4 Bar Charts; 5.5 Histograms; 5.6 Means, Standard Deviations, Medians, andÈQuantiles; 5.7 Boxplots; 5.8 Scatterplots; 5.9 Scatterplots withÈLogarithmic Axes; 5.10 Scatter Matrices; 5.11 Heatmaps; 5.12 Correlations; 5.13 Anscombe's Quartet andẗheÈLimits ofÈNumbers; 5.14 Time Series; 5.15 Further Reading; 5.16 Glossary; Chapter 6 Machine Learning Overview; 6.1 Historical Context.
- 6.2 Supervised versus Unsupervised6.3 Training Data, Testing Data, andẗheÈGreat Boogeyman ofÖverfitting; 6.4 Further Reading; 6.5 Glossary; Chapter 7 Interlude: Feature Extraction Ideas; 7.1 Standard Features; 7.2 Features That Involve Grouping; 7.3 Preview ofÈMore Sophisticated Features; 7.4 Defining theÈFeature YouẄant toÈPredict; Chapter 8 Machine Learning Classification; 8.1 What Is aÈClassifier, andẄhat Can YouÈDo withÏt?; 8.2 A Few Practical Concerns; 8.3 Binary versus Multiclass; 8.4 Example Script; 8.5 Specific Classifiers; 8.6 Evaluating Classifiers.
- 8.7 Selecting Classification Cutoffs8.8 Further Reading; 8.9 Glossary; Chapter 9 Technical Communication andÈDocumentation; 9.1 Several Guiding Principles; 9.2 Slide Decks; 9.3 Written Reports; 9.4 Speaking: What Has Worked forÈMe; 9.5 Code Documentation; 9.6 Further Reading; 9.7 Glossary; Part II Stuff You Still Need to Know; Chapter 10 Unsupervised Learning: Clustering andÈDimensionality Reduction; 10.1 The Curse ofÈDimensionality; 10.2 Example: Eigenfaces forÈDimensionality Reduction; 10.3 Principal Component Analysis andÈFactor Analysis; 10.4 Skree Plots andÜnderstanding Dimensionality.