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|a (OCoLC)970636214
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|a QA76.9.D32.C33 2017
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|a 005.74
|2 23
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| 049 |
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|a UAMI
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| 100 |
1 |
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|a Cady, Field.
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| 245 |
1 |
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|a Handbook for Data Scientists.
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| 260 |
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|a Somerset :
|b John Wiley & Sons, Incorporated,
|c 2017.
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| 300 |
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|a 1 online resource (417 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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|a Print version record.
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|a 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.
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| 505 |
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|a 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.
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| 505 |
8 |
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|a 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.
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| 505 |
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|a 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.
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| 505 |
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|a 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.
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| 500 |
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|a 10.5 Factor Analysis.
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| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 650 |
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|a Databases
|v Handbooks, manuals, etc.
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| 650 |
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|a Handbooks and manuals
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| 758 |
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|i has work:
|a Handbook for Data Scientists (Text)
|1 https://id.oclc.org/worldcat/entity/E39PD3JWpGBMhHVgXYxH6CdHfq
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
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|i Print version:
|a Cady, Field.
|t Handbook for Data Scientists.
|d Somerset : John Wiley & Sons, Incorporated, Ã2017
|z 9781119092940
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4790656
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