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Produktbild: Data Science Using Python and R

Data Science Using Python and R

Aus der Reihe Wiley Series on Methods and Applications in Data Mining

127,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

22.03.2019

Verlag

John Wiley & Sons Inc

Seitenzahl

256

Maße (L/B/H)

23,5/15,7/1,9 cm

Gewicht

531 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-52681-0

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

22.03.2019

Verlag

John Wiley & Sons Inc

Seitenzahl

256

Maße (L/B/H)

23,5/15,7/1,9 cm

Gewicht

531 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-52681-0

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Data Science Using Python and R
  • Preface xi
     
    About the Authors xv
     
    Acknowledgements xvii
     
    Chapter 1 Introduction to Data Science 1
     
    1.1 Why Data Science? 1
     
    1.2 What is Data Science? 1
     
    1.3 The Data Science Methodology 2
     
    1.4 Data Science Tasks 5
     
    1.4.1 Description 6
     
    1.4.2 Estimation 6
     
    1.4.3 Classification 6
     
    1.4.4 Clustering 7
     
    1.4.5 Prediction 7
     
    1.4.6 Association 7
     
    Exercises 8
     
    Chapter 2 The Basics of Python and R 9
     
    2.1 Downloading Python 9
     
    2.2 Basics of Coding in Python 9
     
    2.2.1 Using Comments in Python 9
     
    2.2.2 Executing Commands in Python 10
     
    2.2.3 Importing Packages in Python 11
     
    2.2.4 Getting Data into Python 12
     
    2.2.5 Saving Output in Python 13
     
    2.2.6 Accessing Records and Variables in Python 14
     
    2.2.7 Setting Up Graphics in Python 15
     
    2.3 Downloading R and RStudio 17
     
    2.4 Basics of Coding in R 19
     
    2.4.1 Using Comments in R 19
     
    2.4.2 Executing Commands in R 20
     
    2.4.3 Importing Packages in R 20
     
    2.4.4 Getting Data into R 21
     
    2.4.5 Saving Output in R 23
     
    2.4.6 Accessing Records and Variables in R 24
     
    References 26
     
    Exercises 26
     
    Chapter 3 Data Preparation 29
     
    3.1 The Bank Marketing Data Set 29
     
    3.2 The Problem Understanding Phase 29
     
    3.2.1 Clearly Enunciate the Project Objectives 29
     
    3.2.2 Translate These Objectives into a Data Science Problem 30
     
    3.3 Data Preparation Phase 31
     
    3.4 Adding an Index Field 31
     
    3.4.1 How to Add an Index Field Using Python 31
     
    3.4.2 How to Add an Index Field Using R 32
     
    3.5 Changing Misleading Field Values 33
     
    3.5.1 How to Change Misleading Field Values Using Python 34
     
    3.5.2 How to Change Misleading Field Values Using R 34
     
    3.6 Reexpression of Categorical Data as Numeric 36
     
    3.6.1 How to Reexpress Categorical Field Values Using Python 36
     
    3.6.2 How to Reexpress Categorical Field Values Using R 38
     
    3.7 Standardizing the Numeric Fields 39
     
    3.7.1 How to Standardize Numeric Fields Using Python 40
     
    3.7.2 How to Standardize Numeric Fields Using R 40
     
    3.8 Identifying Outliers 40
     
    3.8.1 How to Identify Outliers Using Python 41
     
    3.8.2 How to Identify Outliers Using R 42
     
    References 43
     
    Exercises 44
     
    Chapter 4 Exploratory Data Analysis 47
     
    4.1 EDA Versus HT 47
     
    4.2 Bar Graphs with Response Overlay 47
     
    4.2.1 How to Construct a Bar Graph with Overlay Using Python 49
     
    4.2.2 How to Construct a Bar Graph with Overlay Using R 50
     
    4.3 Contingency Tables 51
     
    4.3.1 How to Construct Contingency Tables Using Python 52
     
    4.3.2 How to Construct Contingency Tables Using R 53
     
    4.4 Histograms with Response Overlay 53
     
    4.4.1 How to Construct Histograms with Overlay Using Python 55
     
    4.4.2 How to Construct Histograms with Overlay Using R 58
     
    4.5 Binning Based on Predictive Value 58
     
    4.5.1 How to Perform Binning Based on Predictive Value Using Python 59
     
    4.5.2 How to Perform Binning Based on Predictive Value Using R 62
     
    References 63
     
    Exercises 63
     
    Chapter 5 Preparing to Model the Data 69
     
    5.1 The Story So Far 69
     
    5.2 Partitioning the Data 69
     
    5.2.1 How to Partition the Data in Python 70
     
    5.2.2 How to Partition the Data in R 71
     
    5.3 Validating your Partition 72
     
    5.4 Balancing the Training Data Set 73
    &nb