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Data science and analytics with Python / Jesús Rogel-Salazar

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Chapman & Hall/CRC data mining and knowledge discovery series | A Chapman & Hall bookPublisher: Boca Raton ; London ; New York : CRC Press, [2017]Distributor: [Ipswich, Massachusetts] : EBSCO IndustriesCopyright date: © 2017Description: 1 Online-Ressource (xxxv, 376 Seiten) : IllustrationenISBN:
  • 9781351647717
Subject(s): Additional physical formats: 9781138043176. | 9781498742092. | 1138043176 | 1315151677. | 9781498742115 | 1498742092 | 1498742114 | 9781315151670. | Erscheint auch als: Data science and analytics with python. Druck-Ausgabe Boca Raton : CRC Press, Taylor & Francis Group, a Chapman & Hall book, 2017. xxxv, 376 Seiten | Erscheint auch als: Data science and analytics with Python. Online-Ausgabe (Taylor&Francis) Boca Raton : CRC Press, 2017. 1 Online-Ressource (xxxv, 376 Seiten) | Print version: Data science and analytics with Python. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2017]DDC classification:
  • 006.312
  • 006.3/12 23
Local classification: Lokale Notation: inf 3.25LOC classification:
  • QA76.9.D343
Online resources: Summary: 1.4.5 Representation and Interaction1.4.6 Data Science: an Iterative Process; 1.5 Summary; 2: Python: For Something Completely Different; 2.1 Why Python? Why not?!; 2.1.1 To Shell or not To Shell; 2.1.2 iPython/Jupyter Notebook; 2.2 Firsts Slithers with Python; 2.2.1 Basic Types; 2.2.2 Numbers; 2.2.3 Strings; 2.2.4 Complex Numbers; 2.2.5 Lists; 2.2.6 Tuples; 2.2.7 Dictionaries; 2.3 Control Flow; 2.3.1 if... elif... else; 2.3.2 while; 2.3.3 for; 2.3.4 try... except; 2.3.5 Functions; 2.3.6 Scripts and Modules; 2.4 Computation and Data Manipulation; 2.4.1 Matrix Manipulations and Linear AlgebraSummary: 2.4.2 NumPy Arrays and Matrices2.4.3 Indexing and Slicing; 2.5 Pandas to the Rescue; 2.6 Plotting and Visualising: Matplotlib; 2.7 Summary; 3: The Machine that Goes â#x80;#x9C;Pingâ#x80;#x9D;: Machine Learning and Pattern Recognition; 3.1 Recognising Patterns; 3.2 Artificial Intelligence and Machine Learning; 3.3 Data is Good, but other Things are also Needed; 3.4 Learning, Predicting and Classifying; 3.5 Machine Learning and Data Science; 3.6 Feature Selection; 3.7 Bias, Variance and Regularisation: A Balancing Act; 3.8 Some Useful Measures: Distance and Similarity; 3.9 Beware the Curse of DimensionalitySummary: 3.10 Scikit-Learn is our Friend3.11 Training and Testing; 3.12 Cross-Validation; 3.12.1 k-fold Cross-Validation; 3.13 Summary; 4: The Relationship Conundrum: Regression; 4.1 Relationships between Variables: Regression; 4.2 Multivariate Linear Regression; 4.3 Ordinary Least Squares; 4.3.1 The Maths Way; 4.4 Brain and Body: Regression with One Variable; 4.4.1 Regression with Scikit-learn; 4.5 Logarithmic Transformation; 4.6 Making the Task Easier: Standardisation and Scaling; 4.6.1 Normalisation or Unit Scaling; 4.6.2 z-Score Scaling; 4.7 Polynomial Regression; 4.7.1 Multivariate RegressionSummary: 4.8 Variance-Bias Trade-Off4.9 Shrinkage: LASSO and Ridge; 4.10 Summary; 5: Jackalopes and Hares: Clustering; 5.1 Clustering; 5.2 Clustering with k-means; 5.2.1 Cluster Validation; 5.2.2 k-means in Action; 5.3 Summary; 6: Unicorns and Horses: Classification; 6.1 Classification; 6.1.1 Confusion Matrices; 6.1.2 ROC and AUC; 6.2 Classification with KNN; 6.2.1 KNN in Action; 6.3 Classification with Logistic Regression; 6.3.1 Logistic Regression Interpretation; 6.3.2 Logistic Regression in Action; 6.4 Classification with NaÃv̄e Bayes; 6.4.1 NaÃv̄e Bayes Classifier; 6.4.2 NaÃv̄e Bayes in ActionSummary: Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; List of Figures; List of Tables; Preface; Readerâ#x80;#x99;s Guide; About the Author; 1: Trials and Tribulations of a Data Scientist; 1.1 Data? Science? Data Science!; 1.1.1 So, What Is Data Science?; 1.2 The Data Scientist: A Modern Jackalope; 1.2.1 Characteristics of a Data Scientist and a Data Science Team; 1.3 Data Science Tools; 1.3.1 Open Source Tools; 1.4 From Data to Insight: the Data Science Workflow; 1.4.1 Identify the Question; 1.4.2 Acquire Data; 1.4.3 Data Munging; 1.4.4 Modelling and EvaluationPPN: PPN: 1019057548Package identifier: Produktsigel: ZDB-4-NLEBK
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