Data science and analytics with Python

Data science and analytics with Python

by Rogel-Salazar, Jesús

Type Book
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Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized …

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Title Statements

  • Full title :Data science and analytics with Python / Jesus Rogel-Salazar.

Summary

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book.--

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Edition Statements

  • Publication Date:2017

Publication Statements

The Physical Object

  • Description:1 online resource (xxxv, 376 pages)

Edition Identifiers

  • ISBN:9781315151670, 1315151677, 9781498742115, 1498742114, 9781523120000, 1523120002
  • oclc:on1023861775
  • LC Classification:QA76.9.D343
  • Dewey Decimal:006.3/12

Table of Contents

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 Evaluation.

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 Algebra.

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Subjects

Computers, General Data Mining Databases Datenanalyse Exploration de données Exploration de données (Informatique) Python Python (Computer program language) Python (Langage de programmation)

Writers

Rogel-Salazar, Jesús

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