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INTRODUCTION TO DATA SCIENCE A PYTHON APPROACH TO CONCEPTS, TECHNIQUES AND APPLICATIONS IGUAL, LAURA

By: Publisher: Cham : Springer, [2022]Copyright date: ©2022Description: 246 pages illustrations (chiefly color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031489556
Subject(s): Genre/Form: LOC classification:
  • Q180.55 .Q36I48 2024
Contents:
Intro -- Acknowledgement -- Contents -- About the Author -- Chapter 1: Introduction -- 1.1 Before Getting Started -- 1.1.1 Overview (of Old Ways to Analyse Data and Some Problems Related to Them) -- 1.1.2 Who Am I? -- 1.1.3 How Did I Get There? -- 1.1.4 Who Is This Book For? -- 1.1.5 Who Is This Book NOT For? -- 1.1.6 What's Special You Get in This Book? -- 1.1.7 So, What Does This Book Have? -- 1.1.8 How to Best Make Use of This Book? -- 1.2 Types of Research Studies -- 1.2.1 Explanatory Research -- 1.2.2 Predictive Research -- 1.2.3 Exploratory Research -- 1.3 Data.
1.3.1 To Collect or Not Collect Your Own Data -- 1.3.2 Where to Get the Data From? -- 1.3.3 Ways in Which Data Is Divided -- 1.3.4 Five Lessons -- 1.4 Statistics: A Refresher Before Getting into Machine Learning -- References -- Chapter 2: Python Programming -- 2.1 But Do I Have to Learn to Code for Data Analysis? -- 2.2 How to Install Python? -- 2.3 Variables -- 2.4 Operators -- 2.4.1 Arithmetic Operators -- 2.4.2 Comparison Operators -- 2.5 Statements -- 2.6 Loops -- 2.7 Data Structure -- 2.8 Methods and Functions (Built-Ins) in Python -- 2.8.1 Methods -- 2.8.2 Function -- 2.9 Error Resolution.
2.10 Last Words -- Chapter 3: Data Pre-processing -- 3.1 Introduction -- 3.2 Data Cleaning -- 3.2.1 Problem 1: Duplicate Columns and Categorical Variables -- 3.2.2 Problem 2: Outliers -- 3.2.3 Problem 3: Missing Values -- 3.3 Data Transformation -- 3.3.1 Converting Categorical Variables into Numeric Variables -- 3.3.2 Converting Continuous Variables into Categorical Variables -- 3.3.3 Feature Scaling -- 3.4 Data Reduction -- 3.4.1 Strategy 1 -- 3.4.2 Strategy 2 -- 3.4.3 Strategy 3 -- 3.4.4 Strategy 4 -- 3.4.5 Strategy 5 -- 3.5 Final Words -- References -- Chapter 4: Machine Learning.
4.1 Introduction -- 4.2 Classification -- 4.2.1 Getting Started with Supervised Machine Learning -- 4.2.2 Machine Learning (Classifier): The Leak-Proof Approach -- 4.2.3 Confidence Interval -- 4.2.4 Choosing the Best Model for Classification -- 4.2.5 Optimising the Predictive Accuracies of the Model with Hyperparameter Tuning -- 4.3 Regression -- 4.3.1 Regression Using Machine Learning and How to Interpret the Results -- 4.3.2 Feature Importance -- 4.3.3 Exploratory Research Using Unsupervised Machine Learning -- 4.4 Clustering -- 4.4.1 Hierarchical Clustering -- 4.4.2 K-Means Clustering.
4.5 Principal Component Analysis (PCA) -- 4.6 Rule Mining -- References -- Chapter 5: End Note -- Index.
Summary: This textbook presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or sentiment analysis. Topics and features: Provides numerous practical case studies using real-world data throughout the book Supports understanding through hands-on experience of solving data science problems using Python Describes concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data Provides supplementary code resources and data at an associated website This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses. Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Associate Professor at the same institution. The authors wish to mention that some chapters were co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera
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Includes bibliographical references and index.

Intro -- Acknowledgement -- Contents -- About the Author -- Chapter 1: Introduction -- 1.1 Before Getting Started -- 1.1.1 Overview (of Old Ways to Analyse Data and Some Problems Related to Them) -- 1.1.2 Who Am I? -- 1.1.3 How Did I Get There? -- 1.1.4 Who Is This Book For? -- 1.1.5 Who Is This Book NOT For? -- 1.1.6 What's Special You Get in This Book? -- 1.1.7 So, What Does This Book Have? -- 1.1.8 How to Best Make Use of This Book? -- 1.2 Types of Research Studies -- 1.2.1 Explanatory Research -- 1.2.2 Predictive Research -- 1.2.3 Exploratory Research -- 1.3 Data.

1.3.1 To Collect or Not Collect Your Own Data -- 1.3.2 Where to Get the Data From? -- 1.3.3 Ways in Which Data Is Divided -- 1.3.4 Five Lessons -- 1.4 Statistics: A Refresher Before Getting into Machine Learning -- References -- Chapter 2: Python Programming -- 2.1 But Do I Have to Learn to Code for Data Analysis? -- 2.2 How to Install Python? -- 2.3 Variables -- 2.4 Operators -- 2.4.1 Arithmetic Operators -- 2.4.2 Comparison Operators -- 2.5 Statements -- 2.6 Loops -- 2.7 Data Structure -- 2.8 Methods and Functions (Built-Ins) in Python -- 2.8.1 Methods -- 2.8.2 Function -- 2.9 Error Resolution.

2.10 Last Words -- Chapter 3: Data Pre-processing -- 3.1 Introduction -- 3.2 Data Cleaning -- 3.2.1 Problem 1: Duplicate Columns and Categorical Variables -- 3.2.2 Problem 2: Outliers -- 3.2.3 Problem 3: Missing Values -- 3.3 Data Transformation -- 3.3.1 Converting Categorical Variables into Numeric Variables -- 3.3.2 Converting Continuous Variables into Categorical Variables -- 3.3.3 Feature Scaling -- 3.4 Data Reduction -- 3.4.1 Strategy 1 -- 3.4.2 Strategy 2 -- 3.4.3 Strategy 3 -- 3.4.4 Strategy 4 -- 3.4.5 Strategy 5 -- 3.5 Final Words -- References -- Chapter 4: Machine Learning.

4.1 Introduction -- 4.2 Classification -- 4.2.1 Getting Started with Supervised Machine Learning -- 4.2.2 Machine Learning (Classifier): The Leak-Proof Approach -- 4.2.3 Confidence Interval -- 4.2.4 Choosing the Best Model for Classification -- 4.2.5 Optimising the Predictive Accuracies of the Model with Hyperparameter Tuning -- 4.3 Regression -- 4.3.1 Regression Using Machine Learning and How to Interpret the Results -- 4.3.2 Feature Importance -- 4.3.3 Exploratory Research Using Unsupervised Machine Learning -- 4.4 Clustering -- 4.4.1 Hierarchical Clustering -- 4.4.2 K-Means Clustering.

4.5 Principal Component Analysis (PCA) -- 4.6 Rule Mining -- References -- Chapter 5: End Note -- Index.

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This textbook presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or sentiment analysis. Topics and features: Provides numerous practical case studies using real-world data throughout the book Supports understanding through hands-on experience of solving data science problems using Python Describes concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data Provides supplementary code resources and data at an associated website This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses. Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Associate Professor at the same institution. The authors wish to mention that some chapters were co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera

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