Data Analysis Made Easy

✍ Scribed by Carvalho, André; Horvath, Tomás; Moreira, João

• Publisher: John Wiley & Sons, Incorporated
• Year: 2018
• Tongue: English
• Leaves: 392
• Category: Library

✦ Synopsis

Intro; Table of Contents; Preface; Part I: Introductory Background; 1 What Can We Do With Data?; 1.1 Big Data and Data Science; 1.2 Big Data Architectures; 1.3 Small Data; 1.4 What is Data?; 1.5 A Short Taxonomy of Data Analytics; 1.6 Examples of Data Use; 1.7 A Project on Data Analytics; 1.8 How this Book is Organized; 1.9 Who Should Read this Book; Part II: Getting Insights from Data; 2 Descriptive Statistics; 2.1 Scale Types; 2.2 Descriptive Univariate Analysis; 2.3 Descriptive Bivariate Analysis; 2.4 Final Remarks; 2.5 Exercises; 3 Descriptive Multivariate Analysis.

✦ Table of Contents

Table of Contents......Page 2
Preface......Page 19
Part I: Introductory Background......Page 24
1 What Can We Do With Data?......Page 25
1.1 Big Data and Data Science......Page 26
1.2 Big Data Architectures......Page 27
1.4 What is Data?......Page 28
1.5 A Short Taxonomy of Data Analytics......Page 30
1.6 Examples of Data Use......Page 33
1.7 A Project on Data Analytics......Page 34
1.8 How this Book is Organized......Page 40
1.9 Who Should Read this Book......Page 41
Part II: Getting Insights from Data......Page 42
2 Descriptive Statistics......Page 43
2.1 Scale Types......Page 44
2.2 Descriptive Univariate Analysis......Page 47
2.3 Descriptive Bivariate Analysis......Page 66
2.4 Final Remarks......Page 75
2.5 Exercises......Page 76
3 Descriptive Multivariate Analysis......Page 78
3.1 Multivariate Frequencies......Page 79
3.2 Multivariate Data Visualization......Page 80
3.3 Multivariate Statistics......Page 93
3.4 Infographics and Word Clouds......Page 104
3.5 Final Remarks......Page 106
3.6 Exercises......Page 107
4.1 Data Quality......Page 108
4.2 Converting to a Different Scale Type......Page 115
4.3 Converting to a Different Scale......Page 120
4.4 Data Transformation......Page 122
4.5 Dimensionality Reduction......Page 126
4.7 Exercises......Page 135
5 Clustering......Page 137
5.1 Distance Measures......Page 139
5.2 Clustering Validation......Page 146
5.3 Clustering Techniques......Page 148
5.4 Final Remarks......Page 164
5.5 Exercises......Page 165
6 Frequent Pattern Mining......Page 168
6.1 Frequent Itemsets......Page 170
6.2 Association Rules......Page 184
6.3 Behind Support and Confidence......Page 189
6.4 Other Types of Pattern......Page 194
6.6 Exercises......Page 197
7.1 Cheat Sheet of Descriptive Analytics......Page 199
7.2 Project on Descriptive Analytics......Page 202
Part III: Predicting the Unknown......Page 208
8 Regression......Page 209
8.1 Predictive Performance Estimation......Page 212
8.2 Finding the Parameters of the Model......Page 220
8.3 Technique and Model Selection......Page 233
8.5 Exercises......Page 234
9.1 Binary Classification......Page 236
9.2 Predictive Performance Measures for Classification......Page 242
9.3 Distance‐based Learning Algorithms......Page 251
9.4 Probabilistic Classification Algorithms......Page 256
9.6 Exercises......Page 262
10.1 Search‐based Algorithms......Page 264
10.2 Optimization‐based Algorithms......Page 277
10.3 Final Remarks......Page 297
10.4 Exercises......Page 298
11.1 Ensemble Learning......Page 299
11.2 Algorithm Bias......Page 304
11.3 Non‐binary Classification Tasks......Page 306
11.4 Advanced Data Preparation Techniques for Prediction......Page 313
11.5 Description and Prediction with Supervised Interpretable Techniques......Page 315
11.6 Exercises......Page 316
12.1 Cheat Sheet on Predictive Analytics......Page 318
12.2 Project on Predictive Analytics......Page 319
Part IV: Popular Data Analytics Applications......Page 327
13.1 Working with Texts......Page 328
13.2 Recommender Systems......Page 337
13.3 Social Network Analysis......Page 350
13.4 Exercises......Page 361
A.1 Business Understanding......Page 363
A.2 Data Understanding......Page 364
A.3 Data Preparation......Page 365
A.4 Modeling......Page 366
A.6 Deployment......Page 368
References......Page 370
Index......Page 374
End User License Agreement......Page 391

✦ Subjects

Data mining;Electronic data processing;Mathematical statistics--Methodology;Electronic books;Mathematical statistics -- Methodology