| Edition |
1st ed. |
| Description |
xxi, 386 pages : illustrations ; 24 cm |
| Note |
Subtitle from cover. |
| Bibliography |
Includes bibliographical references (pages 361-368) and index. |
| Contents |
Introduction : data-analytic thinking -- Business problems and data science solutions -- Introduction to predictive modeling : from correlation to supervised segmentation -- Fitting a model to data -- Overfitting and its avoidance -- Similarity, neighbors, and clusters -- Decision analytic thinking I : what is a good model? -- Visualizing model performance -- Evidence and probabilities -- Representing and mining text -- Decision analytic thinking II : toward analytical engineering -- Other data science tasks and techniques -- Data science and business strategy -- Conclusion. |
| Summary |
Provides an introduction to the fundamental principles of data science, walking the reader through the "data-analytic thinking" necessary for extracting useful knowledge and business value from collected data. |
| Subject |
Data mining.
|
|
Data mining. |
|
Big data.
|
|
Big data. |
|
Information science.
|
|
Information science. |
|
Business -- Data processing.
|
|
Business -- Data processing. |
|
Data Mining. |
|
Information Science. |
|
Commerce. |
|
Electronic Data Processing. |
| Added Author |
Fawcett, Tom.
|
| Cover Title |
What you need to know about data mining and data-analytic thinking |
| ISBN |
1449361323 paperback |
|
9781449361327 paperback |
|
