Table of Contents

  1. Series page
  2. Title page
  3. Copyright page
  4. Series Foreword
  5. Preface
  6. Acknowledgments
  7. 1 What Is Data Science?
  8. 2 What Are Data, and What Is a Data Set?
  9. 3 A Data Science Ecosystem
  10. 4 Machine Learning 101
  11. 5 Standard Data Science Tasks
  12. 6 Privacy and Ethics
  13. 7 Future Trends and Principles of Success
  14. Glossary
  15. Further Readings
  16. References
  17. Index
  18. About Author

List of Tables

  1. Table 1 A Data Set of Classic Books
  2. Table 2 Diabetes Study Data Set
  3. Table 3 A Data Set of Emails: Spam or Not Spam?

List of Illustrations

  1. Figure 1 A skills-set desideratum for a data scientist.
  2. Figure 2 The DIKW pyramid (adapted from Kitchin 2014a).
  3. Figure 3 Data science pyramid (adapted from Han, Kamber, and Pei 2011).
  4. Figure 4 The CRISP-DM life cycle (based on figure 2 in Chapman, Clinton, Kerber, et al. 1999).
  5. Figure 5 The CRISP-DM stages and tasks (based on figure 3 in Chapman, Clinton, Kerber, et al. 1999).
  6. Figure 6 A typical small-data and big-data architecture for data science (inspired by a figure from the Hortonworks newsletter, April 23, 2013, https://hortonworks.com/blog/hadoop-and-the-data-warehouse-when-to-use-which).
  7. Figure 7 The traditional process for building predictive models and scoring data.
  8. Figure 8 Databases, data warehousing, and Hadoop working together (inspired by a figure in the Gluent data platform white paper, 2017, https://gluent.com/wp-content/uploads/2017/09/Gluent-Overview.pdf).
  9. Figure 9 Scatterplots of shoe size and height, weight and exercise, and shoe size and exercise.
  10. Figure 10 Scatterplots of the likelihood of diabetes with respect to height, weight, and BMI.
  11. Figure 11 (a) The best-fit regression line for the model “Diabetes = −7.38431 + 0.55593 BMI.” (b) The dashed vertical lines illustrate the residual for each instance.
  12. Figure 12 Mapping the logistic and tanh functions as applied to the input x.
  13. Figure 13 A simple neural network.
  14. Figure 14 A neural network that predicts a person’s fitness level.
  15. Figure 15 A deep neural network.
  16. Figure 16 A decision tree for determining whether an email is spam or not.
  17. Figure 17 Creating the root node in the tree.
  18. Figure 18 Adding the second node to the tree.

Guide

  1. Cover
  2. Table of Contents