Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring;
Prof. Dr. Michael R. Berthold is Professor for Bioinformatics and Information Mining in the Department of Computer Science at the University of Konstanz, Germany.Prof. Dr. Christian Borgelt is Professor for Data Science in the departments of Mathematics and Computer Sciences at the Paris Lodron University of Salzburg, Austria; he also co-authored the Springer textbook, Computational Intelligence.Prof. Dr. Frank Höppner is Professor of Information Engineering in the Department of Computer Science at Ostfalia University of Applied Sciences, Wolfenbüttel, Germany.Prof. Dr. Frank Klawonn is Professor for Data Analysis and Pattern Recognition at the same institution and head of the Biostatistics Group at the Helmholtz Centre for Infection Research, Braunschweig, Germany; he has authored the Springer textbook, Introduction to Computer Graphics.Dr. Rosaria Silipo is a Principal Data Scientist and Head of Evangelism at KNIME AG, Zurich, Switzerland.
Innehållsförteckning
Introduction.- Practical Data Analysis: An Example.- Project Understanding.- Data Understanding.- Principles of Modeling.- Data Preparation.- Finding Patterns.- Finding Explanations.- Finding Predictors.- Evaluation and Deployment.- The Labelling Problem.- Appendix A: Statistics.- Appendix B: KNIME.