ESCP Europe

"'Data Science for Business' introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today, providing examples of real-world business problems to illustrate these principles. Youll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your companys data science projects. Youll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making." - from publisher.

Chapter 1: Introduction: data-analytic thinking --- Chapter 2: Business problems and data science solutions --- Chapter 3: Introduction to predictive modeling: from correlation to supervised segmentation --- Chapter 4: Fitting a model to data --- Chapter 5: Overfitting and its avoidance --- Chapter 6: Similarity, neighbors, and clusters --- Chapter 7: Decision analytic thinking i: what is a good model? --- Chapter 8: Visualizing model performance --- Chapter 9: Evidence and probabilities --- Chapter 10: Representing and mining text --- Chapter 11: Decision analytic thinking II: toward analytical engineering --- Chapter 12: Other data science tasks and techniques --- Chapter 12: Data science and business strategy --- Chapter 14. Conclusion --- Appendix: Proposal review guide --- Appendix: Another sample proposal..