Description
Book SynopsisGain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science classes of data science problems, data science techniques and their applications and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practiceprovides a common base of re
Table of ContentsPart One: Fundamentals
1. Introduction: The Data Science Process
2. Data Science and your business
3. Monks vs. Cowboys: Data Science Cultures
Part Two: Classes of Problems
4. Classification
5. Regression
6. Natural Language Processing
7. Clustering
8. Anomaly Detection
9. Recommendations
10. Computer Vision
11. Sequential Decision Making
Part Three: Techniques & Technologies
12. Overview
13. Data Capture
14. Data Preparation
15. Data Visualization
16. Machine Learning
17. Inference
18. Other tools and services
19. Reference Architecture
20. Monks vs. Cowboys: Praxis
Part Four: Building Teams and Executing Projects
21. The Skills Framework
22. Building and structuring the team
23. Data Science Projects
Appendix FAQs
