| Description |
1 online resource |
| Summary |
Gain 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 Practice provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. Reinforced with real examples, this book allows you to confidently determine the strategic answers to effectively align your business goals with the operations of the data science practice. What You'll Learn Transform business objectives into concrete problems that can be solved using data science Evaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a project Build and operate an effective interdisciplinary data science team within an organization Evaluating the progress of the team towards the business RoI Understand the important regulatory aspects that are applicable to a data science practice Who This Book Is For Technology leaders, data scientists, and project managers. |
| Contents |
Part 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. |
| Bibliography |
Includes bibliographical references and index. |
| Subject |
Data mining.
|
|
Big data.
|
|
Machine learning.
|
|
Business -- Data processing.
|
|
Data Mining |
|
Machine Learning |
|
Exploration de données (Informatique) |
|
Données volumineuses. |
|
Apprentissage automatique. |
|
Gestion -- Informatique. |
|
Big data |
|
Business -- Data processing |
|
Data mining |
|
Machine learning |
| Added Author |
Krishnamurthy, Srinath, author.
|
| Other Form: |
Print version: 1484274180 9781484274187 (OCoLC)1268111688 |
| ISBN |
9781484274194 (electronic bk.) |
|
1484274199 (electronic bk.) |
| Standard No. |
10.1007/978-1-4842-7419-4 doi |
|
More Resources