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Title
Building an effective data science practice: a framework to bootstrap and manage a successful data science practice / Vineet Raina, Srinath Krishnamurthy.
ISBN
9781484274194 (electronic bk.)
1484274199 (electronic bk.)
1484274180
9781484274187
Publication Details
[California] : Apress, 2022.
Language
English
Description
1 online resource
Item Number
10.1007/978-1-4842-7419-4 doi
Call Number
QA76.9.D343
Dewey Decimal Classification
006.3/12
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Available in Other Form
Print version: 9781484274187
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.
