Building an Effective Data Science Practice : A Framework to Bootstrap and Manage a Successful Data Science Practice / by Vineet Raina, Srinath Krishnamurthy.

Format:

Book

Author/Creator:

Raina., author.

Krishnamurthy, Srinath,., author.

Contributor:

Vineet.

Language:

English

Subjects (All):

Artificial intelligence--Data processing.

Artificial intelligence.

Computer science.

Data Science.

Computer Science.

Local Subjects:

Data Science.

Computer Science.

Physical Description:

1 online resource (376 pages)

Edition:

1st ed. 2022.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2022.

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. You will: 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 .

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.

Notes:

Print version record.

ISBN:

9781523150953

1523150955

9781484274194

1484274199

OCLC:

1288273234