Managing Data Science: Effective strategies to manage data science projects efficiently and build a sustainable team

Managing Data Science: Effective strategies to manage data science projects efficiently and build a sustainable teamReviews
Author: Kirill Dubovikov
Pub Date: 2020
ISBN: 978-1838826321
Pages: 233
Language: English
Format: PDF/EPUB
Size: 52 Mb

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Understand the concepts and methodologies to manage and deliver top-notch data science solutions for your organization
Data Science and Machine Learning can transform any organization and open new opportunities. A substantial managerial effort is needed to guide the solution from prototype development to production. Traditional approaches often fail as they have different conditions and requirements in mind. This book presents an approach to a data science project management, with tips and best practices to guide you along the way.
With the help of this book, you will understand the practical applications of data science and AI to incorporate them into your solutions. You will go through the data science project life-cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a balanced skillful team, and this book will present advice for hiring and growing a data science team for your organization. The book also shows you how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps.
By the end of the book, the readers will have the practical knowledge to tackle various challenges they deal on a daily basis and will have an understanding of various data science solutions.
What you will learn

  • Understand the underlying problems of building a strong data science pipeline
  • Learn the different tools to build and deploy data science solutions
  • Hire, grow, and sustain an efficient data science team
  • Manage data science projects through all stages from prototype to production
  • Learn how to use ModelOps for improving data science projects
  • Master the model testing techniques used in both development and production stages