Book Image

Managing Data Science

By : Kirill Dubovikov
Book Image

Managing Data Science

By: Kirill Dubovikov

Overview of this book

Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, 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 skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: What is Data Science?
5
Section 2: Building and Sustaining a Team
9
Section 3: Managing Various Data Science Projects
14
Section 4: Creating a Development Infrastructure

Section 2: Building and Sustaining a Team

Data science is an innovation for most organizations. However, every innovation requires deep and careful thinking – not all ideas are equally good, and not all of them have the necessary resources for implementation. This chapter helps you to identify the best ideas and strip them down to the minimum valuable product.

Another important consideration is how successfully you can sell your idea to all stakeholders who might benefit from it.

Merging a knowledge of modern data analysis algorithms and business domain expertise is a necessary step for every project. This chapter outlines the importance of using a scientific approach in business. It helps you to answer the following questions: how can you find an efficient data science application for your business? What are business metrics and technical metrics and how should we define...