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

Managing Innovation

In the Building and Sustaining a Team section of this book, we looked at how we can create a balanced team that can deliver data science solutions. Now, we will look at how we can find projects and problems that have real value. In this section of the book, we will look at data science management at a greater scale. We are moving on from team leadership to the area of data science project management. We will develop specific strategies and approaches so that we can find, manage, and deliver projects that are valuable for the business.

For most companies, data science and machine learning belong in the area of innovation. Unlike software development, these disciplines are terra incognita (unknown territory) for your customers and business stakeholders. If you approach data science projects like any other project, you will face many unexpected problems. The domain...