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
Section 1: What is Data Science?
Section 2: Building and Sustaining a Team
Section 3: Managing Various Data Science Projects
Section 4: Creating a Development Infrastructure

Balancing sales, marketing, team leadership, and technology

To thrive in data science management, you need to find a balance between different specialties. The data science manager switches between the tasks of sales, marketing, and optimization every day. But aren't they supposed to care about data science the most? Since we do our jobs collectively, we tend to communicate a lot. Ask any technical expert working in a business environment about how much time they spend doing actual work. On average, a software engineer will tell you that they spend 2 to 3 hours coding. During the other 6 hours, they attend meetings, write or read documentation, create tickets, and discuss technical designs. Data scientists spend a lot of time talking about data definitions, metric choices, and the business impact of the model they are building.

The number of areas a data scientist can spend...