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

Defining data science team roles

Data science teams need to deliver complex projects where system analysis, software engineering, data engineering, and data science are used to deliver the final solution. In this section, we will explore the main data science project roles. The project role depicts a set of related activities that can be performed by an expert. Role-expert is not strictly a one-to-one correspondence, as many experts have the expertise to handle multiple roles at once.

An average data science team will include a business analyst, a system analyst, a data scientist, a data engineer, and a data science team manager. More complex projects may also benefit from the participation of a software architect and backend/frontend development teams.

Here are the core responsibilities of each team role:

  • Project stakeholders: Represent people who are interested in the project...