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

Discovering the goals of the estimation process

It is important to keep the end goal in mind while making estimations. You can build a data science system without making a grand plan. Creating estimates and keeping them up to date requires a lot of effort and time. Data science projects are complex and unpredictable, so the more you and your customers believe in your estimates, the more likely they're going to fail. Estimates become more uncertain if your team has no prior experience in building solutions for a new business domain or if you are trying to apply new types of algorithms or use new technologies.

Having a fine-grained view of how to achieve the end goal is useful. In contrast, relying on the exact calculations of how long it will take you, or using extremely detailed outlines, is not. Use estimates wisely; they will help you align your implementation plans with...