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

Thinking of projects as products

To benefit from the ideas in this chapter, you must think about your work as product development. While many companies deliver software products to the market, you deliver services. We can perceive services as a product; they also obey the laws of supply and demand, and there are markets for different types of services.

Like software products, you can decompose your activity into service features. Some of the aspects of your service that your team is good at will separate your department or company from your competitors, but you will likely find some of these aspects lagging behind other organizations that focus on this particular type of service. For example, a data science consulting company may shine in creating custom models. However, their user interfaces (UIs) will be worse than that of a specialized UI development company. These tradeoffs...