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

Understanding data science project failure

Every data science project ends up being a software system that generates scheduled reports or operates online. The world of software engineering already provides us with a multitude of software project management methodologies, so why do we need to reinvent a special approach for data science projects? The answer is that data science projects require much more experimentation and have to tolerate far more failures than software engineering projects.

To see the difference between a traditional software system and a system with predictive algorithms, let's look at the common causes of failure for data science projects:

  • Dependence on data: A robust customer relationship management (CRM) system that organizes the sales process will work well in many organizations, independent of their business. A system that predicts the outcome of...