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

Common Pitfalls of Data Science Projects

In this chapter, we will explore the common pitfalls of data science projects, as well as the mistakes that increase the risks your projects may encounter and that are easy to commit. It's important that you know how to deal with them for the success of your projects. Different types of data science solutions have many tempting ways of executing the project that can lead to undesired difficulties in the later stages of the project. We will pick and mitigate those issues one by one while following the data science project life cycle.

In this chapter, we will cover the following topics:

  • Avoiding the common risks of data science projects
  • Approaching research projects
  • Dealing with prototypes and minimum viable product (MVP) projects
  • Mitigating risks in production-oriented data science systems