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

Approaching research projects

A research project is any project that gives you solutions to new, not well-known problems. Research projects aren't always about advancing science. If your team deals with a new kind of business domain, or a new type of machine learning library, these are also considered to be research projects. Discovering ways to apply data science to new business domains is also research. Almost every data science project includes a research subproject that takes care of the modeling process.

The first pitfall of research projects is the absence of scope. Every research project must have a clear scope, otherwise it won't be possible to finish it. It is also important to fix any external constraints for a research project. Your research budgets will grow as the scope's size increases, so limited budgets may also affect the depth and length of research...