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

Introduction to machine learning

Machine learning is by far the most important tool of a data scientist. It allows us to create algorithms that discover patterns in data with thousands of variables. We will now explore different types and capabilities of machine learning algorithms.

Machine learning is a scientific field that studies algorithms that can learn to perform tasks without specific instructions, relying on patterns discovered in data. For example, we can use algorithms to predict the likelihood of having a disease or assess the risk of failure in complex manufacturing equipment. Every machine learning algorithm follows a simple formula. In the following diagram, you can see a high-level decision process that is based on a machine learning algorithm. Each machine learning model consumes data to produce information that can support human decisions or fully automate them...