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 AI

You now have a good understanding of what data science can do and how we can check whether it works. We have covered the main domains of data science, including machine learning and deep learning, but still, the inner workings of the algorithms are difficult to discern through the fog. In this chapter, we will look at algorithms. You will get an intuitive understanding of how the learning process is defined using mathematics and statistics. Deep neural networks won't be so mystical anymore, and common machine learning jargon will not scare you but provide understanding and ideas to complete the ever-growing list of potential projects.

You are not the only one who will benefit from reading this chapter. Your new knowledge will streamline communication with colleagues, making meetings short and purposeful and teamwork more efficient. We will start at the heart...