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

How do machines learn?

How do algorithms learn? How can we define learning? As humans, we learn a lot throughout our lives. It is a natural task for us. In the first few years of our lives, we learn how to control our body, walk, speak, and recognize different objects. We constantly get new experiences, and these experiences change the way we think, behave, and act. Can a piece of computer code learn like we do? To approach machine learning, we first need to come up with a way to transmit experience directly to the algorithm.

In practical cases, we are interested in teaching algorithms to perform all kinds of specific tasks faster, better, and more reliably that we can do ourselves. For now, we will focus on prediction and recognition tasks. Thus, we want to build algorithms that are able to recognize patterns and predict future outcomes. The following table shows some examples...