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


In this chapter, we have explored the practical applications of AI, data science, machine learning, deep learning, and causal inference. We have defined machine learning as a field that studies algorithms that use data to support decisions and give insights without specific instructions. There are three main machine learning methodologies: supervised, unsupervised, and reinforcement learning. In practice, the most common types of task we solve using machine learning are regression and classification. Next, we described deep learning as a subset of machine learning devoted to studying neural network algorithms. The main application domains of deep learning are computer vision and NLP. We have also touched on the important topic of causal inference: the field that studies a set of methods for discovering causal relationships in data. You now know a lot about general data...