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 causal inference

Up to this point, we have talked about predictive models. The main purpose of a predictive model is to recognize and forecast. The explanation behind the model's reasoning is of lower priority. On the contrary, causal inference tries to explain relationships in the data rather than to make predictions about the future events. In causal inference, we check whether an outcome of some action was not caused by so-called confounding variables. Those variables can indirectly influence action through the outcome. Let's compare causal inference and predictive models through several questions that they can help to answer:

  • Prediction models:
    • When will our sales double?
    • What is the probability of this client buying a certain product?
  • Causal inference models:
    • Was this cancer treatment effective? Or is the effect apparent only because of the...