Through the examination of data, data science seeks to enhance decision-making. Today, data science decides which emails are filtered into our spam folders, which adverts we see online, which books and movies are recommended to us online, and even how much we pay for health insurance.
This book gives readers access to theories, data science methods, tools, and analytics while covering a wide range of topics in the field of data science. The book's highlights include Open Source: Modelling in R to Bayes Theorem.
It provides a succinct overview of the rapidly developing discipline of data science, outlining its historical development, present applications, problems with the data infrastructure, and ethical dilemmas. The proper method for managing a data science project will be covered, along with helpful advice and best practices to help you along the way.
- Learn the basics of data science and explore its possibilities and limitations
- Manage data science projects and assemble teams effectively even in the most challenging situations
- Understand management principles and approaches for data science projects to streamline the innovation process