Book Image

The Art of Data-Driven Business

By : Alan Bernardo Palacio
Book Image

The Art of Data-Driven Business

By: Alan Bernardo Palacio

Overview of this book

One of the most valuable contributions of data science is toward helping businesses make the right decisions. Understanding this complicated confluence of two disparate worlds, as well as a fiercely competitive market, calls for all the guidance you can get. The Art of Data-Driven Business is your invaluable guide to gaining a business-driven perspective, as well as leveraging the power of machine learning (ML) to guide decision-making in your business. This book provides a common ground of discussion for several profiles within a company. You’ll begin by looking at how to use Python and its many libraries for machine learning. Experienced data scientists may want to skip this short introduction, but you’ll soon get to the meat of the book and explore the many and varied ways ML with Python can be applied to the domain of business decisions through real-world business problems that you can tackle by yourself. As you advance, you’ll gain practical insights into the value that ML can provide to your business, as well as the technical ability to apply a wide variety of tried-and-tested ML methods. By the end of this Python book, you’ll have learned the value of basing your business decisions on data-driven methodologies and have developed the Python skills needed to apply what you’ve learned in the real world.
Table of Contents (17 chapters)
Part 1: Data Analytics and Forecasting with Python
Part 2: Market and Customer Insights
Part 3: Operation and Pricing Optimization

Using Machine Learning in Business Operations

Machine learning is an area of research focused on comprehending and developing “learning” processes, or processes that use data to enhance performance on a given set of tasks. It is considered to be a component of artificial intelligence. Among them, machine learning is a technology that enables companies to efficiently extract knowledge from unstructured data. With little to no programming, machine learning—and more precisely, machine learning algorithms—can be used to iteratively learn from a given dataset and comprehend patterns, behaviors, and so on.

In this chapter, we will learn how to do the following:

  • Validate the difference of observed effects with statistical analysis
  • Analyze the correlation and causation as well as model relationships between variables
  • Prepare the data for clustering and machine learning models
  • Develop machine learning models for regression and classification...