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

Clustering data and reducing the dimensionality

The process of clustering involves grouping the population or data points into a number of groups so that the data points within each group are more similar to one another than the data points within other groups. Simply said, the goal is to sort any groups of people who share similar characteristics into clusters. It is frequently used in business analytics. How to arrange the enormous volumes of available data into useful structures is one of the issues that organizations are currently confronting.

Image segmentation, grouping web pages, market segmentation, and information retrieval are four examples of how clustering can help firms better manage their data. Data clustering is beneficial for retail firms since it influences sales efforts, customer retention, and customer shopping behavior.

The goal of the vector quantization technique known as “K-means clustering,” which has its roots in signal processing, is to...