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

Finding Business Opportunities with Market Insights

In recent years, the word insight has been used with more frequency among innovation market testers. Most of the time it’s utilized without a clear definition, sometimes implying that there are hidden patterns in the data that is being utilized, or it can be used in the context of business to create new sources of revenue streams, to define more clearly the conditions and preferences of a given market, or how the different customer preferences vary across different geographies or groups.

In this chapter, we will use search engine trends to analyze the performance of different financial assets in several markets. Overall, we will focus on the following:

  • Gathering information about the relative performance of different terms using the Google Trends data with the Pytrends package
  • Finding changes in the patterns of those insights to identify shifts in consumer preferences
  • Using information about similar queries...