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

Determining a product’s relevant attributes

As mentioned before, we will perform a conjoint analysis to weigh the importance that a group of users gives to a given characteristic of a product or service. To achieve this, we will perform a multivariate analysis to determine the optimal product concept. By evaluating the entire product (overall utility value), it is possible to calculate the degree of influence on the purchase of individual elements (partial utility value). For example, when a user purchases a PC, it is possible to determine which factors affect this and how much (important). The same method can be scaled to include many more features.

The data to be used is in the form of different combinations of notebook features in terms of RAM, storage, and price. Different users ranked these combinations.

We will use the following Python modules in the next example:

  • Pandas: Python package for data analysis and data manipulation.
  • NumPy: Python package that...