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)
1
Part 1: Data Analytics and Forecasting with Python
4
Part 2: Market and Customer Insights
9
Part 3: Operation and Pricing Optimization

Exploring the data

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

  • pandas: Python package for data analysis and data manipulation.
  • NumPy: This is a library that adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
  • Statsmodels: Python package that provides a complement to SciPy for statistical computations, including descriptive statistics and estimation and inference for statistical models. It provides classes and functions for the estimation of many different statistical models.
  • Seaborn and Matplotlib: Python packages for effective data visualization.

We’ll get started using the following steps:

  1. The following block of code will load all the required packages, as well as load the data and show the first five rows of it. For readability purposes, we will limit the maximum number of rows to be shown to 20, set the limit of maximum...