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 data science and advanced analytics in business

Most of the, time the question of what differentiates a data scientist from a business analyst arises, as both roles focus on attaining insight from data. From a certain perspective, it can be considered that data science involves creating forecasts by analyzing the patterns behind the raw data. Business intelligence is backward-looking and discovers the previous and current trends, while data science is forward-looking and forecasts future trends.

Business decision-making strongly relies on data science and advanced analytics because they help managers understand how decisions affect outcomes. As a result, data scientists are increasingly required to integrate common machine learning technologies with knowledge of the underlying causal linkages. These developments have given rise to positions like that of the decision scientist, a technologist who focuses on using technology to support business and decision-making. When compared to a different employment description known as a “data scientist” or “big data scientist,” however, the phrase “decision scientist” becomes truly meaningful.

Most times, there might be confusion between the roles of business analysts, data scientists, and data analysts. Business analysts are more likely to address business problems and suggest solutions, whereas data analysts typically work more directly with the data itself. Both positions are in high demand and are often well paid, but data science is far more engaged in forecasting since it examines the patterns hidden in the raw data.