Preface

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 data-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 ML. 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 developed the Python skills needed to apply what you’ve learned in the real world.

Who this book is for

This book is for data scientists, ML engineers and developers, data engineers, and business decision-makers who want to apply data science to business process optimization and develop the skills needed to implement data science projects in marketing, sales, pricing, customer success, ad tech, and more from a business perspective. Other professionals looking to explore how data science can be used to improve business operations, as well as individuals with technical skills who want to back their technical proposal with a strong business case, will also find this book useful.

What this book covers

Chapter 1, Analyzing and Visualizing Data with Python, serves as an introduction to data analytics with pandas and data analytics with Seaborn. You will learn how to transform, visualize, and analyze data, as these are the fundamental tools that will be used throughout the book. You will be introduced to these libraries through examples based on real-life example applications.

Chapter 2, Using Machine Learning in Business Operations, introduces scikit-learn, the most popular ML framework for applying ML algorithms using Python. You will learn about the basic concepts of ML, how to perform training, and the inference of supervised and unsupervised algorithms. These concepts will be reinforced through exercises and used in later chapters in real-life applications regarding the optimization of various business applications.

Chapter 3, Finding Business Opportunities with Market Insights, focuses on the use of Python and search trends analysis in order to obtain valuable information from search engine data. You will learn how to obtain information about search engine trends using Python, structure and visualize the results to validate assumptions, expand queries with similar ones, and analyze the content of the results using NLP and scikit-learn.

Chapter 4, Understanding Customer Preferences with Conjoint Analysis, will introduce you to conjoint analysis, which involves analyzing user preference survey data, applying methods to determine how users weigh each attribute, and predicting how new combinations will be ranked.

Chapter 5, Selecting the Optimal Price with Price Demand Elasticity, will introduce you to the concept of price elasticity and it will use it to find the best price for different products using sales data. By the end of the chapter, you will be able to find the price that maximizes revenue and understand the demand curve.

Chapter 6, Product Recommendation, demonstrates two methods for creating product recommendations and performing market basket analysis. You will learn about collaborative filtering and a priori algorithms and how to implement them to create product recommendations using sales data.

Chapter 7, Predicting Customer Churn, will show you how to predict the subtle changes in consumer behavior using Python and scikit-learn.

Chapter 8, Grouping Users with Customer Segmentation, will help you learn about and practice, with real-life cases, methods that can be applied to model the data and which unsupervised ML methods can be used to find these groups, as well as to find their key characteristics. Finally, you will learn how to capitalize on this knowledge by learning to analyze these segments in terms of sales, and how to convey these findings in clearly defined dashboards using Seaborn.

Chapter 9, Using Historical Markdown Data to Predict Sales, will allow you to analyze the impact of promotions on historic time-series sales data using pandas and Seaborn, as well as optimize stock and storage using scikit-learn to analyze the impact of promotions and optimize storage costs.

Chapter 10, Web Analytics Optimization, will show you how to analyze digital marketing data using Python by analyzing the result of digital advertising campaigns, calculate the return on investment based on the customer lifetime value prediction, and optimize the investments being done in programmatic advertising platforms.

Chapter 11, Creating a Data-Driven Culture in Business, reaches out to business leaders to learn how they have applied data science and analytics to improve business operations. We will reach out to several chief data officers and lead data scientists to gather concrete examples of how they have applied these methods throughout several companies.

To get the most out of this book

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/The-Art-of-Data-Driven-Business-Decisions. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

results_df = pd.DataFrame(results).dropna()
results_df.columns = ['client','slope','std']
results_df.index = results_df.client
results_df = results_df.drop(['client'],axis=1)
results_df.head()

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

df.columns.tolist()
>>> ['period', 'sub_market', 'client_class', 'division', 'brand','cat', 'product', 'client_code', 'client_name', 'kgs_sold']

Any command-line input or output is written as follows:

!pip install --upgrade openpyxl scikit-surprise

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Select System info from the Administration panel.”

Tips or important notes

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Get in touch

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