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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
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1
Part 1: Data Analytics and Forecasting with Python
Part 1: Data Analytics and Forecasting with Python
Free Chapter
2
Chapter 1: Analyzing and Visualizing Data with Python
Chapter 1: Analyzing and Visualizing Data with Python
Technical requirements
Using data science and advanced analytics in business
Using NumPy for statistics and algebra
Storing and manipulating data with pandas
Visualizing patterns with Seaborn
Summary
3
Chapter 2: Using Machine Learning in Business Operations
Chapter 2: Using Machine Learning in Business Operations
Technical requirements
Validating the effect of changes with the t-test
Establishing correlation and causation
Scaling features to a range
Clustering data and reducing the dimensionality
Building machine learning models
Summary
4
Part 2: Market and Customer Insights
Part 2: Market and Customer Insights
5
Chapter 3: Finding Business Opportunities with Market Insights
Chapter 3: Finding Business Opportunities with Market Insights
Technical requirements
Understanding search trends with Pytrends
Installing Pytrends and ranking markets
Finding changes in search trend patterns
Using related queries to get insights on new trends
Analyzing the performance of related queries over time
Summary
6
Chapter 4: Understanding Customer Preferences with Conjoint Analysis
Chapter 4: Understanding Customer Preferences with Conjoint Analysis
Technical requirements
Understanding conjoint analysis
Designing a conjoint experiment
Determining a product’s relevant attributes
OLS with Python and Statsmodels
Working with more product features
Predicting new feature combinations
Summary
7
Chapter 5: Selecting the Optimal Price with Price Demand Elasticity
Chapter 5: Selecting the Optimal Price with Price Demand Elasticity
Technical requirements
Understanding price demand elasticity
Exploring the data
Finding the demand curve
Summary
8
Chapter 6: Product Recommendation
Chapter 6: Product Recommendation
Technical requirements
Targeting decreasing returning buyers
Understanding product recommendation systems
Using the Apriori algorithm for product bundling
Summary
9
Part 3: Operation and Pricing Optimization
Part 3: Operation and Pricing Optimization
10
Chapter 7: Predicting Customer Churn
Chapter 7: Predicting Customer Churn
Technical requirements
Understanding customer churn
Exploring customer data
Exploring variable relationships
Predicting users who will churn
Summary
11
Chapter 8: Grouping Users with Customer Segmentation
Chapter 8: Grouping Users with Customer Segmentation
Technical requirements
Understanding customer segmentation
Feature engineering
Creating client segments
Understanding clusters as customer segments
Summary
12
Chapter 9: Using Historical Markdown Data to Predict Sales
Chapter 9: Using Historical Markdown Data to Predict Sales
Technical requirements
Creating effective markdowns
Analyzing the data
Predicting sales with Prophet
Summary
13
Chapter 10: Web Analytics Optimization
Chapter 10: Web Analytics Optimization
Technical requirements
Understanding web analytics
Using web analytics to improve business operations
Exploring the data
Calculating CLV
Predicting customer revenue
Summary
14
Chapter 11: Creating a Data-Driven Culture in Business
Chapter 11: Creating a Data-Driven Culture in Business
Starting to work with data
Using data in organizations
Benefits of being data-driven
Challenges of data-driven strategies
Creating effective data teams
Visualizing the future of data
Implementing a data-driven culture
Summary
15
Index
Index
Why subscribe?
16
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Analyzing and Visualizing Data with Python

Advanced analytics and data science now play a major role in the majority of businesses. It supports organizations in tracking, managing, and gathering performance metrics to enhance organizational decision-making. Business managers can utilize innovative analysis and machine learning to help them decide how to best engage customers, enhance business performance, and increase sales. Data science and analytics can be utilized to create user-centric products and make wise choices. This can be achieved by comparing various product aspects and studying consumer feedback and market trends to develop goods and services that can draw clients and keep them around for an extended period.

This book is intended for everyone who wants to have an introduction to the techniques and methods of data science, advanced analytics, and machine learning for studying business cases that have been impacted by the use of these methods. The cases shown are heavily based on real use cases, with a demonstrated positive impact in various companies of different sectors. So, anyone who might be considering the application of data science in business operations, regardless of whether they are a seasoned business analyst seeking to enhance their list of skills, or a manager looking for methods that can be applied to maximize certain operations, can benefit from the examples discussed in this book.

In this chapter, we will lay down the initial components that will be used throughout this book to manage the data, manipulate it, and visualize it. Specifically, we will discuss the following:

  • The use of data science in business and the main differences with roles such as business or data analysts
  • The use of statistical programming libraries such as NumPy to apply matrix algebra and statical methods
  • Storing the data in pandas, a library for data analysis and manipulation that is widely used in the context of data science
  • Visualization with Seaborn and how the different types of charts can be used in different kinds of situations

Next, we will discuss the technical requirements that you will need to be able to follow the examples presented in this chapter.

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End of Section 1
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