The Art of Data-Driven Business

Book description

Learn how to make the right decisions for your business with the help of Python recipes and the expertise of data leaders

Key Features

  • Learn and practice various clustering techniques to gather market insights Explore real-life use cases from the business world to contextualize your learning
  • Work your way through practical recipes that will reinforce what you have learned

Book Description

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.

What you will learn

  • Create effective dashboards with the seaborn library
  • Predict whether a customer will cancel their subscription to a service
  • Analyze key pricing metrics with pandas
  • Recommend the right products to your customers
  • Determine the costs and benefits of promotions
  • Segment your customers using clustering algorithms

Who this book is for

This book is for data scientists, machine learning engineers and developers, data engineers, and business decision makers who want to apply data science for 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.

Table of contents

  1. The Art of Data-Driven Business
  2. Contributors
  3. About the author
  4. About the reviewer
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Download the example code files
    5. Conventions used
    6. Get in touch
    7. Share Your Thoughts
    8. Download a free PDF copy of this book
  6. Part 1: Data Analytics and Forecasting with Python
  7. Chapter 1: Analyzing and Visualizing Data with Python
    1. Technical requirements
    2. Using data science and advanced analytics in business
    3. Using NumPy for statistics and algebra
    4. Storing and manipulating data with pandas
    5. Visualizing patterns with Seaborn
    6. Summary
  8. Chapter 2: Using Machine Learning in Business Operations
    1. Technical requirements
    2. Validating the effect of changes with the t-test
      1. Modeling relationships with multiple linear regression
    3. Establishing correlation and causation
    4. Scaling features to a range
    5. Clustering data and reducing the dimensionality
    6. Building machine learning models
    7. Summary
  9. Part 2: Market and Customer Insights
  10. Chapter 3: Finding Business Opportunities with Market Insights
    1. Technical requirements
    2. Understanding search trends with Pytrends
    3. Installing Pytrends and ranking markets
    4. Finding changes in search trend patterns
    5. Using related queries to get insights on new trends
    6. Analyzing the performance of related queries over time
    7. Summary
  11. Chapter 4: Understanding Customer Preferences with Conjoint Analysis
    1. Technical requirements
    2. Understanding conjoint analysis
    3. Designing a conjoint experiment
    4. Determining a product’s relevant attributes
    5. OLS with Python and Statsmodels
    6. Working with more product features
    7. Predicting new feature combinations
    8. Summary
  12. Chapter 5: Selecting the Optimal Price with Price Demand Elasticity
    1. Technical requirements
    2. Understanding price demand elasticity
    3. Exploring the data
    4. Finding the demand curve
      1. Exploring the demand curve in code
      2. Optimizing revenue using the demand curve
    5. Summary
  13. Chapter 6: Product Recommendation
    1. Technical requirements
    2. Targeting decreasing returning buyers
    3. Understanding product recommendation systems
      1. Creating a recommender system
    4. Using the Apriori algorithm for product bundling
      1. Performing market basket analysis with Apriori
    5. Summary
  14. Part 3: Operation and Pricing Optimization
  15. Chapter 7: Predicting Customer Churn
    1. Technical requirements
    2. Understanding customer churn
    3. Exploring customer data
    4. Exploring variable relationships
    5. Predicting users who will churn
    6. Summary
  16. Chapter 8: Grouping Users with Customer Segmentation
    1. Technical requirements
    2. Understanding customer segmentation
      1. Exploring the data
    3. Feature engineering
    4. Creating client segments
    5. Understanding clusters as customer segments
    6. Summary
  17. Chapter 9: Using Historical Markdown Data to Predict Sales
    1. Technical requirements
    2. Creating effective markdowns
    3. Analyzing the data
    4. Predicting sales with Prophet
    5. Summary
  18. Chapter 10: Web Analytics Optimization
    1. Technical requirements
    2. Understanding web analytics
    3. Using web analytics to improve business operations
    4. Exploring the data
    5. Calculating CLV
    6. Predicting customer revenue
    7. Summary
  19. Chapter 11: Creating a Data-Driven Culture in Business
    1. Starting to work with data
      1. Julio Rodriguez Martino
      2. Michael Curry
      3. Micaela Kulesz
      4. Bob Wuisman
      5. Wim Van Der
      6. Florian Prem
    2. Using data in organizations
      1. Florian Prem
      2. Micaela Kulesz
      3. Wim Van Der
      4. Julio Rodriguez Martino
      5. Michael Curry
      6. Bob Wuisman
      7. Jack Godau
    3. Benefits of being data-driven
      1. Wim Van Der
      2. Michael Curry
      3. Jack Godau
      4. Bob Wuisman
      5. Florian Prem
    4. Challenges of data-driven strategies
      1. Bob Wuisman
      2. Florian Prem
      3. Wim Van Der
      4. Jack Godau
      5. Micaela Kulesz
      6. Julio Rodriguez Martino
    5. Creating effective data teams
      1. Florian Prem
      2. Michael Curry
      3. Micaela Kulesz
      4. Bob Wuisman
      5. Jack Godau
      6. Julio Rodriguez Martino
      7. Wim van der
    6. Visualizing the future of data
      1. Michael Curry
      2. Florian Prem
      3. Bob Wuisman
      4. Micaela Kulesz
      5. Julio Rodriguez Martino
    7. Implementing a data-driven culture
      1. Agustina Hernandez
      2. Patrick Flink
    8. Summary
  20. Index
    1. Why subscribe?
  21. Other Books You May Enjoy
    1. Packt is searching for authors like you
    2. Share Your Thoughts
    3. Download a free PDF copy of this book

Product information

  • Title: The Art of Data-Driven Business
  • Author(s): Alan Bernardo Palacio
  • Release date: December 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781804611036