The Art of Data-Driven Business Decisions: Recipes of how businesses leverage data science to optimize sales and operations
- Length: 316 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2023-01-10
- ISBN-10: 1804611034
- ISBN-13: 9781804611036
- Sales Rank: #0 (See Top 100 Books)
Learn how to make the right decisions for your business with the power of Python machine learning
Key Features
- Use the power of Python machine learning to make the right business decisions
- Follow 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 uses of data science is in helping businesses make the right decisions. This is a complicated confluence of two disparate worlds, as well as a fiercely competitive market, so you’ll need all the guidance you can get.
Whether you’re a data scientist wanting to get a business-driven perspective, or you want the decisions in your business to be guided by the power of machine learning, The Art of Data-Driven Business Decisions will be your invaluable guide.
We start 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 we soon get into the meat of the book and look at the many and varied ways machine learning with Python can be applied to the domain of business decisions through real-world business problems that you can tackle yourself. You will gain priceless practical insights into the value that machine learning can provide to your business, as well as the technical ability to apply a wide variety of tried and tested machine learning methods.
By the end of this book, you will have learned the value of basing business decisions on data-driven methodologies, and you will have the Python skills to apply what you’ve learned in the real world.
What you will learn
- Scrape web pages for their data with Python
- Create effective dashboard with Seaborn
- Predict whether a customer will cancel a 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
- Use NLP and Scikit-Learn to improve digital advertisements
Who This Book Is For
The primary audience are data scientists, machine learning engineers and developers, data engineers, and business decision makers. They want to learn the skills to implement data science projects in the areas of marketing, sales, pricing, customer success, adtech, and more from a business perspective. They want to apply data science for business processes optimization. This book intends to provide a common ground of discussion for several audience profiles within a company. Book is for business people seeking to improve their knowledge on how can data science can be used to improve business operations, and for individuals with technical skills who want to implement successful data science projects within a company and want to back their technical proposal with a strong business case.
The Art of Data-Driven Business Contributors About the author About the reviewer 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 Share Your Thoughts Download a free PDF copy of this book Part 1: Data Analytics and Forecasting 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 Chapter 2: Using Machine Learning in Business Operations Technical requirements Validating the effect of changes with the t-test Modeling relationships with multiple linear regression Establishing correlation and causation Scaling features to a range Clustering data and reducing the dimensionality Building machine learning models Summary Part 2: Market and Customer 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 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 Chapter 5: Selecting the Optimal Price with Price Demand Elasticity Technical requirements Understanding price demand elasticity Exploring the data Finding the demand curve Exploring the demand curve in code Optimizing revenue using the demand curve Summary Chapter 6: Product Recommendation Technical requirements Targeting decreasing returning buyers Understanding product recommendation systems Creating a recommender system Using the Apriori algorithm for product bundling Performing market basket analysis with Apriori Summary Part 3: Operation and Pricing Optimization Chapter 7: Predicting Customer Churn Technical requirements Understanding customer churn Exploring customer data Exploring variable relationships Predicting users who will churn Summary Chapter 8: Grouping Users with Customer Segmentation Technical requirements Understanding customer segmentation Exploring the data Feature engineering Creating client segments Understanding clusters as customer segments Summary Chapter 9: Using Historical Markdown Data to Predict Sales Technical requirements Creating effective markdowns Analyzing the data Predicting sales with Prophet Summary 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 Chapter 11: Creating a Data-Driven Culture in Business Starting to work with data Julio Rodriguez Martino Michael Curry Micaela Kulesz Bob Wuisman Wim Van Der Florian Prem Using data in organizations Florian Prem Micaela Kulesz Wim Van Der Julio Rodriguez Martino Michael Curry Bob Wuisman Jack Godau Benefits of being data-driven Wim Van Der Michael Curry Jack Godau Bob Wuisman Florian Prem Challenges of data-driven strategies Bob Wuisman Florian Prem Wim Van Der Jack Godau Micaela Kulesz Julio Rodriguez Martino Creating effective data teams Florian Prem Michael Curry Micaela Kulesz Bob Wuisman Jack Godau Julio Rodriguez Martino Wim van der Visualizing the future of data Michael Curry Florian Prem Bob Wuisman Micaela Kulesz Julio Rodriguez Martino Implementing a data-driven culture Agustina Hernandez Patrick Flink Summary Index Why subscribe? Other Books You May Enjoy Packt is searching for authors like you Share Your Thoughts Download a free PDF copy of this book
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