Generalization, Utility, and Experimentation: ML Concepts for Making Better Business Decisions

This hands-on, self-service lab introduces fundamental concepts of machine learning (ML) as they apply to making data driven decisions. Our goal is to empower decision makers to make more effective use of machine learning results and be better able to evaluate opportunities to apply ML in their industries. You will get a brief overview of what it means to discover generalizable patterns in data, and learn basic principles of how to apply probabilistic results, including optimizing the business value of applying machine learning classifiers based on their sensitivity and specificity. Finally, you will learn how ML and advanced analytics can help to guide (but not replace) the process of experimentally testing the effects of incremental changes to products and processes.

This is a non-programming workshop for business leaders and other people involved in managing products and making decisions based on data. To start please go to the README file in each section. These describe the hands-on exercises that require only a web browser.

The full downloadable content is here in this repository datascience4managers.

Contents

Part 1 - Learn how machine learning (ML) differs from traditional software engineering

Part 2 - See how ML fits in the context of making better business decisions

Part 3 - Understand why causal relationships matter in data analysis, and why we still need to do experiments

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.