Machine Learning for Business Decisions
Crack open the black box of Machine Learning
What you'll learn
Gain hands-on experience with Machine Learning. You'll get your own Jupyter notebooks with actual code to run against hundreds of thousands of rows of real data from Evite.
Conduct Exploratory Data Analysis: assessing and cleaning data
Build, train, test, and evaluate machine learning models (using XGBoost)
Lean on business reasoning to increase model performance through feature engineering
Use your persuasion skills to convince key stakeholders of your approach
Gain full access to this course for 2 weeks from the time you start the experience
About Machine Learning for Business Decisions
How does Machine Learning (ML) and data analytics help us to make smarter business decisions?
For most, ML is a mystery.
Which is why we created this pioneering learning experience called a data application course.
Wharton’s data apps combine an engaging ARC game with just in time lessons from experts, and Jupyter notebooks where you’ll run real code against real data to build, optimize and understand the outputs of machine learning models.
A compelling story and adaptive assistance will ensure that even if you have never worked with data or built an algorithm before, you will gain a new appreciation for how Machine Learning can transform companies.
This data app requires no prior experience in Python or building and running algorithms– instruction and support are built into the experience. Whether you work with a data science team or are generally interested in understanding how machine learning works, this data app will guide you every step of the way.
You’ll learn the fundamentals of Machine Learning, using half a million rows of real Evite customer data to build and fine-tune XGBoost models
In this data app, you assume the role of a rising star at Symmetry, a data analytics company. You’ll use Python to prepare a massive dataset from Evite, and use XGBoost, a powerful ML technique, to deliver real business insight.
This game will expose you to all these areas of ML, from the technical aspects of data analysis and running ML algorithms to analyzing the outcomes and bringing the insights back into the business decision-making process.
If you’re more business and less technical, you will:
- Gain critical knowledge of the fundamentals of Machine Learning so you can ask the right questions
- Upgrade your domain knowledge by learning to combine it with modern data analytics approaches
- Gain familiarity with the challenges that data analysts face
- Understand how to use Machine Learning to better understand your business
If you’re more technical and less business, you will:
- Learn the fundamental best practices of building and optimizing machine learning
- Understand how technical skills must align with business needs
- Learn to think about data analytics and Machine Learning through a business lens, gaining a managerial perspective
- Increase your organization’s return on investment in data analytics
You have 2 weeks to complete this course from the time you click "Start Experience" from your My Interactive dashboard (after purchase).
Intrigued and want to learn more?
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The buzz from past participants
“I loved the game format for learning how to delve into the data to find insights for making sound business decisions - much better than a classroom only format. I gained a better/more detailed understanding of the steps necessary to parse/scrub the data (find errors, missing elements, etc) and what questions to ask during the process in order to prepare the data for analysis (moved from intuition to complete picture understanding). This knowledge will be immediately applied. And, as a result of participating in this game, I plan on taking some data analytics courses. Finally, as a non-technical person, it was helpful to have the technical pieces worked out as that would have been a deterrent to completing the session.”
“As a non-tech person, I thought Machine Learning would be tough to understand. But this game makes it so easy to gain crucial knowledge of ML including the issues faced during data analysis. Now I’m able to apply those skills to business to guide important decisions.”
What you'll learn
Learning Objectives
- Learn how to conduct exploratory data analysis and find common errors
- Learn how to work with an XGBoost Machine Learning algorithm to build, train, test, and evaluate your model
- Convert data into insights through understanding the questions to ask, the limits of the data, and the stories the data tells
- Overcome the black box of machine learning by understanding how to experiment with a variety of models
- Learn the role of judgment and feature engineering, and how individual decisions affect the insights gained from Machine Learning
- Understand how to deploy your quantitative solution to make real-time decisions that drive measurable improvement
Practice Objectives
- Gain access to Jupyter notebooks where you'll build, test, and train a series of XGBoost models
- Evaluate a variety of models, including using key metrics such as R-squared and Mean Absolute Error to evaluate the accuracy of your models
- Use your intuition and managerial know-how to feature engineer your model
- Make decisions based on quantitative analysis
- Engage with critical stakeholders in high-stakes settings: managers & employees
- Persuade stakeholders of your analysis and strategy, explaining your decisions to a critical stakeholder
Thinking Objectives
- Analytical thinking: the ability to analyze and frame problems
- Creative thinking: recognizing and pursuing creative approaches towards machine learning by considering multiple techniques
- Perspective-taking: paying attention to stakeholders and business perspectives with different levels of expertise, abilities, and points of view
- Learn how to conduct exploratory data analysis and find common errors
- Learn how to work with an XGBoost Machine Learning algorithm to build, train, test, and evaluate your model
- Convert data into insights through understanding the questions to ask, the limits of the data, and the stories the data tells
- Overcome the black box of machine learning by understanding how to experiment with a variety of models
- Learn the role of judgment and feature engineering, and how individual decisions affect the insights gained from Machine Learning
- Understand how to deploy your quantitative solution to make real-time decisions that drive measurable improvement
- Gain access to Jupyter notebooks where you'll build, test, and train a series of XGBoost models
- Evaluate a variety of models, including using key metrics such as R-squared and Mean Absolute Error to evaluate the accuracy of your models
- Use your intuition and managerial know-how to feature engineer your model
- Make decisions based on quantitative analysis
- Engage with critical stakeholders in high-stakes settings: managers & employees
- Persuade stakeholders of your analysis and strategy, explaining your decisions to a critical stakeholder
- Analytical thinking: the ability to analyze and frame problems
- Creative thinking: recognizing and pursuing creative approaches towards machine learning by considering multiple techniques
- Perspective-taking: paying attention to stakeholders and business perspectives with different levels of expertise, abilities, and points of view
Enroll in Machine Learning for Business Decisions
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