About This Online Course
In this games-based, online course from Wharton Interactive, you have the unique opportunity to gain valuable hands-on experience with machine learning techniques. You will use your own private Jupyter notebooks, complete with actual code, data and algorithms. By “walking a mile in the shoes” of data scientists, you will gain critical insight about the practical application of what it takes to leverage machine learning to make “smarter,” data-driven decisions.
Why a Games-Based Course?
The narrative and in-game characters will support, challenge and engage you. By immersing in the scenario of this games-based course, you get to play the “starring” role in this hyper-immersive, adaptive learning experience. It is the latest state-of-the-art experiential learning from the Wharton School: alternative reality courses.
Throughout the ARC, you are tasked with conducting exploratory data analysis to assess and clean the data, as well as building, training and testing machine learning models using XGBoost. Additionally, you will be challenged to use your business reasoning skills to increase the performance of your machine learning models through feature engineering. Lastly, you will use your persuasion skills to convince key stakeholders of your chosen approach to machine learning to gain their support and approval.
This online course is a unique and engaging way for learners to gain practical experience with machine learning and apply it to real-world scenarios.
Click here to watch a video to learn more.
What You Will Learn
- 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 tell
- 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
Raghu Iyengar is a professor of marketing at the Wharton School and the faculty director of Wharton Customer Analytics. Dr. Iyengar’s research interests fall in two domains: pricing and social influence. In the area of pricing, his work focuses on the impact of multi-part pricing schemes on consumer response.
Dr. Iyengar’s research has been published (or is forthcoming) in Journal of Marketing Research, Marketing Science, Psychometrika, Quantitative and Marketing Economics and Experimental Economics. He serves on the editorial boards of the Journal of Marketing Research, Marketing Science and the International Journal of Research in Marketing, and earned his Doctorate and Master of Philosophy from Columbia University and his Bachelor in Technology from IIT Kanpur, India.
Who Should Take This Course
This course is designed for anyone running the gamut of data scientists to individuals who are interested in better understanding how machine learning works and how it can help make better data-driven decisions.
Games-based, self-paced, online training course (approximately two hours).
$99 (per person)
Register through FedLearn using the special promo code FEDLEARN_5%_OFF and receive a five-percent discount on the original online course price.
Continuing Education Unit Credits
Wharton interactive currently does not offer CEUs for its courses.