The Institute for Statistics Education at Statistics.com is offering a course on The Ethical Practice of Data Science. This course, for both data science practitioners and managers, provides guidance and tools to build better models that avoid bias and unfairness.There is increasing public and corporate concern about bias and other unintended harmful effects resulting from data science models. This online course provides guidance and practical tools to build models that avoid these problems. The course offers a framework to follow when implementing data science projects and an audit process to follow when reviewing them. Case studies along with R and Python code are provided.
Organization: This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.
Time Requirements: About 15 hours per week, at times of your choosing.
Course Text: The Ethical Practice of Data Science, Wiley, forthcoming. The book is not yet available but draft material will be provided online.
Software: Python or R
Syllabus
About the Instructors
Peter Bruce founded the Institute for Statistics Education at Statistics.com in 2002. The Institute specializes in introductory and graduate level online education in statistics, optimization, risk modeling, predictive modeling, data mining, and other subjects in quantitative analytics.
Grant Fleming is a Data Scientist at Elder Research Inc. During his time at Elder Research, he has worked with clients in both government and the private sector on statistical testing, data asset creation, predictive analytics, and latent variable modeling. He has given multiple talks on machine learning interpretability and fairness and is working on developing software packages for creating reproducible and interpretable black box models.