Responsible Data Science

Continue to expand your AI, machine learning and data science expertise by delving into the critical risk of bias in data analysis and how to avoid or mitigate it.

Enroll in Responsible Data Science

About This Online Course

Public and corporate concern about bias and other unintended harmful effects resulting from artificial intelligence or machine learning models has resulted in greater attention to the ethical practice of data science.

Continue to expand your knowledge and skills in AI, machine learning and data science by learning about the critical subject of bias in data analysis and modeling. This online training course from, developed for both data science practitioners and managers, provides guidance and practical tools to build better AI and machine learning models and avoid bias-related challenges in data models—particularly those using back-box algorithms.

This online course offers a solid framework for you to follow in implementing data science projects and an audit process to follow in reviewing them to avoid unfairness. Case studies, along with R and Python code, will be provided as part of the course content.

The required text for this course is Responsible Data Science, 1st edition (Wiley, 2021), Fleming, G., and Bruce, P. C. (available on the Wiley website). Learners must purchase the book before starting the course.

What You Will Learn

  • Identify the types of unintended harm that can arise from AI and machine learning models
  • Explain why interpretability is key to avoiding harm
  • Distinguish between intrinsically interpretable models and black-box models
  • Evaluate tradeoffs between model performance and interpretability
  • Establish and implement a responsible data science framework for your projects
  • Evaluate predictor impact in black-box models using interpretability methods
  • Assess the performance of models with metrics to measure bias and unfairness
  • Conduct an audit of a data science project from an ethical standpoint

Your Instructors

Grant Fleming is a data scientist at Elder Research and co-author (with Peter Bruce) of the Responsible Data Science (Wiley, 2021). His professional focus is on machine learning for social science applications, model explainability and building tools for reproducible data science. Previously, he was a research contractor with the United States Agency for International Development.

Mr. Fleming received his Master of Science from the Institute for Advanced Analytics at North Carolina State University and Bachelor of Arts/Science from the University of South Carolina.

Peter Bruce is founder and president of The Institute for Statistics Education at He is the developer of Resampling Stats ad-on for Microsoft Excel (originated by Julian Simon in the 1970’s), and also has taught resampling statistics at the University of Maryland and via a variety of short courses.

Mr. Bruce is the author of Responsible Data Science with Mr. Fleming (Wiley, 2021); Machine Learning for Business Analytics, with Galit Shmueli, Peter Gedeck, Inbal Yahav and Nitin R. Patel (prior title Data Mining for Business Analytics) (Wiley, 2016); JMP version (2017); R version (2018); and Python version (2019). He also is the author of Introductory Statistics and Analytics (Wiley, 2015), and Practical Statistics for Data Scientists, (O’Reilly, 2016) with Andrew Bruce and Peter Gedeck. His books have been translated into Japanese, Chinese, Korean, German, Polish and Spanish.

Mr. Bruce received his Master of Arts in Russian from Harvard University, Massachusetts, his Master of Business Administration for the University of Maryland and his Bachelor of Arts in Russian from Princeton University. New Jersey.

Who Should Take This Course

This course is perfect for data science architects and programmers and managers of data science projects and teams.



You should have familiarity with predictive modeling and able to work in R or Python. Taking either of the following courses from can help you prepare:

Course Certificate

A record of completion will be issued, along with professional development credits in the form of continuing education units upon 50-percent completion.

In addition, a Credly badge to add to your LinkedIn profile will be issued upon 80-percent completion of this online training course.

Course Format

This self-paced, online training course takes place at The Institute for Statistics Education at for four weeks. During each session week, you can participate at times of your own choosing—there are no set times for the lessons. 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.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Course Pricing

$599 (per person)

Register through FedLearn using the special promo code FedLearn22 and receive a five-percent discount on the original online course price.

Continuing Education Unit Credits

This online course provides 5.0 CEUs upon 50-percent completion.

This course is also recommended for 3.0 upper division college credits by the American Council on Education upon 80-percent completion.