Public and corporate concern about bias and other unintended harmful effects resulting from data science models has resulted in greater attention to the ethical practice of data science. This course, for both data science practitioners and managers, provides guidance and practical tools to build better models and avoid these problems. The course offers a framework to follow in implementing data science projects, and an audit process to follow in reviewing them. Case studies along with R and Python code are provided.
Responsible Data Science
This course, for both data science practitioners and managers, provides guidance and tools to build better models that avoid bias and unfairness.
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Learning Outcomes
This course covers processes and tools to minimize bias and unfairness in predictive models, particularly those using black-box algorithms. You will learn how to:
- Identify the types of unintended harm that can arise from AI 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
Who Should Take This Course
Data Science architects and programmers, managers of data science projects and teams.
Instructors
Mr. Peter Bruce
Mr. Peter Bruce is Founder and President of The Institute for Statistics Education at Statistics.com. He is the developer of Resampling Stats software (originated by Julian Simon in the 1970's), and has also taught resampling statistics at the University of Maryland and in a variety of short courses. He is the author of Responsible Data Science, with Grant 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, 3rd ed. 2016; JMP version 2017, R version 2018, Python version 2019), Introductory Statistics and Analytics (Wiley, 2015), and Practical Statistics for Data Scientists, with Andrew Bruce and Peter Gedeck, (O'Reilly 2016). His books have been translated into Japanese, Chinese, Korean, German, Polish and Spanish.
Course Syllabus
Week 1
Introduction
- Review of predictive modeling
- Why Responsible Data Science?
- Types of harms
- The black box problem
- Legal considerations, legal
Week 2
Interpretability
- Why interpretability is an ethical issue
- Interpretability versus performance tradeoff
- Establishing baseline
- Intrinsically-interpretable algorithms
- Interpretability for black-box algorithms
- Global interpretability
- Local interpretability
Week 3
The Responsible Data Science (RDS) Process and getting started
- The RDS Framework
- Enhancing standard "best practices" from an ethical standpoint
- The 10 RDS questions to answer
Week 4
The Audit
- Metrics for assessing bias
- Assessment of results of applying local and global interpretability methods
- Presentation
- Technical
- Nontechnical
- Auditing for neural nets (briefly)
Class Dates
2022
Aug 19, 2022 to Sep 16, 2022
2023
Jan 20, 2023 to Feb 17, 2023
Aug 18, 2023 to Sep 15, 2023
2024
Jan 19, 2024 to Feb 16, 2024
Prerequisites
You should be familiar with predictive modeling and able to work in R or Python. Either of the following courses is good preparation:
This course introduces the basic paradigm for predictive modeling: classification and prediction.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using Python | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Sep 9, 2022, Jan 13, 2023, May 12, 2023
Class Start Dates: Sep 9, 2022, Jan 13, 2023, May 12, 2023
This course introduces to the basic predictive modeling paradigm: classification and prediction.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Sep 9, 2022, Jan 13, 2023, May 12, 2023
Class Start Dates: Sep 9, 2022, Jan 13, 2023, May 12, 2023
Frequently Asked Questions
Do you provide custom training services?
We provide custom training services that can include, but is not limited to:
- Custom curriculum
- Custom courses and materials
- Live training on-site
- Live training via webinar
- Special tuition pricing for multiple-student volume
Contact us to see how we can help you meet your training needs.
Can I transfer or withdraw from a course?
We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.
- Students are entitled to a full refund if a course they are registered for is canceled.
- You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
- Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.
Please see this page for more information.
Is the Institute for Statistics Education certified?
The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: https://www.schev.edu/
Please see our knowledge center for more information.
How do I enroll in a course?
In most courses, you are able to register for any course until the course start date.
Once you decide which course you want to take, click the ‘Enroll Now’ button and follow the purchasing process. Once you have purchased the course, you are enrolled in the class date you have selected.
You will receive a personal email message confirming your registration in the course. A second email is sent to you three days before the course start date with instructions to access the course in our Learning Management System (LMS).
Please see our knowledge center for more information.
Related Courses
This course introduces the basic paradigm for predictive modeling: classification and prediction.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using Python | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Sep 9, 2022, Jan 13, 2023, May 12, 2023
Class Start Dates: Sep 9, 2022, Jan 13, 2023, May 12, 2023
This course introduces to the basic predictive modeling paradigm: classification and prediction.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Sep 9, 2022, Jan 13, 2023, May 12, 2023
Class Start Dates: Sep 9, 2022, Jan 13, 2023, May 12, 2023
Additional Course Information
Organization of Course
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.
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.
Time Requirements
About 15 hours per week, at times of your choosing.
Homework
Homework consists primarily of practical exercises with R or Python.
Course Text
The required text for this course is Responsible Data Science, Wiley, by Grant Fleming, Peter Bruce. Please order a copy of your course textbook prior to course start date.”
Software
Python or R
Course Fee & Information
Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.
Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.
Group Rates
Contact us to get information on group rates.
Discounts
Academic affiliation? In most courses you are eligible for a discount at checkout.
New to Statistics.com? Click here for a special introductory discount code.
Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.
Options for Credit and Recognition
This course is eligible for the following credit and recognition options:
No Credit
You may take this course without pursuing credit or a record of completion.
Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.
CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.
Have a Question About This Course?
Janet Dobbins
Sales and Business Development
Phone
(571) 281-8817