The Ethical Practice of Data Science

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.

$549 | Enroll Now
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Overview

There is increasing public and corporate concern about bias and other unintended harmful effects resulting from data science models. 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.

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
  • Explain the tradeoff between performance and interpretability
  • Establish and implement a Responsible Data Science framework for your projects
  • Apply interpretability methods to black box models
  • Measure the performance of models with metrics to assess 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

grant_headshot

Mr. Grant Fleming

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, Grant was a research contractor for USAID.  

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Peter-2019-sweater-cropped

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 Data Mining for Business Analytics, with Galit Shmueli, Peter Gedeck, Inbal Yahav and Nitin R. Patel (Wiley, 3rd ed. 2016; JMP version 2017, R version 2018, Python version 2019), Introductory Statistics and Analytics (Wiley, 2015), and Statistics for Data Scientists, with Andrew Bruce and Peter Gedeck, ...

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Course Syllabus

Week 1

Introduction
  • Review of predictive modeling
  • Why Ethical 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

2021

Jul 23, 2021 to Aug 20, 2021

2022

Jan 28, 2022 to Feb 25, 2022

Jul 22, 2022 to Aug 19, 2022

2023

No classes scheduled at this time.

Send me reminder for next class

Prerequisites

You should be familiar with predictive modeling and able to work in R or Python.  Either of the following courses is good preparation:

Course Icon

Predictive Analytics 1 – Machine Learning Tools with Python

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: May 14, 2021, Sep 10, 2021, Jan 14, 2022, May 13, 2022
Course Icon

Predictive Analytics 1 – Machine Learning Tools with R

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: May 14, 2021, Sep 10, 2021, Jan 14, 2022, May 13, 2022

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Frequently Asked Questions

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We provide custom training services that can include, but is not limited to: 

  • Custom curriculum 
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  • Live training on-site
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  • Special tuition pricing for multiple-student volume

Contact us to see how we can help you meet your training needs.

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  • 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.

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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/

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Please see our knowledge center for more information.

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FAQs + Knowledge Base

Related Courses

Course Icon

Predictive Analytics 1 – Machine Learning Tools with Python

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: May 14, 2021, Sep 10, 2021, Jan 14, 2022, May 13, 2022
Course Icon

Predictive Analytics 1 – Machine Learning Tools with R

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: May 14, 2021, Sep 10, 2021, Jan 14, 2022, May 13, 2022

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 Ethical Practice of Data Science, Wiley, forthcoming.  The book is not yet available but draft material will be provided online.

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.

 

Supplemental Information

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The Ethical Practice of Data Science
$549 | Enroll Now
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