Can we trust anything we see? Or hear? Last week saw the news that James Earl Jones was retiring from his role voicing Darth Vader in the Star Wars franchise, and farming it out to AI. Brief musings follow.Specifically, Jones was licensing the rights to his voice to Lucasfilm so they could recreate the voiceContinue reading “Oct 6: Ethical AI: Darth Vader and the Cowardly Lion”
Blog
Oct 19: Data Literacy – The Chainsaw Case
“In the age of Big Data we often believe that our predictions about the future are better than ever before. But …in the real world, we often get better results by using simple rules and considering less information.” (From a review of Gerd Gigerenzer’s Data Savvy) The Introduction to Data Literacy course, taught by Veronica Carlan, is your entryContinue reading “Oct 19: Data Literacy – The Chainsaw Case”
Data Literacy – The Chainsaw Case
A famous business school case by Harvard Professor Michael Porter on forecasting chainsaw sales dramatically illustrated the limits of statistical models when common business sense and clear-eyed thinking are missing. In the chainsaw case, students were asked to forecast the future U.S. demand for chainsaws, a growing market, and assess the relative positions of differentContinue reading “Data Literacy – The Chainsaw Case”
Word of the Week – Drift
In deployed machine learning pipelines, “drift” is changes in the model environment that cause the model performance to degrade over time. Drift might result from data quality changes. For example, increasing amounts of missing values in the input data. Or a company might alter the definitions in categories (e.g. product groupings) that are features inContinue reading “Word of the Week – Drift”
Aug 5: AI success does not always lead to business success
This week we look at several case studies where success on the AI front was not sufficient to assure longer term business success. Our course spotlight is on: Aug 20 – Sep 17: Introduction to Design of Experiments Learn how to design experiments (especially costly ones) in ways that yield the most information for theContinue reading “Aug 5: AI success does not always lead to business success”
Book Review – Noise
Who would have thought that an entire book devoted to the bias-variance tradeoff would make it to the NY Times business best seller list? The book is the recently-published Noise, by Daniel Kahneman, Olivier Sibony and Cass R. Sunstein, and, as of the beginning of August, it was #2 on the list. Kahneman, who wroteContinue reading “Book Review – Noise”
Word of the Week – Label Spreading
A common problem in machine learning is the “rare case” situation. In many classification problems, the class of interest (fraud, purchase by a web visitor, death of a patient) is rare enough that a data sample may not have enough instances to generate useful predictions. One way to deal with this problem is, in essence,Continue reading “Word of the Week – Label Spreading”
AI Success, But Not Business Success
In their book, “Mining Your Own Business,” Jeff Deal and Gerhard Pilcher, COO and CEO of Elder Research respectively, describe what I’ll call “The Case of the Climbing Churn.” Churn is when a subscriber cancels or fails to renew a service or subscription. A successful predictive model for identifying likely churners was deployed for aContinue reading “AI Success, But Not Business Success”
July 22: Odds and Betting
This week we look at odds and betting; our course spotlights are July 23 -Aug 20: SQL – Responsible Data ScienceJuly 30 -Aug 27: SQL – Biostatistics 1 – For Medical Science and Public Health See you in class! – Peter BruceFounder of The Institute for Statistics Education at Statistics.com …………………………….. News You Need to Know What’sContinue reading “July 22: Odds and Betting”
Word of the Week – Incidence versus Prevalence
Epidemiological terms are top of mind now, due to the pandemic. Here are two that often confuse: incidence and prevalence. For example, I encountered the following sentence on a popular medical web site: “Knee meniscal injuries are common with an incidence of 61 cases per 100,000 persons and a prevalence of 12% to 14%.” IContinue reading “Word of the Week – Incidence versus Prevalence”
Why Statisticians Like Odds
In your introductory statistics class, probability took center stage. Odds were for gamblers. But it turns out odds play an important role in statistics, too. The relationship between the two is simple. To estimate the probability that event “A” will happen, we divide the number of times A occurs by the number of all events,Continue reading “Why Statisticians Like Odds”
June 2: From Data Science to Data Engineering
This week we look at the increasingly important role of data engineers. Our spotlight is on our Programming for Data Science certificate program -you can start in July with July 9 -Aug 6: SQL – Introduction to Database Queries See you in class! – Peter BruceFounder of The Institute for Statistics Education at Statistics.com …………………………….. NewsContinue reading “June 2: From Data Science to Data Engineering”
Words of the Week – Inference and Confidence
An often-overlooked basic part of learning new things is vocabulary: if you don’t fully understand the meaning of terms, you are handicapped. Worse, if you think you do understand, but that understanding is wrong, you are deprived of the ability to identify the gap in your understanding. This can happen in data science, where differentContinue reading “Words of the Week – Inference and Confidence”
May 5: Deceptive Data Leaks
This week we discuss data leaks, which can render apparently well-performing machine learning models useless. Our spotlight is on the Thomas Edison University Data Science Analytics Master’s Program, which was developed in partnership with Statistics.com See you in class! – Peter BruceFounder of The Institute for Statistics Education at Statistics.com …………………………….. News You Need toContinue reading “May 5: Deceptive Data Leaks”
Student Spotlight – Thomas Karagiorgios
He spoke with Val Woodside, our Customer Success Specialist at Statistics.com about his experience in the certificate program. Tell us about your experience at Statistics.com? It was excellent. For me it was the first time that I studied through this online process. You have to study on your own and then you can send inContinue reading “Student Spotlight – Thomas Karagiorgios”
Controlling Leaks
Good psychics have a knack for getting their audience to reveal, unwittingly, information that can be turned around and used in a prediction. Statisticians and data scientists fall prey to a related phenomenon, leakage, when they allow into their models highly predictive features that would be unavailable at prediction time. In one noted example, aContinue reading “Controlling Leaks”
March 9: Statistics and Data Science in Practice
This week we spotlight our introductory statistics courses, and take a look at supposed “need to know” statistical concepts for data science. See you in class! – Peter BruceFounder of The Institute for Statistics Education at Statistics.com …………………………….. News You Need to Know What’s happening in the field of Data Science, Analytics, Statistics? STATISTICS INContinue reading “March 9: Statistics and Data Science in Practice”
Feb 23: Statistics and Data Science in Practice
This week we look at a backdoor method to make predictions come true. Our student spotlight is on Staci Taylor, Assistant Prof. of Nursing at Southeastern Louisiana State Univ., and our featured programs are: Analytics for Data Science Biostatistics See you in class! – Peter BruceFounder of The Institute for Statistics Education at Statistics.com ……………………………..Continue reading “Feb 23: Statistics and Data Science in Practice”
Word of the Week – Ruin Theory
The classic Gambler’s Ruin puzzle has an actuarial parallel: “Ruin Theory,” the calculations that govern what an insurance company should charge in premiums to reduce the probability of “ruin” for a given insurance line. “Ruin” means encountering claims that exhaust initial reserves plus accumulated premiums. The process can be depicted as a time plot, whereContinue reading “Word of the Week – Ruin Theory”
Puzzle – Gambler’s Ruin
Which is better – wealth or ability? Fred Mosteller posed this question in his classic 1965 small compendium Fifty Challenging Problems in Probability, in the context of the Gambler’s Ruin puzzle. Two players, M and N, engage in a game in which $1 is transferred from one player to the other at each play. PlayerContinue reading “Puzzle – Gambler’s Ruin”