Sean HarkinData Scientists: Don’t Feel Like You Have to be Instant Experts on LLMsIt’s an easy temptation, but it’s better to be clear that most data scientists are still mastering this tech.Jan 15Jan 15
Sean HarkinData Science for Refuting Bad IdeasOne of the most important uses of statistical methodsJan 12Jan 12
Sean HarkinLimiting Learning CapacityThe most essential idea and the most counter-intuitive idea in data science Jan 12Jan 12
Sean HarkinAgile Project Management for Data ScienceYou need it because both data and code bases spring surprises Jan 12Jan 12
Sean HarkinThe Two Cultures in Data ScienceWe get better results if we have both statistics-type backgrounds and engineering-type backgrounds in our teams Jan 12Jan 12
Sean HarkinDon’t Leave Models Static for a Long TimeIt’s common practice in many businesses, but it has serious pitfallsJan 12Jan 12
Sean HarkinUnderstand Your Data Before You Model ItEven though it’s very tempting to race ahead to the most fun part of data science Jan 12Jan 12
Sean HarkinThe Different Kinds of Data Science PredictionAnd how to avoid allergic reactions to the word “prediction”Jan 11Jan 11
Sean HarkinSaying No To Building A ModelThe importance of opposing building models when you know they will be bad.Jan 10Jan 10
Sean HarkinMachine Learning for Explaining Your DataThe case for combining traditional coefficient inference with explainable ML techniquesJan 8Jan 8