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Tuesday, Jun 2, 2020
Hundreds of Bachelors and Masters Degree programs, Certificate programs, and bootcamps have sprung up to teach the science behind Data Science, including machine learning algorithms, statistics, and coding in python, R, and SQL. Yet data scientists quickly discover that it takes more to be a data scientist than knowing the science and data doesn’t always cooperate with the analyst! Dean describes five things that are critical to success in building predictive models and creating solutions with machine learning in a business context that are rarely taught in school. Real-world examples show how these principles matter operationally. Hint: as cool as Gradient Boosted Trees, Random Forests, and Deep Learning networks are, none of these principles are related to algorithms.