Category Teaching Philosophy

Philosophy of Data Science: Embrace the Practical and the Profound

This is my last blog post for Statistics 4242, Introduction to Data Science at Columbia University. All final projects have been turned in; grades have been given; the semester is over. I reserve the right to start blogging again at a later date. Dear Students, From the beginning, this course viewed Data Science simultaneously in […]

On Inspiring Students and Being Human

Dear Students, Lest you think (yes, I know I used that turn of phrase in posts before. I like it.) that I am bragging about my character traits (in which case you don’t know me well enough yet– I never brag, and who isn’t human?), wipe that thought from your mind, and read on. On […]

Curse of dimensionality

This is a guest post by Professor Matthew Jones, from Columbia’s History department, who has been attending the course. I invited him to give his perspective on the course thus far. Few things lurk as much a challenge and instigation in data mining (or machine learning or the data sciences) as the “curse of dimensionality.” […]

Who is taking this class?

We have students from the following departments: Business/Marketing , Statistics, Earth and Environmental Engineering, Biomedical Informatics, Industrial Engineering/Operations Research, Actuarial Science, Quantitative Methods in the Social Sciences, Psychology, Economics, Physics, Politics, Mechanical Engineering,  Sociology, History, Journalism (Some departments have more than others. If I missed anyone, sorry! You’re welcome too.) We have students who are Undergrads, […]

How am I supposed to get experience if I need experience to get experience?

I pulled this excerpt out of my post about defining the scope of the course, because I think maybe I buried the lead: There are a shortage of people who can do Data Science well. When I talk to people in positions recruiting, they don’t want recent grads because the recent grads don’t have enough […]