Master’s in Data Science for MIT MicroMaster's Graduates
Course Descriptions and Schedule
Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.
MSDS 410-DL : Supervised Learning Methods
Description
This course introduces traditional statistics and data modeling
for supervised learning problems, as employed in observational and
experimental research. With supervised learning there is a clear
distinction between explanatory and response variables. The
objective is to predict responses, whether they be quantitative as
with multiple regression or categorical as with logistic regression
and multinomial logit models. Students work on research and
programming assignments, exploring data, identifying appropriate
models, and validating models. They utilize techniques for
observational and experimental research design, data visualization,
variable transformation, model diagnostics, and model
selection.
This is a required course for the Analytics and Modeling
specialization.
Prerequisites: MSDS 400-DL Math for Modelers and MSDS 401-DL
Applied Statistics with R.
