BIOSTAT 620 Introduction to Health Data Science

Course Information

Instructor: Dylan Cable [dmcable@umich.edu]
Graduate student instructor: Yize Hao [yizehao@umich.edu]
Location: SPH I 1655
Date and time: Tue & Thu 8:30 - 10:00am
Textbooks: https://github.com/rafalab/dsbook-part-1, https://github.com/rafalab/dsbook-part-2
Canvas: https://umich.instructure.com/courses/727331
GitHub repo: https://github.com/dmcable/BIOSTAT620
Remember to read the syllabus, listen to SD.

Lectures

Lecture slides, class notes, and problem sets are linked below. New material is added approximately on a weekly basis.

Dates	Topic	Slides	Reading
Jan 09	Productivity Tools	Intro, Unix, RStudio	Installing R and RStudio on Windows or Mac, Getting Started, Unix
Jan 14	Productivity Tools	Quarto, Git and GitHub	RStudio Projects, Quarto, Git
Jan 16	Data processing in R	R basics, Vectorization	R Basics, Vectorization
Jan 16	Data Processing in R	Tidyverse, ggplot2	dplyr, ggplot2
Jan 21	Data Processing in R	Tyding data	Reshaping Data
Jan 21, Jan 23	Wrangling	Intro, Data Importing, Dates and Times, Locales, Data APIs, Web scraping, Joining tables	Importing data, dates and times, Locales, Joining Tables, Extracting data from the web
Jan 28, Jan 30	Data visualization	Dataviz Principles, Distributions, Dataviz in practice	Distributions, Dataviz Principles
Feb 04	Probability	Intro, Foundations for Inference	Monte Carlo, Random Variables & CLT
Feb 06	Inference	Intro, Parameter and estimates, Confidence Intervals	Parameters & Estimates, Confidence Intervals
Feb 11, Feb 13	Statistical Models	Models, Bayes, Hierarchical Models	Data-driven Models, Bayesian Statistics, Hierarchical Models
Feb 18	Midterm 1		Covers material from Jan 09-Feb 13
Feb 20, Feb 25	Linear models	Intro, Regression	Regression, Multivariate Regression
Feb 27, Mar 11	Linear models	Multivariate regression, Treatment effect models	Measurement Error Models, Treatment Effect Models, Association Tests, Association Not Causation
Mar 13	High dimensional data	Intro to Linear Algebra, Matrices in R	Matrices in R, Applied Linear Algebra,
Mar 18	High dimensional data	Distance, Dimension reduction	Dimension Reduction
Mar 20	Machine Learning	Intro, Metrics, Conditionals, Smoothing	Notation and terminology, Evaluation Metrics, conditional probabilities, smoothing
Mar 25, Mar 27	Machine Learning	kNN, Resampling methods, caret package, Algorithms, ML in practice	Resampling methods, ML algorithms, ML in practice

Problem sets

Problem set	Topic	Due Date (11:59pm)	Difficulty
01	Unix, Quarto	Jan 19	easy
02	Data analysis with R	Jan 26	medium
03	Wrangling	Feb 2	medium
04	Dataviz	Feb 9	medium
05	Probability	Feb 23	medium
06	Predict the election	Mar 02	hard
07	Excess mortality after Hurricane María	Mar 16	medium
08	Matrices	March 25	easy

Office hour times

Meeting	Time	Location
Dylan Cable	Wed 2:00-3:00PM	SPH I 4635
Yize Hao	Fri 10:00-11:00AM	SPH II M4034

Acknowledgments

We thank Maria Tackett and Mine Çetinkaya-Rundel for sharing their web page template, which we used in creating this website. We thank Rafael Irizarry for sharing materials.