Statistics and Big Data Analytics

Understanding statistics is essential for working in the biomedical sciences. Below are some resources ranging from refresher courses to more advanced statistical topics.

Available resources

Since offerings are always evolving, this list may not be comprehensive. Still have questions?

Topic	Title	Sponsor	Fee	Credit
Big Data Analytics Course	AICD3 203: Fundamentals of Machine Learning	SOP	$	*
Big Data Analytics Course	AICD3 204: Computation and AI in Drug Discovery and Development	SOP	$	*
Big Data Analytics Course	AICD3 205: Modeling for Drug Development	SOP	$	*
Big Data Analytics Course	BIOE 245: Machine Learning in Medical Imaging	Grad Div	$	*
Big Data Analytics Course	BIOSTAT 202: Opportunities and Challenges of Complex Biomedical Data: Introduction to the Science of "Big Data"	Epi/Biostat	$	*
Big Data Analytics Course	BIOSTAT 216: Machine Learning in R for the Biomedical Sciences	Epi/Biostat	$	*
Big Data Analytics Course	BMI/BIOE 212: Deep Learning for Biological and Clinical Research	Grad Div	$	*
Big Data Analytics Course	DATASCI 223: Applied Data Science with Python	Epi/Biostat	$	*
Big Data Analytics Course	DATASCI 224: Understanding Machine Learning: From Theory to Applications	Epi/Biostat	$	*
Big Data Analytics Course	DATASCI 225: Advanced Machine Learning for the Biomedical Sciences II	Epi/Biostat	$	*
Big Data Analytics Course	DATASCI 226: Bayesian Methods and Gaussian Processes	Epi/Biostat	$	*
Big Data Analytics Course	Generative AI Foundations	Library	$	*
Big Data Analytics Course	Google AutoML Tools for Machine Learning	Library	—	—
Big Data Analytics Course	Introduction to Machine Learning	Library	—	—
Big Data Analytics Course	Machine Learning	Gladstone	—	—
Big Data Analytics Course	NLP and Document Classification	Library	—	—
Big Data Analytics Course	R for Machine Learning	Library	—	—
Consultation Services	Biostatistics Consultation	CTSI	$	—
Statistics Course	BIOSTAT 200: Biostatistical Methods for Clinical Research I	Epi/Biostat	$	*
Statistics Course	BIOSTAT 208: Biostatistical Methods for Clinical Research II	Epi/Biostat	$	*
Statistics Course	BIOSTAT 209: Biostatistical Methods for Clinical Research III	Epi/Biostat	$	*
Statistics Course	BIOSTAT 210: Biostatistical Methods for Clinical Research IV	Epi/Biostat	$	*
Statistics Course	BIOSTAT 211: Mathematical Foundations of Biostatistics	Epi/Biostat	$	*
Statistics Course	BIOSTAT 215: Advanced Approaches to the Analysis of Observational Data	Epi/Biostat	$	*
Statistics Course	EPI 213: Cost-Effectiveness Analysis in Medicine and Public Health	Epi/Biostat	$	*
Statistics Course	Hypothesis testing	Gladstone	—	—
Statistics Course	Introduction to Experimental Design and Hypothesis testing	Gladstone	—	—
Statistics Course	Linear Mixed Effects Modeling	Gladstone	—	—
Statistics Course	Power and Sample Size Calculations with R	Library	—	—
Statistics Course	R for Statistics	Library	—	—
Statistics Course	Regression Modeling with R	Library	—	—
Statistics Course	Statistics of Enrichment Analyses Methods	Gladstone	—	—
Statistics Course	Univariate and Bivariate Statistics with R	Library	—	—
Statistics Course	BIOSTAT 272: Foundations in Biostatistical Principles and Methods	Epi/Biostat	$	*
Statistics/Analytics Program	Advanced Training in Clinical Research (ATCR)	Epi/Biostat	$	*
Statistics/Analytics Program	Certificate in Health Data Science (CiHDaS)	Epi/Biostat	$	*
Statistics/Analytics Program	Doctoral Program in Epidemiology & Translational Science	Epi/Biostat	$	*
Statistics/Analytics Program	Master's Degree in Health Data Science (MiHDaS)	Epi/Biostat	$	*
Statistics/Analytics Program	PhD in Computational Precision Health	UCB/UCSF	$	*
Tutorials/User Support Services	NLP@UCSF Training and Support	BCHSI	—	—

See the full list of UCSF data science education and training sponsors and a key to the abbreviations.

Can’t find what you’re looking for?

If you are an educator or learner and can’t find a particular UCSF data science training opportunity on this portal, feel free to contact us.

Note that there are additional data science training opportunities that UCSF employees may access for free or at discounted rates which are outside the scope of this portal. For example, you may want to explore offerings through LinkedIn Learning (all content free through UCSF MyAccess)