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Course Details

Spring 2016-2017
QCB 508   Graded A-F, P/D/F, Audit

Foundations of Applied Statistics and Data Science (with Applications in Biology)

John D. Storey

This course establishes a foundation in applied statistics and data science for those interested in pursuing data-driven research. The course may involve examples from any area of science, but it places a special emphasis on modern biological problems and data sets. Topics may include data wrangling, exploration and visualization, statistical programming, likelihood based inference, Bayesian inference, bootstrap, EM algorithm, regularization, statistical modeling, principal components analysis, multiple hypothesis testing, and causality. The statistical programming language R is extensively used to explore methods and analyze data.

Sample reading list:
Wasserman, All of Statistics
Hastie, Tibshirani, and Friedman, Elements of Statistical Learning
Grolemund and Wickham, R for Data Science
Casella and Berger, Statistical Inference
See instructor for complete list

Reading/Writing assignments:
There will be problem sets due over the course of the semester. There will be primary and supplementary reading assigned by the instructor.

Requirements/Grading:
Take Home Final Exam - 30%
Class/Precept Participation - 10%
Problem set(s) - 60%

Prerequisites and Restrictions:
A course in basic statistics or machine learning (e.g., ORF 245, ORF 305, COS 424) or a course in quantitative biology (e.g., NEU 314, QCB 455). A working knowledge of basic genetics and molecular biology. A working knowledge of calculus, linear algebra, and computer programming..

Reserved Seats:
Open to Graduate Students Only 25

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
43605 L01 11:00:00 am - 12:20:00 pm T Th   Lewis Library   120   Enrolled:15 Limit:30