Syllabus

Bio 5075 Fall 2020 Syllabus

Live Zoom sessions are Mondays from 2:30 to 4:00 pm. Zoom link is available from the course site in Canvas or from the instructors upon request.

 

Office hours are by appointment. To schedule a time to meet, email the TAs and instructors at: bio5075-admin@lists.genetics.wustl.edu

Instructors: Zach Pincus and Mike White

Teaching Assistants: Aidan Schneider, Yawei Wu

 

IMPORTANT: Before the first class, ensure that you have a working installation of Jupyter notebook. Follow these instructions.

If you have trouble completing the installation instructions, contact one of the instructors at bio5075-admin@lists.genetics.wustl.edu and we will help you.

For any questions, email the course admins at: bio5075-admin@lists.genetics.wustl.edu

HOMEWORK:

Weekly homework is due by the end of the day on Wednesdays. Late homework will penalized 5% and any homework not submitted within two weeks of the due date will be given no credit.

SCHEDULE:

2020-09-14
Lecture 0 (Computation): Introduction to Jupyter and Python

  • Course format
  • Introduction to Jupyter Notebook

 

2020-09-21
Lecture 1 (Computation): Python Loops and Variables

  • Variables
  • Lists
  • Flow control: FOR loops

2020-09-28
Lecture 2 (Computation): Dictionaries and Conditionals

  • Dictionaries
  • Flow control: conditional statements
  • Coding strategies

 

2020-10-05
Lecture 3 (Computation): More lists, Reading Files, Functions

  • Functions
  • File input/output
  • List comprehensions and manipulations

2020-10-12

Lecture 4 (Statistics): Summarizing Numbers

  • Single number summaries: mean, median, mode
  • Two numbers: variance and standard deviation
  • Dot plots and histograms
  • Distributions

2020-10-19 Lecture 5 (Statistics): Basic Probability

  • Intuitive probability estimation from histograms
  • Basic theory and notation
  • How probabilities combine: “and” and “or”
  • Independence and conditional probability
  • Counting successes and failures

 

2020-10-26

Lecture 6 (Statistics): Simulation and Hypothesis Testing (I)

  • Why simulate?
  • Hypothesis testing and the null distribution
  • What p-values are and are not
  • Recent controversies in the use of p-values

2020-11-02
Lecture 7 (Computation): Compound data structures, Plots

  • Lists of lists, dictionaries of lists, etc.
  • Matplotlib

 

2020-11-09

Lecture 8 (Computation): Numpy arrays

  • Numpy arrays vs lists

 

2020-11-16

Lecture 9 (Computation): Pandas, Bioinformatics

  • Pandas dataframes
  • IO

 

2020-11-23

Lecture 10 (Statistics): Simulation and Hypothesis Testing (II)

  • Permutation testing
  • Sampling from a population
  • Bootstrap confidence intervals
  • Bootstrap hypothesis testing

2020-11-30
Lecture 11 (Statistics): Power Analysis, Experimental Design, and Parametric Statistics I

  • Statistical Power
  • Paired tests
  • The standard error and the t-test
  • ANOVA

2020-12-07
Lecture 12 (Statistics): Power Analysis, Experimental Design, and Parametric Statistics II

  • Chi squared tests

 

FINAL HOMEWORK DUE Tuesday, December 15.