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:

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 and we will help you.

For any questions, email the course admins at:


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.


Lecture 0 (Computation): Introduction to Jupyter and Python

  • Course format
  • Introduction to Jupyter Notebook


Lecture 1 (Computation): Python Loops and Variables

  • Variables
  • Lists
  • Flow control: FOR loops

Lecture 2 (Computation): Dictionaries and Conditionals

  • Dictionaries
  • Flow control: conditional statements
  • Coding strategies


Lecture 3 (Computation): More lists, Reading Files, Functions

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


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



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

Lecture 7 (Computation): Compound data structures, Plots

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



Lecture 8 (Computation): Numpy arrays

  • Numpy arrays vs lists



Lecture 9 (Computation): Pandas, Bioinformatics

  • Pandas dataframes
  • IO



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

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

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

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

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

  • Chi squared tests


FINAL HOMEWORK DUE Tuesday, December 15.