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: email@example.com
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 firstname.lastname@example.org and we will help you.
For any questions, email the course admins at: email@example.com
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
- Flow control: FOR loops
Lecture 2 (Computation): Dictionaries and Conditionals
- Flow control: conditional statements
- Coding strategies
Lecture 3 (Computation): More lists, Reading Files, 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
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.
Lecture 8 (Computation): Numpy arrays
- Numpy arrays vs lists
Lecture 9 (Computation): Pandas, Bioinformatics
- Pandas dataframes
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.