BE/Bi 103, Fall 2018: Homework 10

Due 1pm, December 14

(c) 2018 Justin Bois. With the exception of pasted graphics, where the source is noted, this work is licensed under a Creative Commons Attribution License CC-BY 4.0. All code contained herein is licensed under an MIT license.

This document was prepared at Caltech with financial support from the Donna and Benjamin M. Rosen Bioengineering Center.

This homework was generated from an Jupyter notebook. You can download the notebook here.


This homework is to be done individually, not in groups. Your responses are not to be put in your GitHub account so you can speak candidly without the influence of your teammates. You do not need to use a Jupyter notebook to do the problem. Instead, you should submit your responses either as a PDF or just as text in an email. Do not submit a MS Word document. Please email your responses to all of the following email addresses.

bois at caltech dot edu
jciemnie at caltech dot edu
semiller at caltech dot edu
csu at caltech dot edu
jwagner2 at caltech dot edu

A total of 30 points will be awarded for thoughtful responses for this homework. I am not expecting a paragraph for each question, but if you have detailed comments on something you have a strong or insightful opinion about, I would like to hear them.

Problem 10.1: Topical coverage

a) Are there topics you would like covered that were not?

b) Were there topics you think we spent too much time on?

c) Which topics did you find most interesting?

d) Which did you find most pertinent?

e) Where there any common misconceptions that persisted throughout the course for you? What topics (if any) did you find particularly hard to understand and apply?


Problem 10.2: Data sets and exercises

a) What was your favorite homework problem or data set? Which problem would you remove? Why?

b) Did you like that we kept revisiting data sets throughout the term? (We can always do less of that, but we can do even more of that. E.g., we could do image processing on the Singer, et al., mRNA FISH images.)


Problem 10.3: Course structure

a) Where did the majority of your learning take place? (Example answers include: Reading the tutorials, doing the tutorial exercises, completing the homeworks, interacting with TAs during the lab sessions, interacting with TAs during office hours, during JB's lectures, interacting with JB's during office hours, talking with teammates, etc.)

b) How would the class have been different for you if the work weren't done in teams?

c) Do you have any suggestions for improvement of the Monday sessions?

d) Did you like using GitHub as a platform for collaboration and submission?

e) What advice would you give to future students about using GitHub? Were there any common problems you frequently encountered?

f) Did you attend any recitation sessions? Did you find them useful? Do you have general suggestions for that?

g) Did you find the homework help sessions useful? Do you have general suggestions for that?

h) Do you have any comments on the lecture style or material?

i) What are your opinions on Piazza?


Problem 10.4: Your future

a) What will you be able to do with your data that you weren't able to before?

b) Has this class changed the way you read scientific papers? Has it changed your opinion on any papers you've read previously?

c) Which of the following principles do you think you will apply in your work going forward?

  1. Tidy data
  2. Bayesian inference for parameter estimation
  3. Bayesian model comparison/posterior predictive checks
  4. Hacker stats and bootstrapping for nonparametric statistics
  5. Bootstrap/permutation hypothesis tests
  6. Automated image processing

d) Which of the following software tools do you think you will use in your work going forward?

  1. Jupyter notebooks
  2. Git/GitHub
  3. Python-based tools in general (NumPy, SciPy, etc.)
  4. Pandas
  5. Stan
  6. Bokeh
  7. Altair


Problem 10.5: Miscellany

Please include any comments you have that I have not asked about.