Tutorials
We will have several computational tutorials throughout the course. As the course progresses, these materials will be posted below. Note: In some browsers (Firefox primarily), the end of each sentence is clipped off, making it difficult to read. If this is occurring, please try using another browser (Chrome, Safari, etc).
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Tutorial 0a: Configuring your computer | This tutorial will walk you through how to install a Python 3.7 scientific computing environment.
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Tutorial 0b: Using the Jupyter notebook | This tutorial will teach you how to write code and text in Jupyter notebooks for homework submissions.
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Tutorial 0c: A Primer on Python Syntax | This tutorial will walk you through the basics of programming in Python.
Data sets
- Data set 1 | Set of phase contrast and fluorescence images of a growing E. coli. colony.
Exercises
As we progress through the course, the code written in class will be posted here, along with the polished version of the same material.
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Exercise 1 | numerically integrating the differential equation for exponential growth.
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exercise 2 | determining bacterial growth rate from microscopy data. [data set]
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exercise 3 | simulating diffusing particles with coin flips.
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exercise 4 | continuation of our stochastic treatment of diffusion. –> <!– [in class]
External resources
Below is a list of useful online resources for learning the Python programming language and principles of programming in general.