Tutorials marked with an asterisk are available to Caltech personnel only. This is because they contain unpublished information that the researchers who generously supplied the data would prefer be shared in their own publication. Apologies to non-Caltech people who may otherwise find these tutorials useful.
Similarly, many of the data sets are also only available to Caltech personnel. This is because many of them are unpublished and/or being used for ongoing research.
- Tutorial 0a
- [Before first class] Setting up a Python environment for scientific computing
- Tutorial 0b
- [Before first homework] Introduction to Jupyter notebooks
- Tutorial 0c
- [Before first homework] Introduction to LaTeX
- Tutorial 0d
- [Before first homework] A sample homework problem and solution
- Tutorial 1a
- [09/28] Introduction to Python
- Tutorial 1b
- [09/28] Exploratory data analysis (data set)
- Tutorial 2a
- [10/05] Managing data sets (data set)
- Tutorial 2b
- [10/05] Defining parameters and estimating them (same data set as Tutorial 2a)
- Tutorial 3a
- [10/12] Regression (data set)
- Tutorial 3b
- [10/12] Boolean data (data set)
- Tutorial 4a
- [10/19] Maximum likelihood estimation (data set)
- Tutorial 4b
- [10/19] Parameter estimation with MCMC
- Tutorial 5a
- [10/26] Model selection I (data set 1 / data set 2)
- Tutorial 5b
- [10/26] Outlier detection and correction (data set)
- Tutorial 6a
- [11/02] Frequentist parameter estimation (data set 1 / data set2)
- Tutorial 6b
- [11/02] Frequentist hypothesis testing
- Tutorial 7a
- [11/09] Time series and data smoothing (data set)
- Tutorial 7b
- [11/09] Model selection II (data set)
- Tutorial 8*
- [11/16] Extracting information from images (data set sent via Dropbox)
- Tutorial 9a*
- [11/23] Basic filtering and thresholding
- Tutorial 9b*
- [11/23] Segmentation
- Tutorial 10
- [11/30] Colocalization (data set)
Auxilliary tutorials
Throughout the term, we will post auxiliary tutorials here. These are tutorials we will cover in recitations, but not in the Monday sessions.
- Rec 1
- (Th 10/08) Interactive data analysis with Bokeh (JB)
- Rec 2
- (Th 10/15) Review of concepts in probability (MR)
- Rec 3
- (Th 10/22) Kernel density estimation (MM)
- Rec 4*
- (Th 10/29) LASSO and ridge regression (JB) (data set)
- Rec 5
- (Th 11/05) Principle component analysis (MR) (data set)
- Rec 6
- (Th 11/12) Introduction to SymPy (MR)
- Rec 7
- (Th 11/19) Intro to PyMC3 (JB)
- Rec 8
- (Th 12/03) Advanced segmentation: watershed algorithms and cell lineage tracking (GC)
- Rec 9
- (Th 12/10) Introduction to R (Axel Müller)