Lessons

0. Setting up your computer
1. Intro to Python I
2. Intro to Python II
3. Exploratory data analysis
4. Data wrangling
5. Intro to image processing
6. Probability distributions
7. Nonparametric inference with hacker stats
8. Maximum likelihood estimation
9. Model assessment

Lecture notes

1. Cycle of science/Version control
2. Style and TDD
3. Data storage and sharing
4. Reproducible workflows
5. Intro to images
6. Intro to probability
7. Nonparameteric inference
8. Parametric inference
9. The paper of the future
10. Statistical watchouts

Homework

1. Practice with Python
2. File I/O and validation
3. Exploratory data analysis I
4. Exploratory data analysis II
5. Image processing
6. Random number generation and time series
7. Nonparametric hacker stats
8. Maximum likelihood estimation
9. Model comparison
10. Course feedback

Recitations

1. The command line
2. Using Git and GitHub
3. Package management
4. Reshaping data
5. Overplotting and dashboards
6. Time series and data smoothing
7. Colocalization
8. Bootstrapping
9. Pairs, residual, and wild bootstrap

BE/Bi 103 a

BE/Bi 103 a main page
Download notebook

Lesson 0b: Introduction to JupyterLab¶

[1]:

import itertools

import numpy as np
import scipy.integrate

import holoviews as hv
hv.extension('bokeh')