2025 Course Outline

	Topics	Reference books
	Unit 1
Week 1	Python & Linux	Downey, Vasiliev Ch 3 is limited, Healy 3.2
Week 2	NumPy, SciPy, Pandas, & tidydata
Week 3	Data visualization (matplotlib, R, gnuplot)	Wilke 2, 5, 4 & 19, Healy Ch 1, 3, and maybe 4
	Unit 2
Week 4	Correlation Testing	Wall 4
Week 5	Statistical/Systematic Errors & Fisher forecasting	Wall 3.3 & ??
Week 6	Autocorrelation & Hypothesis testing	?? & Wall 5
Week 7	Covariance & Dimensionality Reduction, SVD & PCA	Wall 4.5
	Unit 3
Week 8	Frequentist vs Bayesian statistics	VanderPlas, Wall 6
Week 9	Model fitting, linear regression & MCMC (statsmodels & emcee)	Wall 6.1 & 6.2, statsmodel docs, Kelly, emcce docs
Week 10	Model validation via simulations, bootstrap & Jackknife errors	Wall 6.6, Gelman 8, maybe 16–19
Week 11	Model selection	Wall 7
	Unit 4
Week 12	BHM, multilevel regression, missing data (pymc)	Kelly, Gelman 11-15, pymc docs
Week 15	2pt correlation functions	Wall 10, ?
Week 14	Machine Learning: Unsupervised learning, neural networks, Gaussian Processes	??, Vasiliev ch12
Week 13	Time Series data analysis, FFT	Wall 9, Robinson, Vasiliev ch 10

https://www.astroml.org/astroML-notebooks/ https://idl.uw.edu/visualization-curriculum/intro.html

References

• Downey “Think Python” 2nd edition

• Gelman & Hill “Data Analysis Using Regression and Multilevel/Hierarchical Models”

• Healy “Data Visualization”

• Kelly “Some Aspects of Measurement Error in Linear Regression of Astronomical Data”

• Robinson “Data Analysis for Scientists and Engineers”

• VanderPlas “Frequentism and Bayesianism: A Python-driven Primer”

• Vasiliev “Python for Data Science”

• Wall & Jenkins “Practical Statistics for Astronomers” 2nd edition

• Wilke “Fundamentals of Data Visualization”