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Frontmatter

2025 Course Outline

Unit 1: Basic Programing

1. Python
2. Statistical Variable Types
3. Linux
4. Scientific Python (NumPy, SciPy, & pandas)
5. Data Visualization
6. R

Homework Unit 1

Homework 1

Unit 2: Basic Statistics

7. Correlation Testing
8. Pearson r & Spearman \(\rho\)
9. Fisher Forecasting
10. Autocorrelation
11. Hypothesis Testing
12. Dimensionality Reduction (PCA)

Homework Unit 2

Homework 2
Homework 3

Unit 3: Model Fitting

13. Frequentist vs Bayesian
14. Linear Regression
16. \(\chi^2\), Maximum Likelihood and Minimizing
17. Resampling
18. Goodness of Fit
19. Model Comparison
20. emcee and Bayesian Software Tools
21. Bayesian Hierarchical Modeling
22. Bayesian Model Comparison

Homework Unit 3

Homework 4
Homework 5

Unit 4: Advanced Topics

23. Scikit Learn
25. Neural Networks
26. Deep Learning

References

References
Index

Repository
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By Benjamin Rose

© Copyright 2024-2025.

CC BY-NC 4.0