# Notebooks

Links to Jupyter Notebooks rendered in HTML and available through nbviewer to review key concepts. I don’t work in Julia every day so I find it useful to keep a record of basic syntax until this becomes muscle memory.

## Python

- Monte Carlo is Easy and Free in Python [ipynb] [html]
- Waterfall Charts in Plotly - Useful for Financial Planning and Analysis (FP&A) folks [ipynb] [html]
- Simple Examples of Bayesian Networks with Python
`pgmpy`

[ipynb] [html] - Using
`pandas`

to create Fiscal Calendars and 52/53 (aka 4-4-5) Lookup Calendars in Python [ipynb] [html] - Time Series forecasting cheatsheet with the scikit-time (
`sktime`

) library [ipynb] [html] - Bayesian Decision Modeling with pymc4 (Notes from Ravin Kumar’s PyData Global 2020 talk) [ipynb] [html]
- Ravin Kumar’s original talk [Youtube] [Presentation]

- An intro to Lasso, Ridge, and ElasticNet in
`sklearn`

(bonus: support vector regression) [ipynb] [html] - A bare-bones intro to the
`statsmodels`

API with VAR, AR, and linear regression [ipynb] [html] - Auto ARIMA and ARIMAX/SARIMAX with
`pmdarima`

[html]

## Learning causal impact

- WIP collection of notebooks around probabilistic programming with
`numpyro`

, forecasting, and causal inference [Github]

## Julia

- SQL to Julia Translation for basic sorting, filtering, and aggregating of data [ipynb][html]
- Linear Regression with
`GLM`

[ipynb] [html] - A Julia Project Workflow, i.e. setting up a new environment and project scaffolding [ipynb] [html]
- Random Sampling from Distributions in Julia [ipynb] [html]
- First Impressions of Data Visualization with
`Makie`

and`AlgebraOfGraphics`

[ipynb] [html] - Animations in Julia with
`Plots.jl`

[ipynb] [html] - Animations in Julia with
`Makie.jl`

[ipynb] [html] - Cyberpunk theme for Julia plots with
`Makie.jl`

[ipynb] [html] - Exploring
`MLJ`

, a wrapper for lots of machine learning libraries for Julia, similar to python’s`scikit-learn`

[ipynb] [html] - Example analysis workflow using TidyTuesday data using
`GadFly`

,`DataFramesMeta`

, and`DuckDB`

[ipynb] [html] - Combining Optimization with
`JuMP`

and Bayesian Decision Making with`Turing`

[ipynb] [html]