Afterwards you can describe two samples in Julia, test whether they differ with HypothesisTests.jl, and report the effect size with a confidence interval.
Afterwards you can read a table into a Julia DataFrame, select by column and condition, handle missing values deliberately, and average by hour or group.
Afterwards you can fit a scikit-learn classifier, score it on a held-out test set, tune it by cross-validation, and chain a scaler and model in one pipeline.
Afterwards you can say what a Fourier transform computes, read an amplitude spectrum, and tell how long a record you need to separate two nearby frequencies.
Afterwards you can fit a model to data with known uncertainties using LsqFit, report parameter errors you can defend, and shade a confidence band.
Afterwards you can read a table into a pandas DataFrame, select by label and condition, handle missing values deliberately, and average by hour or group.
Afterwards you can build a Matplotlib figure from Figure and Axes, size it for a journal column, share an axis between panels, and save it at print size.
Afterwards you can seed a generator with default_rng, draw whole arrays from common distributions at once, and spawn independent, reproducible streams.
Afterwards you can integrate data and formulas with scipy.integrate, report a peak area with its uncertainty, and handle singular points and infinite limits.
Afterwards you can describe two samples with scipy.stats, test whether they differ, and report the effect size with a confidence interval, not a bare p-value.
Afterwards you can integrate a two-variable ODE system with solve_ivp, find its fixed points, and draw its phase portrait over a direction field.
Afterwards you can say what a Fourier transform computes, read an amplitude spectrum, and tell how long a record you need to separate two nearby frequencies.
Afterwards you can fit a model to data with known uncertainties, report parameter errors you can defend, and shade a confidence band around the fitted curve.