Python for Astronomers

Introduction to different python versions, jupyter, google colab

Installing/updating packages

Absolute basics of python (syntax, modules, data types)

Loading and writing files

Plotting

Functions, lambda function

Tables, Pandas data structures and data analysis

Advanced data formats (pickle, hdf5, fits)

Units, constants, uncertainties

Linear algebra (integration, equation systems)

Data manipulation 1 (interpolation, filtering (smoothing), bad values)

Data manipulation 2 (binning, sigma clipping, de-trending)

Optimization (fitting, minimizing functions, brute force, bootstrap)

Time series analysis (Fourier-, Lomb-Scargle-, time-frequency analysis)

Ground- and space-based photometry

Bayesian inference with Markov Chain Monte Carlo

Introduction to Gaussian Process regression

Parallel execution

Introduction to Machine Learning 1 (classification: K-Nearest Neighbors, Decision Trees, Naive Bayes, Support Vector Machines, Logistic Regression)

Introduction to Machine Learning 2 (neural networks, deep learning and other magic words; scikit-learn, tensorflow, keras)

Version-control: git, github