Data Gymnasia

Sets and functions

Introduction Subsets Set Builder Notation Set Operations Lists Functions Function Properties Function Operations

Programmieren mit Python

Einführung Grundlagen Typen Bedingungen Funktionen Pakete Klassen Lists and Tuples Sets und Dictionaries Wiederholung (Schleifen)Projekt 1: Spotify Projekt 2: Seriendruck

Programming in Julia

Introduction Basics Types Conditionals Functions Packages Custom types Lists and Tuples Sets and Dictionaries Iteration Multidimensional Arrays Plotting

Numerical Computation

Machine Arithmetic Error Pseudorandom Number Generation Automatic Differentiation Optimization Parallel Computing

Multivariable Calculus

Introduction Taylor Series Differentiation Optimization Matrix differentiation The chain rule Multivariable integration Custom integration

Machine Learning

Introduction A regression example: linear models A classification example: QDA Likelihood Ratio Classification Generative Models Logistic Regression Support Vector Machines Decision Trees Ensemble Methods Neural Networks Dimension Reduction

Linear Algebra

Introduction Vectors Span Linear Independence Vector Spaces Linear Transformations Matrices Dot Products Transposes Orthogonality Eigenanalysis Singular Value Decomposition Determinants

Statistics

Introduction Estimating Joint Densities Point Estimation Confidence Intervals Empirical CDF Convergence Bootstrapping Maximum Likelihood Estimation Hypothesis Testing Causal Inference

Probability

Introduction Counting Probability Models Random Variables Probability Distributions Joint Distributions Conditional Probability Independence Expectation and Variance Covariance Continuous Distributions Conditional Expectation Common Distributions Central Limit Theorem

The Data Science Pipeline

Introduction Acquisition Wrangling Visualization Modeling Communication

Epidemic modeling

Introduction Stochastic SIR models Deterministic SIR models Lessons for the real world Links

Utilities

Introduction Unix Git LaTeX Conda Markdown Jupyter VS Code Make Docker

Data and Society

Introduction Bias in Word Embeddings

Bayesian Inference and Graphical Models

Introduction Markov Chain Monte Carlo Bayesian networks Expectation-Maximization Probabilistic Programming

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