# Statistics and Probabilistic Programming in Julia Training Course

## Overview

This instructor-led, live training (online or onsite) is aimed at people that already have a background in data science and statistics.

### Format of the Course

• Interactive lecture and discussion.
• Exercises and practice.

## Requirements

This course is intended for people that already have a background in data science and statistics.

## Statistics & Probabilistic Programming in Julia

### Basic statistics

• Statistics
• Summary Statistics with the statistics package
• Distributions & StatsBase package
• Univariate & multivariate
• Moments
• Probability functions
• Sampling and RNG
• Histograms
• Maximum likelihood estimation
• Product, trucation, and censored distribution
• Robust statistics
• Correlation & covariance

### DataFrames

(DataFrames package)

• Data I/O
• Creating Data Frames
• Data types, including categorical and missing data
• Sorting & joining
• Reshaping & pivoting data

### Hypothesis testing

(HypothesisTests package)

• Principle outline of hypothesis testing
• Chi-Squared test
• z-test and t-test
• F-test
• Fisher exact test
• ANOVA
• Tests for normality
• Kolmogorov-Smirnov test
• Hotelling’s T-test

### Regression & survival analysis

(GLM & Survival packages)

• Principle outline of linear regression and exponential family
• Linear regression
• Generalized linear models
• Logistic regression
• Poisson regression
• Gamma regression
• Other GLM models
• Survival analysis
• Events
• Kaplan-Meier
• Nelson-Aalen
• Cox Proportional Hazard

### Distances

(Distances package)

• What is a distance?
• Euclidean
• Cityblock
• Cosine
• Correlation
• Mahalanobis
• Hamming
• RMS
• Mean squared deviation

### Multivariate statistics

(MultivariateStats, Lasso, & Loess packages)

• Ridge regression
• Lasso regression
• Loess
• Linear discriminant analysis
• Principal Component Analysis (PCA)
• Linear PCA
• Kernel PCA
• Probabilistic PCA
• Independent CA
• Principal Component Regression (PCR)
• Factor Analysis
• Canonical Correlation Analysis
• Multidimensional scaling

### Clustering

(Clustering package)

• K-means
• K-medoids
• DBSCAN
• Hierarchical clustering
• Markov Cluster Algorithm
• Fuzzy C-means clustering

### Bayesian  Statistics & Probabilistic Programming

(Turing package)

• Markov Chain Model Carlo
• Hamiltonian Montel Carlo
• Gaussian Mixture Models
• Bayesian Linear Regression
• Bayesian Exponential Family Regression
• Bayesian Neural Networks
• Hidden Markov Models
• Particle Filtering
• Variational Inference