Statistics and Probabilistic Programming in Julia

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

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.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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
• MAD
• 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