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Course Outline
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
Requirements
This course is intended for people that already have a background in data science and statistics.
21 Hours
Testimonials (5)
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
the trainer had patience, and was eager to make sure we all understood the topics, the classes were fun to attend
Mamonyane Taoana - Road Safety Department
Course - Statistical Analysis using SPSS
Day 1 and Day 2 were really straight forward for me and really enjoyed that experience.
Mareca Sithole - Africa Health Research Institute
Course - R Fundamentals
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
