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.
Testimonials (4)
the clarity with which he explained the entire course, as well as the willingness to return to the syllabus when necessary
Carlos Eloy - AMERICAN EXPRESS COMPANY MEXICO
Course - Data Analytics With R
Machine Translated
I liked that the trainer made sure we all understood and were following the lectures. if we had a problem, he stopped and helped us fix it.
Cesar - AMERICAN EXPRESS COMPANY MEXICO
Course - Data Analytics With R
I really was benefit from the real life practical examples.
Wioleta
Course - Data and Analytics - from the ground up
The flexible and friendly style. Learning exactly what was useful and relevant for me.