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Course Outline
Statistics & Probabilistic Programming in Julia
<\/h2>
Basic Statistics
<\/h3>
-
Statistics
-
Summary Statistics using the statistics package
<\/li><\/ul>
<\/li> -
Distributions & StatsBase package
-
Univariate & multivariate distributions
<\/li> -
Moments
<\/li> -
Probability functions
<\/li> -
Sampling and RNG
<\/li> -
Histograms
<\/li> -
Maximum likelihood estimation
<\/li> -
Product, truncation, and censored distributions
<\/li> -
Robust statistics
<\/li> -
Correlation & covariance
<\/li><\/ul>
<\/li><\/ul>
DataFrames
<\/h3>
(DataFrames package)
<\/p>
-
Data Input/Output
<\/li> -
Creating Data Frames
<\/li> -
Data types, including categorical and missing data
<\/li> -
Sorting & joining data
<\/li> -
Reshaping & pivoting data
<\/li><\/ul>
Hypothesis Testing
<\/h3>
(HypothesisTests package)
<\/p>
-
Principle outline of hypothesis testing
<\/li> -
Chi-Squared test
<\/li> -
z-test and t-test
<\/li> -
F-test
<\/li> -
Fisher's exact test
<\/li> -
ANOVA
<\/li> -
Tests for normality
<\/li> -
Kolmogorov-Smirnov test
<\/li> -
Hotelling's T-test
<\/li><\/ul>
Regression & Survival Analysis
<\/h3>
(GLM & Survival packages)
<\/p>
-
Principle outline of linear regression and exponential family
<\/li> -
Linear regression
<\/li> -
Generalized linear models
-
Logistic regression
<\/li> -
Poisson regression
<\/li> -
Gamma regression
<\/li> -
Other GLM models
<\/li><\/ul>
<\/li> -
Survival analysis
-
Events
<\/li> -
Kaplan-Meier estimator
<\/li> -
Nelson-Aalen estimator
<\/li> -
Cox Proportional Hazard model
<\/li><\/ul>
<\/li><\/ul>
Distances
<\/h3>
(Distances package)
<\/p>
-
Understanding distance metrics
<\/li> -
Euclidean distance
<\/li> -
Cityblock distance
<\/li> -
Cosine distance
<\/li> -
Correlation distance
<\/li> -
Mahalanobis distance
<\/li> -
Hamming distance
<\/li> -
MAD (Mean Absolute Deviation)
<\/li> -
RMS (Root Mean Square)
<\/li> -
Mean squared deviation
<\/li><\/ul>
Multivariate Statistics
<\/h3>
(MultivariateStats, Lasso, & Loess packages)
<\/p>
-
Ridge regression
<\/li> -
Lasso regression
<\/li> -
Loess smoothing
<\/li> -
Linear discriminant analysis
<\/li> -
Principal Component Analysis (PCA)
-
Linear PCA
<\/li> -
Kernel PCA
<\/li> -
Probabilistic PCA
<\/li> -
Independent Component Analysis (ICA)
<\/li><\/ul>
<\/li> -
Principal Component Regression (PCR)
<\/li> -
Factor Analysis
<\/li> -
Canonical Correlation Analysis
<\/li> -
Multidimensional scaling
<\/li><\/ul>
Clustering
<\/h3>
(Clustering package)
<\/p>
-
K-means clustering
<\/li> -
K-medoids clustering
<\/li> -
DBSCAN
<\/li> -
Hierarchical clustering
<\/li> -
Markov Cluster Algorithm
<\/li> -
Fuzzy C-means clustering
<\/li><\/ul>
Bayesian Statistics & Probabilistic Programming
<\/h3>
(Turing package)
<\/p>
-
Markov Chain Monte Carlo (MCMC)
<\/li> -
Hamiltonian Monte Carlo (HMC)
<\/li> -
Gaussian Mixture Models
<\/li> -
Bayesian Linear Regression
<\/li> -
Bayesian Exponential Family Regression
<\/li> -
Bayesian Neural Networks
<\/li> -
Hidden Markov Models
<\/li> -
Particle Filtering
<\/li> -
Variational Inference
<\/li><\/ul>
<\/h3>
-
Statistics
-
Summary Statistics using the statistics package
<\/li><\/ul> <\/li> -
Distributions & StatsBase package
-
Univariate & multivariate distributions
<\/li> -
Moments
<\/li> -
Probability functions
<\/li> -
Sampling and RNG
<\/li> -
Histograms
<\/li> -
Maximum likelihood estimation
<\/li> -
Product, truncation, and censored distributions
<\/li> -
Robust statistics
<\/li> -
Correlation & covariance
<\/li><\/ul> <\/li><\/ul>DataFrames
<\/h3>(DataFrames package)
<\/p>-
Data Input/Output
<\/li> -
Creating Data Frames
<\/li> -
Data types, including categorical and missing data
<\/li> -
Sorting & joining data
<\/li> -
Reshaping & pivoting data
<\/li><\/ul>Hypothesis Testing
<\/h3>(HypothesisTests package)
<\/p>-
Principle outline of hypothesis testing
<\/li> -
Chi-Squared test
<\/li> -
z-test and t-test
<\/li> -
F-test
<\/li> -
Fisher's exact test
<\/li> -
ANOVA
<\/li> -
Tests for normality
<\/li> -
Kolmogorov-Smirnov test
<\/li> -
Hotelling's T-test
<\/li><\/ul>Regression & Survival Analysis
<\/h3>(GLM & Survival packages)
<\/p>-
Principle outline of linear regression and exponential family
<\/li> -
Linear regression
<\/li> -
Generalized linear models
-
Logistic regression
<\/li> -
Poisson regression
<\/li> -
Gamma regression
<\/li> -
Other GLM models
<\/li><\/ul> <\/li> -
Survival analysis
-
Events
<\/li> -
Kaplan-Meier estimator
<\/li> -
Nelson-Aalen estimator
<\/li> -
Cox Proportional Hazard model
<\/li><\/ul> <\/li><\/ul>Distances
<\/h3>(Distances package)
<\/p>-
Understanding distance metrics
<\/li> -
Euclidean distance
<\/li> -
Cityblock distance
<\/li> -
Cosine distance
<\/li> -
Correlation distance
<\/li> -
Mahalanobis distance
<\/li> -
Hamming distance
<\/li> -
MAD (Mean Absolute Deviation)
<\/li> -
RMS (Root Mean Square)
<\/li> -
Mean squared deviation
<\/li><\/ul>Multivariate Statistics
<\/h3>(MultivariateStats, Lasso, & Loess packages)
<\/p>-
Ridge regression
<\/li> -
Lasso regression
<\/li> -
Loess smoothing
<\/li> -
Linear discriminant analysis
<\/li> -
Principal Component Analysis (PCA)
-
Linear PCA
<\/li> -
Kernel PCA
<\/li> -
Probabilistic PCA
<\/li> -
Independent Component Analysis (ICA)
<\/li><\/ul> <\/li> -
Principal Component Regression (PCR)
<\/li> -
Factor Analysis
<\/li> -
Canonical Correlation Analysis
<\/li> -
Multidimensional scaling
<\/li><\/ul>Clustering
<\/h3>(Clustering package)
<\/p>-
K-means clustering
<\/li> -
K-medoids clustering
<\/li> -
DBSCAN
<\/li> -
Hierarchical clustering
<\/li> -
Markov Cluster Algorithm
<\/li> -
Fuzzy C-means clustering
<\/li><\/ul>Bayesian Statistics & Probabilistic Programming
<\/h3>(Turing package)
<\/p>-
Markov Chain Monte Carlo (MCMC)
<\/li> -
Hamiltonian Monte Carlo (HMC)
<\/li> -
Gaussian Mixture Models
<\/li> -
Bayesian Linear Regression
<\/li> -
Bayesian Exponential Family Regression
<\/li> -
Bayesian Neural Networks
<\/li> -
Hidden Markov Models
<\/li> -
Particle Filtering
<\/li> -
Variational Inference
<\/li><\/ul>
-
Markov Chain Monte Carlo (MCMC)
-
K-means clustering
-
Linear PCA
-
Ridge regression
-
Understanding distance metrics
-
Events
-
Logistic regression
-
Principle outline of linear regression and exponential family
-
Principle outline of hypothesis testing
-
Data Input/Output
-
Univariate & multivariate distributions
-
Summary Statistics using the statistics package
Requirements
This course is intended for individuals who already have a background in data science and statistics.
<\/p>
21 Hours
Testimonials (3)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
The real life applications using Statcan and CER as examples.
