Autumn School - New Data Horizon
Explore innovative data integration, ethics, and methodological challenges in social science research, from biological data to online surveys and genetic studies.
Restricted Boltzmann Machines
Explore Restricted Boltzmann Machines: their layered structure, hidden and visible layers, statistical independence, and applications in modeling high-dimensional data like images.
ICA - Independent Component Analysis
Explore Independent Component Analysis for blind source separation, covering cumulant-based methods to recover original signals from linear mixtures without prior knowledge of the mixing process.
Clustering
Discover data structure through clustering algorithms, learning to identify concentration regions and uncover hidden patterns in datasets.
ANOVA - Analysis of Variance
Learn statistical analysis techniques with Anova, covering one-way and two-way methods, calculations, notations, and test statistics for effective data interpretation.
Randomization-Based Hypothesis Testing
Learn randomization-based hypothesis testing in under an hour with Professor Christina Knudson.
Introductory Hypothesis Testing
Learn essential concepts and techniques for statistical hypothesis testing, including p-values, errors, and various test types for means and proportions.
Bootstrap Confidence Intervals
Learn to create and interpret bootstrap confidence intervals for various statistical measures, enhancing your data analysis skills.
Maximum Likelihood Estimation
Explore maximum likelihood estimation through examples of binomial, Poisson, and uniform distributions, enhancing statistical analysis skills.
Multiple Random Variables - Joint and Conditional Distributions
Explore joint and conditional distributions of multiple random variables, covering discrete and continuous cases, CDFs, independence, covariance, and variance of sums.
Linear Regression Essentials
Comprehensive overview of linear regression, covering simple and multiple regression, matrix notation, estimator properties, inference, and model evaluation metrics.
Statistics - Vol 3
Comprehensive exploration of sampling distributions, Central Limit Theorem, and confidence intervals. Covers statistical concepts for estimating population means and proportions using various sample sizes.
Random Variables
Explore continuous random variables, probability density functions, and the normal distribution. Learn to calculate expected values, variances, and use statistical tables for data analysis.
Bivariate Data Analysis
Explore scatter plots, correlation coefficients, and lines of best fit to analyze relationships between two variables in this comprehensive introduction to bivariate data analysis.
Basic Single Variable Analysis
Explore misleading graphs, means, medians, and statistical measures through engaging problems and visual representations.
Learn Statistics with Python
Master Python for statistical analysis: design matrices, linear models, F distribution, and OLS. Hands-on tutorials using Orange and Patsy libraries for practical data science skills.
Healthcare Statistics with SPSS
Learn to analyze healthcare data using SPSS, covering data creation, import, visualization, descriptive statistics, file manipulation, and parametric testing.
Julia
Learn Julia programming for data analysis, statistics, and linear algebra. Covers databases, plotting, environments, and advanced mathematical concepts with practical tutorials.
Web Scraping and Covid 19
Learn web scraping techniques to gather COVID-19 data, with practical examples and ethical considerations for responsible data collection and analysis.
Ordinal Logistic Regression
Learn to analyze ordered categorical data using ordinal logistic regression, covering introduction, multiple regression, and the proportional odds assumption.
Social Science Methods & Automated Data Algorithms
Explore cutting-edge data algorithms in social science, covering AI ethics, privacy implications, and innovative applications like deep learning for urban analysis.