Introductory Statistics
Comprehensive introduction to statistics using Excel, covering data analysis, probability, distributions, hypothesis testing, and regression with practical exercises and real-world applications.
Stats Examples
Comprehensive statistical training covering key concepts, probability, distributions, hypothesis testing, and advanced analyses with step-by-step examples and practical applications.
The Statistical Process
Explore key statistical concepts including data types, sampling methods, survey design, inference, significance, and measures of central tendency and spread.
Embedded Linux Quality Assurance: How to Not Lie with Statistics
Explore statistical quality assurance for embedded Linux systems, covering best practices, automated testing, and data evaluation using R. Learn to avoid common pitfalls in interpreting results.
Identification and Estimation of Dynamic Structural Models with Unobserved Choices
Explore dynamic structural models with unobserved choices, focusing on identification, estimation, and applications in information design and data science.
StocSum: A Reference Panel Free Summary Statistics Framework for Diverse Populations
Explore StocSum, a novel framework for analyzing summary statistics in diverse populations without reference panels. Learn its applications in genomics research and meta-analysis.
Learning Pandas - Start Here
Comprehensive overview of Pandas functionality, covering data I/O, DataFrame operations, filtering, statistics, grouping, and data manipulation techniques for efficient data wrangling and analysis.
Data Morph: A Cautionary Tale of Summary Statistics
Explore Data Morph, an open-source tool challenging summary statistics assumptions. Learn to uncover hidden truths in datasets through innovative visualization techniques and reshape your approach to data analysis.
Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 3
Explorez les dynamiques d'optimisation en très haute dimension et leur application en apprentissage automatique, en mettant l'accent sur les matrices aléatoires et les statistiques résumées.
Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 4
Explore random matrices and optimization dynamics in high dimensions, focusing on effective dynamics, spectral transitions, and applications in machine learning algorithms.
Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 2
Explorez les dynamiques d'optimisation en très haute dimension et leur application en apprentissage automatique, en mettant l'accent sur les matrices aléatoires et les statistiques résumées.
Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 1
Explorez les algorithmes d'optimisation en haute dimension pour l'apprentissage automatique, en mettant l'accent sur la descente de gradient stochastique et les transitions spectrales des matrices aléatoires.