Graph Your Game: Strategic Playbooks Unveiled - Mapping Lacrosse Tactics
Explore strategic playbooks in lacrosse using graph theory and Golang. Learn to visualize player and team dynamics, implement knowledge graphs, and leverage CypherQL for data analysis.
Numbers and Art: Statistical Pitfalls and Their Consequences
Explore statistical pitfalls in data analysis, from Simpson's Paradox to hypothesis testing. Learn to critically evaluate models and metrics, understanding their real-world implications beyond simple accuracy measures.
Causal Inference in Python: From Theory to Practice
Explore causal inference in Python, moving beyond association to causation. Learn graphical causal models, do-calculus, and apply the causal data science pipeline to retail problems using DoWhy library.
Learning Gaussian DAGs from Network Data
Descubre técnicas avanzadas para aprender DAGs gaussianos a partir de datos de red con el Dr. Oscar Hernán Madrid Padilla de UCLA.
Introduction to Linear Algebra for Data Science
Explore core linear algebra concepts in data science, from vector representations to matrix factorization, with real-world applications in text similarity, image processing, and content recommendation.
Introduction to Causal Inference - Asking Your Data "Why?"
Explore causal relationships in data beyond correlation. Learn to ask "Why?" and unveil insights using simple techniques, avoiding misinterpretation pitfalls like Simpson's Paradox.
Advanced Causal Inference Made Simple
Explore automated causal inference techniques using auto-causality package. Learn to analyze A/B test results, measure impacts, and identify customer responses to different options.
Causality in a Post-Cookie World
Explore causal learning for data-driven marketing decisions in an era of privacy protection and unpredictable market conditions.
Causal Data Science in Retail - Understanding Supply Chain and Customer Behavior
Explore causal inference and Bayesian techniques in retail, focusing on supply chain, customer behavior, and promotion effects at M&S. Learn to answer "why" and "what-if" questions using machine learning.
Improving Insights by Utilizing Causal Inference Methods
Explore causal inference methods to de-bias data, approximate incremental improvements, and accelerate insights using propensity score matching and regression discontinuity.
Proving the Impact: Using Synthetic Control to Evaluate Intervention Effectiveness
Explore Synthetic Control methodology for measuring intervention impact when A/B testing isn't feasible. Learn to construct "synthetic" units, estimate causal effects, and apply this innovative approach to real-world scenarios.
Introduction to Statistics - Key Concepts and Applications
Learn fundamental statistical concepts, data types, and key terms. Explore data summarization, visualization techniques, and real-world applications in this comprehensive introduction to statistics.
Using Causal Inference to Increase a FinTech's Customer Acquisitions
Explore causal inference techniques to optimize customer acquisition strategies in FinTech, focusing on reducing disengagement during onboarding and improving contact timing.
Simultaneous Feature Selection and Outlier Detection Using Mixed-Integer Programming
Explore mixed-integer programming for simultaneous feature selection and outlier detection in high-dimensional regressions, with applications to biomedical research and microbiome studies.
Uncertainty Quantification, Software Ecosystems, and Infrastructure Resilience - Technical Talk
Explore uncertainty quantification, exascale software ecosystems, and infrastructure resilience in this technical talk by Sarah Osborn at WiDS Livermore 2024.
Progress in Graphs and Networks
Explore advancements in graph theory and network analysis, uncovering applications in various fields and enhancing problem-solving skills.
Topological Perspectives on Networks
Explore algebraic topology tools for network analysis, focusing on their application in neuroscience to understand structure-function relationships.
Topological Perspectives on Networks
Explore algebraic topology tools for network analysis, focusing on their application in neuroscience to understand structure-function relationships.
Bayesian Inference of Dependent Population Dynamics in Coalescent Models
Explore Bayesian inference techniques for analyzing dependent population dynamics in coalescent models, focusing on applications in genomics and evolutionary biology.
High-Quality Graph Visualization - Styling, Layout, and Strategies
Explore graph visualization techniques, styling, layout adjustments, and strategies with Jaebum Jung in this livestream overview from Wolfram's R&D team.
Recreating the Beresheet Lunar Crash - Wolfram Student Podcast S2 Ep1
Explore the recreation of the Beresheet Lunar Crash using telemetry data and rocket equations to model and predict landing locations in Mare Serenitatis.