Statistical Rethinking 2023 - Counts & Hidden Confounds
Explore hidden confounds in statistical analysis, sensitivity analysis, and poison counts. Learn about Simpson's paradox and its implications for data interpretation.
Statistical Rethinking - Social Networks
Explore statistical approaches to analyzing social networks, including reciprocal ties, generalized giving, and household features. Learn techniques for modeling complex social structures.
Logistic Regression and Generalized Linear Models
Explore exponential family models, logistic regression, and Newton's method for classification tasks, with applications in app recommendation and sparse data analysis.
SoK- Quantifying Cyber Risk
Explore methodologies for quantifying cyber risk, examining naive approaches, causal models, and challenges in data interpretation. Gain insights into effective risk assessment strategies.
Learn Data Science Full Course - 2020
Comprehensive introduction to data science, covering key concepts, tools, and methodologies. Ideal for beginners seeking to understand the field's fundamentals and career prospects.
Advanced SQL for Data Scientists
Master essential SQL skills for data science: statistics, data prep, advanced filtering, window functions, and analytics tool integration.
Fit and Predict With Logistic Regression for Bird Bath Observations in Australia
Learn to model bird bath observations in Australia using tidymodels, combining model specifications with feature engineering recipes for effective fitting and prediction.
Predict Water Availability in Sierra Leone With Random Forests
Explore random forest modeling with tidymodels to analyze water sources in Sierra Leone, covering data preparation, model building, and evaluation techniques.
Estimate Change in CEO Departures With Bootstrap Resampling
Explore bootstrap resampling with tidymodels to analyze trends in CEO departures, using generalized linear models and #TidyTuesday data.
Predict Class Membership for the Datasaurus Dozen With Tidymodels
Explore multiclass evaluation metrics for random forest models using the Datasaurus Dozen dataset, focusing on model performance and visualization techniques.
Impute Missing Data and Handle Class Imbalance for Himalayan Climbing Expeditions
Learn to predict survival in Himalayan climbing expeditions using tidymodels in R. Explore data handling, imputation, and modeling techniques for imbalanced datasets.
Data Science: Introduction to Pandas
Explore Pandas for data manipulation: peek, sort, filter, and analyze with mathematical functions, correlation, and covariance.
Data Science Tutorial 1 - Pandas Analysis
Learn to analyze data using Pandas, covering data types, columns, plotting, correlation, and covariance in this comprehensive tutorial.
Data Analysis with Pandas
Learn essential Pandas techniques for data analysis, including data manipulation, correlation analysis, and visualization, to enhance your Python data science skills.
Data & Cyber - Introduction to Data Science
Explore fundamental concepts in data science, including probability, sets, Bayesian theory, correlation, and distribution, to build a strong foundation for advanced analysis.
The Signal and the Noise - Lessons from 20 Years of Data Analysis
Nate Silver shares insights from 20 years of data analysis, focusing on election modeling, forecasting challenges, and lessons learned in interpreting complex data in competitive environments.
How to Build a Cloud Data Platform - Machine Learning and Business Intelligence - Part 4
Learn to leverage Delta Lake for dashboards and advanced analytics, enabling downstream teams with machine learning and business intelligence capabilities on a unified data platform.
Time Series Analysis via Matrix Estimation
Explore matrix estimation techniques for time series analysis, focusing on imputation, algorithms, and theoretical foundations with practical applications in societal networks.
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.
ACM-IMS Interdisciplinary Summit on the Foundations of Data Science
Explore cutting-edge data science topics with leading experts, including deep learning, reinforcement learning, robustness, fairness, and ethics, through keynote talks and panel discussions.
Efficiently Learning Structured Distributions from Untrusted Batches
Explore robust learning techniques for structured distributions using untrusted batches, covering information-theoretic bounds, L1 mean estimation, and SOS relaxation methods.