
Explore causal inference in observational studies with Prof. Emma McCoy. Learn about robust estimation of treatment effects, time-series methodology, and applications in transport settings.

Explore statistical approaches to social network analysis, covering generative and statistical models, wealth effects, and block models. Gain insights into complex social structures and their implications.

Explore hidden confounds in statistical analysis, sensitivity analysis, and poison counts. Learn about Simpson's paradox and its implications for data interpretation.

Explore statistical approaches to analyzing social networks, including reciprocal ties, generalized giving, and household features. Learn techniques for modeling complex social structures.

Explore exponential family models, logistic regression, and Newton's method for classification tasks, with applications in app recommendation and sparse data analysis.

Explore methodologies for quantifying cyber risk, examining naive approaches, causal models, and challenges in data interpretation. Gain insights into effective risk assessment strategies.

Comprehensive introduction to Graph Neural Networks, covering fundamentals, mathematics, and practical implementation using NetworkX and PyG. Ideal for beginners seeking hands-on experience with GNNs.

Comprehensive introduction to data science, covering key concepts, tools, and methodologies. Ideal for beginners seeking to understand the field's fundamentals and career prospects.

Master essential SQL skills for data science: statistics, data prep, advanced filtering, window functions, and analytics tool integration.

Learn to model bird bath observations in Australia using tidymodels, combining model specifications with feature engineering recipes for effective fitting and prediction.

Explore random forest modeling with tidymodels to analyze water sources in Sierra Leone, covering data preparation, model building, and evaluation techniques.

Explore bootstrap resampling with tidymodels to analyze trends in CEO departures, using generalized linear models and #TidyTuesday data.

Explore multiclass evaluation metrics for random forest models using the Datasaurus Dozen dataset, focusing on model performance and visualization techniques.

Learn to predict survival in Himalayan climbing expeditions using tidymodels in R. Explore data handling, imputation, and modeling techniques for imbalanced datasets.

Explore Pandas for data manipulation: peek, sort, filter, and analyze with mathematical functions, correlation, and covariance.

Learn to analyze data using Pandas, covering data types, columns, plotting, correlation, and covariance in this comprehensive tutorial.

Learn essential Pandas techniques for data analysis, including data manipulation, correlation analysis, and visualization, to enhance your Python data science skills.

Explore fundamental concepts in data science, including probability, sets, Bayesian theory, correlation, and distribution, to build a strong foundation for advanced 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.

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.

Explore drug discovery using knowledge graphs, NLP, and Spark. Learn techniques for efficient data processing, entity recognition, and relationship extraction to enhance pharmaceutical research.