Interactive data visualization with Plotly, ECharts, and D3
原文语言:英语
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Interactive data visualization with Plotly, ECharts, and D3
原文语言:英语
Guide to Metabase for open-source research data analytics and dashboards
原文语言:英语
Visualize networks, graphs, citation maps, and relational data
原文语言:英语
Guide to Plotly.py for interactive scientific visualizations in Python
原文语言:英语
Create journal-quality scientific figures with proper styling and accessibility
原文语言:英语
Publication-quality data visualization with matplotlib, seaborn, and plotly
原文语言:英语
Guide to Redash for SQL-driven research data dashboards and sharing
原文语言:英语
14 data visualization skills. Trigger: charts, plots, figures, publication-quality graphics. Design: one skill per tool with code templates and academic formatting conventions.
原文语言:英语
Causal inference methods including DiD, IV, RDD, and synthetic control
原文语言:英语
Apply EconML for causal inference combining machine learning and econometrics
原文语言:英语
Apply instrumental variables, 2SLS, and address endogeneity issues
原文语言:英语
Replication code and guide for Mostly Harmless Econometrics methods
原文语言:英语
Expert panel data regression analysis with fixed effects and GMM
原文语言:英语
Panel data analysis with fixed and random effects models
原文语言:英语
Learn causal inference with Python using the Brave and True handbook
原文语言:英语
Sequential robustness checks in Stata with confounder blocks
原文语言:英语
12 econometrics skills. Trigger: causal analysis, regression models, treatment effects, panel data. Design: method-centric guides with R/Python code and diagnostic tests.
原文语言:英语
STATA code for empirical accounting and financial economics research
原文语言:英语
Stata workflows for publication-ready sociology and social science research
原文语言:英语
Comprehensive Stata reference covering syntax, econometrics, and 20+ packages
原文语言:英语
Apply ARIMA, VAR, cointegration, and time series econometric methods
原文语言:英语
Bayesian inference methods including prior selection, MCMC, and model comparison
原文语言:英语
Detect anomalies and outliers in research data using statistical methods
原文语言:英语
Statistical hypothesis testing, power analysis, and significance reporting
原文语言:英语
Conduct systematic meta-analyses with effect size pooling and heterogeneity
原文语言:英语
Plan reproducible ML experiment runs with parameters and metrics tracking
原文语言:英语
Strategic statistical modeling, experimentation, and causal inference
原文语言:英语
Apply Mann-Whitney, Kruskal-Wallis, and other nonparametric methods
原文语言:英语
Sample size calculation and statistical power analysis guide
原文语言:英语
Structural equation modeling with latent variables guide
原文语言:英语
10 statistical analysis skills. Trigger: statistical tests, Bayesian analysis, hypothesis testing, sampling. Design: method guides covering assumptions, code, and result interpretation.
原文语言:英语
Conduct Kaplan-Meier, Cox regression, and time-to-event analyses
原文语言:英语
Load, explore, clean, and analyze CSV data with statistical summaries
原文语言:英语
Systematic data cleaning workflows for research datasets
原文语言:英语
Upload messy CSVs with minimal prompting for deep automated analysis
原文语言:英语
Diagnose missing data patterns and apply appropriate imputation strategies
原文语言:英语
Data cleaning, transformation, and exploratory analysis with pandas
原文语言:英语
Questionnaire and survey design with Likert scales and coding
原文语言:英语
10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.
原文语言:英语
Clean, transform, and validate messy research data using Stata
原文语言:英语