Data Science

What it does

This skill enables AI agents to perform data science tasks, including statistical modeling, experimentation, and advanced analytics. It facilitates designing A/B tests, building predictive models, performing causal analysis, feature engineering, and conducting statistical hypothesis testing using various tools and techniques. The skill provides code snippets for calculating sample sizes for A/B tests and analyzing test results.

When to use it

• Designing A/B tests and experiments
• Building predictive models
• Performing causal analysis
• Feature engineering
• Statistical hypothesis testing

Key capabilities

• A/B Test Framework: Includes functions for calculating required sample sizes and analyzing test results.
• Statistical Significance Analysis: Provides methods to determine statistical significance using z-tests.
• Feature Engineering: Offers a pipeline for creating new features from data, including temporal features like hour of day and day of week.
• Tool Support: Utilizes Python, SQL, R, NumPy, Pandas, SciPy, Scikit-learn, XGBoost, LightGBM, Matplotlib, Seaborn, Plotly, Statsmodels, PyMC, Jupyter Notebooks, and VS Code.

Example prompts

• "Calculate the required sample size for an A/B test with a baseline conversion rate of 5% and a minimum detectable effect of 0.5%."
• "Analyze the results of an A/B test where the control group had a conversion rate of 10% and the treatment group had a conversion rate of 12%."
• "Create new features in this dataset, including hour of day and day of week from the timestamp column."

Tips & gotchas

• This skill provides code snippets; you may need to adapt them to your specific data and context.
• The provided functions assume a basic understanding of statistical concepts like p-values and effect sizes.
• Familiarity with Python, Pandas, and related libraries will be helpful for effectively using this skill.