Airline Passenger Data
Trend, evolving seasonality and long-term growth
Automated data analysis, interpretable models and human-readable statistical reports for the modern data stack.
Explore how machines can discover structure in data, compare models, explain patterns and turn complex analysis into understandable reports.
Inspect structure, missingness, distributions and relationships.
Search across candidate statistical models and structural explanations.
Compare fit, complexity, predictive performance and uncertainty.
Turn discovered patterns into charts, predictions and readable descriptions.
Trend, evolving seasonality and long-term growth
Long-term structure, variation and periodic behaviour
Structural change, trends and uncertainty over time
Discover patterns and choose appropriate analytical methods automatically.
Automated model selection, tuning and evaluation.
Make machine-generated conclusions understandable and auditable.
Probabilistic modelling with interpretable uncertainty.
Detect trends, seasonality, change points and anomalies.
Modern AI systems that help analysts explore and understand data.
The original Automatic Statistician research investigated whether parts of statistical modelling could be automated without giving up interpretability. Rather than fitting a single fixed model, the system searched a compositional space of Gaussian process kernels, building structure out of smooth trends, periodic components, changepoints and noise.
Candidate models were compared using Bayesian reasoning, which balances fit against complexity. The discovered structure was then translated into natural-language descriptions and charts, together with model criticism that flagged where the model disagreed with the data. The result was a report a statistician could read, check and argue with.
Explore the ResearchUse pivot_table with an explicit aggregation function when repeated row-key and column-key pairs represent legitimate repeated measurements.
Use melt with explicit identifier and measurement columns to turn each selected wide cell into one long-format row.
Use validate="many_to_one" in a left merge to require unique join keys in the right-hand lookup table.
Pass output_name=(input_column, aggregation) pairs to DataFrameGroupBy.agg to create explicitly named summary columns.
Top 10 US data science programmes by career outcomes: MIT Sloan, CMU ML, UC Berkeley MIMS, UChicago Analytics, Columbia DS. Best ROI: Georgia Tech OMSA (~$10.7K total), UIUC MSc DS on Coursera (~$21K). Best for ML research: CMU, MIT, Stanford, Berkeley. Employer relationships and career services drive outcomes more than rankings.
Capture an isna mask before filling values, then store it as a Boolean or zero-one indicator beside the original column.
Reviews and comparisons of the platforms that automate parts of the analytical workflow, evaluated against the methodology we publish.
Explore Data Science Tools