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Since 2014 · Automated statistical modelling

Artificial intelligence for data science.

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.

Gaussian processes Bayesian model comparison Model criticism
Airline Passenger Analysis MODEL 04 · BIC −1284
Observations · fit · forecast
FORECAST
OBSERVED TREND FORECAST · 95% CI
COMPONENT 1
Long-term trend
COMPONENT 2
Annual periodic structure
COMPONENT 3
Increasing seasonal amplitude
COMPONENT 4
Residual variation
TREND DETECTED
+2.7% / year
PERIODIC COMPONENT
12 month cycle
CHANGE POINT
Detected: 2008
MODEL CONFIDENCE · HIGH
The method

From raw data to an understandable model

DATAMODEL DISCOVERYEVALUATIONEXPLANATIONREPORT
01

Explore

Inspect structure, missingness, distributions and relationships.

02

Discover

Search across candidate statistical models and structural explanations.

03

Evaluate

Compare fit, complexity, predictive performance and uncertainty.

04

Explain

Turn discovered patterns into charts, predictions and readable descriptions.

Example reports

See what automated analysis can uncover

All examples →
Origins

The idea behind the Automatic Statistician

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 Research
DISCOVERED KERNEL STRUCTURE
SE × PER
+ SE × LIN
+ CP(2008) × SE
+ WN
SE — smooth trend
PER — periodicity
LIN — linear growth
CP — changepoint
WN — noise
BIC — model score
Guides

Learn automated data analysis

Browse All Guides →
Automated Data Analysis · 4 MIN

How to pivot a table with duplicate key pairs in Pandas (with Code)

Use pivot_table with an explicit aggregation function when repeated row-key and column-key pairs represent legitimate repeated measurements.

Automated Data Analysis · 5 MIN

How to reshape wide data to long format in Pandas (with Code)

Use melt with explicit identifier and measurement columns to turn each selected wide cell into one long-format row.

Automated Data Analysis · 5 MIN

How to validate a many-to-one merge in Pandas (with Code)

Use validate="many_to_one" in a left merge to require unique join keys in the right-hand lookup table.

Automated Data Analysis · 5 MIN

How to aggregate columns with named groupby outputs in Pandas (with Code)

Pass output_name=(input_column, aggregation) pairs to DataFrameGroupBy.agg to create explicitly named summary columns.

Automated Data Analysis · 5 MIN

The 10 Best Universities for Data Science and Analytics in the US — 2026 Rankings by Career Outcome

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.

Automated Data Analysis · 5 MIN

How to flag missing values before imputation in Pandas (with Code)

Capture an isna mask before filling values, then store it as a Boolean or zero-one indicator beside the original column.

Software

Tools for automated data science

Reviews and comparisons of the platforms that automate parts of the analytical workflow, evaluated against the methodology we publish.

Explore Data Science Tools

The Automatic Analysis Brief

Research, tools and practical techniques for automated statistics, machine learning and AI-assisted analytics.