Academic Researcher

 AutoStat®animation

Your data lab on the cloud

Take your research pursuits beyond hypothesis testing and harness the most advanced suite of statistical and ML frameworks in one platform.

Built by leading academics, AutoStat(R) encompasses the entire data analysis and modelling continuum from data upload to model interpretation, allowing academic teams to take their research to the next level. Access bayesian variable selection frameworks and interpret ML outputs like never before with AutoStat(R) model builder.

• R Integration

AutoStat®’s dedicated module for analytics in R. Users can run, save and integrate R scripts in their data science pipelines, or undertake additional pre-processing and analytics in the R console where applicable.

• Model Builder

Create reports and journal ready papers within AutoStat®’s built-in document builder. Document editing allows you to easily incorporate the whole prjoject workflow into one compiled document.

• Document Builder

Create reports and journal ready papers within AutoStat®’s built-in document builder. Document editing allows you to easily incorporate the whole prjoject workflow into one compiled document.

• Bayesian Variable Selection & Bayesian Optimisation

Switch between frequentist and bayesian statistical models and let AutoStat(r) find the best parameters for optimal machine learning training.

• Data Manipulation

Undertake relational-style database querying for easy data manipulation and pre-processing, including filtering, aggregation, concatenation, reshaping and imputation.

• Team collaboration & sharing

Set up groups, permissions and share your insights with the team. Distribute entire solutions to other teams for cross-collaboration

Experience AutoStat®

From data processing and visualisation, to machine learning and beyond, get up to speed with the most advanced data science techniques with real-world projects using AutoStat®.

Ensemble Machine Learning

Applying ML techniques to predict Heart Disease

Regression Models in Practice

A deep-dive into frequentist and Bayesian regression models

Mixed Effects Modelling

Modelling chick weights over time with fixed and random effects

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