Data Literacy for Research & Industry
Empowering Decisions Through Accessible Analytics
"We used to learn reading, writing and arithmetic. Now we need to learn about data, too... It's also important for all of us to be data literate so that we can make use of the data ourselves..."
Distinguished Professor Kerrie Mengersen (2020)
Intuitive
GUI Driven Software
No Code
Focus on Outcomes, Not Syntax
Unified
Statistics & Machine Learning
The Challenge: A Universal Skills Gap
The explosion of data across government, business, and research has created a critical need for data literacy in almost every member of society. However, a significant skills gap exists. Researchers often lack the time to learn coding, small to medium businesses cannot afford large data science teams, and educators need better tools to teach statistical thinking to the next generation.
Our Solution: AutoStat®
To address this challenge, we are developing AutoStat®, a statistical software package designed to be accessible to a wide range of users. Its core philosophy is to provide access to sophisticated algorithms through an intuitive, graphical user interface that does not require coding.
AutoStat® allows users to switch between and compare different paradigms like Bayesian, Frequentist, and machine learning. Explore the interactive simulation below to see how a user might compare models for their specific problem.
Interactive Model Comparison
1. Choose Your Persona & Problem:
2. Compare Model Outputs:
Machine Learning Model
Random Forest combines votes of many decision trees.
Statistical Model
Logistic regression with coefficients.
The Impact: Data Literacy for All
AutoStat® democratises data analysis, providing tangible benefits across different sectors. By removing the coding barrier, it unlocks the potential for more people to make better data informed decisions. Explore the benefits for our key user groups below.
researchers
business
educators
Empowering Research
Researchers with limited coding skills can run sophisticated analyses. AutoStat® helps them move faster by comparing models and visualising complex patterns.
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