Education’s Data Science Revolution

The education sector has experienced significant reform in the decade. Policy and funding model changes, new market entrants in the form of Massive Online Open Courses (MOOCs) and the introduction of a demand-driven system for tertiary education, have all occurred in an environment where domestic demand for education has grown at a faster rate than population.

The importance of Big Data in education cannot be understated. To stay competitive locally and globally, public and private education providers must be aware of crucial issues such as trends in course preferences and forecasts of international student numbers.

The Solution


Educational institutions have a unique opportunity to apply advanced statistical and machine learning frameworks to alleviate bottlenecks, increase operational efficiency and improve student outcomes. The scheduling module in AutoStat® can optimise schedules and automate the process taking account of changes to timetables from year to year and intra year periods. In so doing, AutoStat® will optimise the use of infrastructure, classrooms and associated equipment. The AutoStat® machine learning and statistical module can assist educators gain insight into data that cannot be gleaned by using traditional methods. To this extent, AutoStat® can perform deep dives into data, cleansing and consolidating millions of pieces of content and data, and making connections and conclusions that positively impact the teaching and learning process. In the form of predictive analytics, AutoStat® assists administrators and educators make conclusions about things that may happen in the future. For instance, using a data set of middle or high school students’ cumulative records, predictive analytics can tell us which ones are more likely to drop out because of academic failure or even their predicated score on a standardized exam.

AutoStat® in Education

AutoStat®’s vast range of inbuilt algorithms allow users to undertake exploratory analysis, rapid protoyping, deploy and scale data science pipelines. AutoStat can help your organization to:

1. Analyze and interpret disparate educational datasets
2. Conduct sentiment analysis of student feedback and communications
3. Build predictive models of the student population by department and course
4. Undertake student marketing analysis including hypothesis testing to isolate and quantify the factors that impact demand for each course See autostat education project


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