Machine learning is not magic - we make data science easy and accessible for everyone.
"Information is the oil of the 21st century, and analytics is the combustion engine". This statement from Peter Sondergaard (Gartner Research) reflects the industry’s enthusiasm for data. Primarily, tech companies such as Amazon or Google use the collected data to offer personalized services. However, as with oil that cannot be used in its raw form, data must be "refined" to reveal its value. In other words, a good toolbox is needed to prepare, understand, analyze, and perform predictions on the data.
The objective of the Software Engineering for Applied Data Analytics Group is the application of predictive data analytics across disciplines and domains. Here we approach the challenges from the perspective of Data Science and Software Engineering. The vision of the group encompasses the following two main pillars:
- Developing an automated framework for streamlining the data science task
- “Visualizing and facilitating” the data science task across disciplines and domains
The expertise of the Software Engineering for Applied Data Analytics Group comprises
- Time series imputation, analysis, and forecasting,
- Feature selection and engineering,
- Machine learning, and
- Deep learning.
We have ongoing and successfully completed projects in the areas of
- Aerospace (High Performance Data Analytics),
- Industry (Predictive Maintenance),
- Medicine (Heart Failure Prediction),
- Nature (Bee Mortality Prediction),
- Psychology (Anxiety Prediction), and
- Sport (Human Performance Modelling).