Cooperative Masters in Data Science
Whether you’re building data-driven products, improving forecasting, or trying to make better decisions with the data you already have, AIMS Data Science interns help turn complex data into practical solutions. Trained in machine learning, statistical modeling, and optimization, they bring the analytical tools needed to uncover insights, build models, and support smarter strategies across your organization.

Courses on the Program
The program includes training in areas such as:
Dr. Olushina Olawale Awe
Ludwigsburg University of Education, Germany
Deep Learning Methods and Algorithms
Prof. Azeez Stephen
Dublin Business School, Ireland
Machine Learning for Finance and Business
Dr. Fernand Leonel Mouasson
Memorial University, Canada
Numerical weather prediction and Forecasting
Dr. Patrice Monkam
Northeastern University, Shenyang, China
Computer Vision
Dr. Fazil Baksh
University of Reading, UK
Supervised Machine Learning
Dr. Pascal Sasdrich
Ruhr-Universität Bochum, Germany
Implementing Cryptographic Schemes
Dr. Allen Sam James llewelyn
ETH Zürich, Switzerland
Statistical methods for climate science
Prof. Edward Furman
York University, Canada
Actuarial Mathematics and Risk Theory
Prof. Timoteo Carletti
Université de Namur ASBL, Belgium Denison University
Complex Networks
Prof. Klaus Dohmen
Mittweida University of Applied Sciences, Germany
Cryptography and Security
Dr. Trylee Matongera
University of Nottingham, Malaysia
Crop monitoring and mapping
Tomorrow's Thought Leaders.
Organizations working with AIMS Data Science interns gain access to talent trained to tackle complex analytical problems across sectors.

During their six-month cooperative internships, students typically work on defined analytical projects that deliver tangible outputs—from predictive models and dashboards to research reports and prototype tools.
1
Data analysis and insight generation
Transforming raw organizational data into clear insights that support strategic decisions.
2
Predictive modeling and forecasting.
Building models to anticipate demand, identify trends, and improve planning.
3
Machine learning solutions.
Developing algorithms for classification, recommendation, and automation tasks.
4
Optimization and decision support.
Designing models that improve efficiency in logistics, finance, energy, and operations.
5
Data-driven product and strategy development.
Helping teams integrate analytics into products, services, and policy decisions.






