Data Science - Computational Track
Programme overview
Main Subject
Data Science and Artificial Intelligence
Degree
MSc
Study Level
Masters
Study Mode
On Campus
We offer a Masters in Data Science degree that covers basic and advanced essentials in statistical inference, machine learning, data visualization, data mining, and big data methods, all of which are key for a trained data scientist. In order to be selected for our program we require a basic background in calculus, linear algebra, probability, computer programming, data structures, and algorithms. Our program is spread across 30 credits and contains projects involving big datasets, classification methods, variable selection, and deep learning to name a few.
In our curriculum, we make extensive use of the Python programming language and its data science libraries while also featuring tools like R for statistical analysis, Tableau for data visualization, and SQL for databases. Students work on assignments covering both theory and applications on real data with support available from the professor and teaching assistant.
Programme overview
Main Subject
Data Science and Artificial Intelligence
Degree
MSc
Study Level
Masters
Study Mode
On Campus
We offer a Masters in Data Science degree that covers basic and advanced essentials in statistical inference, machine learning, data visualization, data mining, and big data methods, all of which are key for a trained data scientist. In order to be selected for our program we require a basic background in calculus, linear algebra, probability, computer programming, data structures, and algorithms. Our program is spread across 30 credits and contains projects involving big datasets, classification methods, variable selection, and deep learning to name a few.
In our curriculum, we make extensive use of the Python programming language and its data science libraries while also featuring tools like R for statistical analysis, Tableau for data visualization, and SQL for databases. Students work on assignments covering both theory and applications on real data with support available from the professor and teaching assistant.
Admission Requirements
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