Algorithmic Foundations of Data Science
University of Edinburgh · Scotland · INFR11156
Algorithmic Foundations of Data Science is a subject at University of Edinburgh, Scotland. It covers topics like computational-mathematics, data-analytics, data-mining, data-science. It is part of 8 degrees. The information below is compiled from public course material — approximate, not official.
University of Edinburgh
INFR11156
3.00
Topics covered
Degrees that include Algorithmic Foundations of Data Science
- Artificial Intelligence (MSc) (Full-time)
- Artificial Intelligence (MSc) (Part-time) - 2 Years
- Artificial Intelligence (MSc) (Part-time) - 3 Years
- Cognitive Science (MSc) (Full-time)
- Cognitive Science (MSc) (Part-time) - 2 Years
- Cognitive Science (MSc) (Part-time) - 3 Years
- Informatics: LFCS: Foundations of Computer Science, Databases, Software & Systems Modelling (PhD) (Full-time)
- Sensing, Processing and AI for Defence and Security (UoE Lead with Heriot-Watt) (PhD with Integrated Study)
Frequently asked questions
What is Algorithmic Foundations of Data Science about?
Algorithmic Foundations of Data Science covers topics like computational-mathematics, data-analytics, data-mining, data-science.
Which degrees include Algorithmic Foundations of Data Science?
Algorithmic Foundations of Data Science is part of 8 degrees, including Artificial Intelligence (MSc) (Full-time), Artificial Intelligence (MSc) (Part-time) - 2 Years, Artificial Intelligence (MSc) (Part-time) - 3 Years, Cognitive Science (MSc) (Full-time), Cognitive Science (MSc) (Part-time) - 2 Years.
How many credit points is Algorithmic Foundations of Data Science?
Algorithmic Foundations of Data Science at University of Edinburgh is typically around 3.00 credit points. Approximate — confirm with the university.
Where is Algorithmic Foundations of Data Science taught?
Algorithmic Foundations of Data Science is taught at University of Edinburgh, Scotland.
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Sources: University of Edinburgh public course catalogue. Information is compiled and summarised — not copied verbatim — and is approximate; always confirm with the original source.
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