Course overview
Data science is increasingly being used to make important decisions in the world. However, the methods have been accused of being black box, taking data and producing information with little understanding of the process. In this course we introduce and explore the mathematical foundations of tools commonly used in data science. Learners will gain skills in using and interpreting common tools for supervised and unsupervised learning, with a particular focus on their mathematical foundations This course broadens the tools learners will have experience with and introduces them to one of the many uses of mathematics in industry.
Course learning outcomes
- Implement supervised and unsupervised methods in Python
- Analyse a scenario and identify the correct analysis method
- Compare and contrast properties of analysis methods
- Reproduce or annotate (as appropriate) mathematical derivation of key properties of methods
- Interpret results of method in context
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