These lecture notes are written for the University of Edinburgh Course Informatics 2– Foundations of Data Science. Ultimately we aim that they will give comprehensive (though perhaps not exhaustive) coverage of the material covered in the lectures. Although the notes are designed to fit in with the Foundations of Data Science course, we’re making them an open educational resource available under a Creative Commons licence. The development of this course has benefited from other open educational resources. Since the notes are fairly well developed (though still not complete or error free!) it seems right to give back to the world.

 

The philosophy of Foundations of Data Science

It is often said that 80% of the time spent on a data science project is in preparing the data, and the quality of the data is crucial to the outcome of any data science or machine learning project. The philosophy of Foundations of Data Science is to allow students to acquire the foundational skills of data, before moving on to other courses (such as Machine Learning), which build on these foundations. As stated in the Learning Outcomes, we intend that on completion of this course, the student will be able to:

1. Describe and apply good practices for storing, manipulating, summarising, and visualising data.
2. Use standard packages and tools for data analysis and describing this analysis, such as Python and LaTeX.
3. Apply basic techniques from descriptive and inferential statistics and machine learning; interpret and describe the output from such analyses.
4. Critically evaluate data-driven methods and claims from case studies, in order to identify and discuss a) potential ethical issues and b) the extent to which stated conclusions are warranted given evidence provided.
5. Complete a data science project and write a report describing the question, methods, and results.

 

View Informatics 2– Foundations of Data Science course materials

 

 

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