School of Computing and Mathematical Sciences

Resources

Library resources

Open the library guide in a new page

Data science

  • Overview of statistical learning
  • Linear regression
  • Classification
  • Resampling methods
  • Linear model selection and regularisation
  • Moving beyond linearity
  • Tree-based methods
  • Support vector machines
  • Deep learning
  • Survival analysis and censored data
  • Unsupervised learning
  • Multiple testing

General computer science

A good general text is:

Computer Science: A Very Short Introduction by Subrata Dasgupta, Oxford University Press, 2016 (Print ISBN-13: 9780198733461)

which also has the advantage of being very concise.

Availability in Kortext may change; check the catalogue for current access.

Programming in Python

The recommended programming text depends on the programme of study you wish to undertake.

For prospective Data Science students:

For prospective Computer Science students

For a more detailed examination of programming and the Python programming language, consider the following text:

Software

Mathematics and statistics

  1. Linear Algebra (The Language of Data)

    When working with machine learning models or neural networks, data is almost always represented as matrices and vectors. You don’t need to write complex proofs, but you should understand how data is manipulated.

  2. Calculus (The Engine of Learning)

    You won’t necessarily be calculating complex integrals by hand, but you need to understand the language of change, because optimizing an AI model (like training a neural network via backpropagation) relies entirely on calculus.

  3. Probability and Core Statistics (The Foundation of Analytics)

Khan Academy is an excellent source for targeted brush-ups on Linear Algebra and Multivariable Calculus. Also, MIT OpenCourseWare (18.06 Linear Algebra by Gilbert Strang) is free to download.

If you are a visual learner then StatQuest with Josh Starmer (YouTube): Great for visual, intuitive explanations of core statistics and machine learning concepts without overwhelming mathematical jargon.

Students have recommended 3Blue1Brown (YouTube) has playlists “Essence of Linear Algebra” and “Essence of Calculus”. They provide the geometric, intuitive understanding that makes the coding side of AI much easier to grasp. Also, OpenIntro Statistics (David Diez et al.).

Useful sites

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