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  3. Analysis of Biological Data

Analysis of Biological Data (MAST20031)

Undergraduate level 2Points: 12.5On Campus (Parkville)

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Year of offer2019
Subject levelUndergraduate Level 2
Subject codeMAST20031
Semester 1
FeesSubject EFTSL, Level, Discipline & Census Date

A capacity to interpret data is fundamental to making informed decisions in everyday life. The design of experiments, analysis, and interpretation of biological data also lie at the very heart of the scientific enterprise. You cannot be a scientist without an understanding of data and design. This subject introduces you to fundamental concepts in data science for biology, with emphasis on modern statistical methods. Drawing on real biological problems and datasets, as well as drawing on data collected by the class, the lectures cover foundational concepts in experimental design and statistical modelling. The subject emphasises hands-on problem solving. As well as a solid grounding in statistical methodology, you will also develop practical skills, developing your capacity to design experiments, collect data, and analyse those data using the R statistical environment.

Intended learning outcomes

Students completing this subject should be able to:

  • Evaluate importance of careful design and analysis in scientific enterprise
  • Design biological experiments, build statistical models and sample real biological populations
  • Practically approach problems entailing the collection and analysis of biological data
  • Structure data sheets and enter data
  • Recognise and deal with common data types and models in biology
  • Understand fundamental statistical concepts including exploratory data analysis; basic principles of statistical inference; linear models, likelihood-based methods and re-sampling techniques
  • Execute basic analyses

Generic skills

The subject builds upon generic skills developed in first year level subjects, including the ability to critically assess and assimilate new knowledge. Students will also learn how to:

  • solve practical data analysis problems faced by biologists
  • design experiments and critically evaluate observations
  • evaluate and interpret real data

Last updated: 19 July 2019