MAST20031 Analysis of Biological Data

Credit Points: 12.5
Level: 2 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2017:

Semester 1, Parkville - Taught on campus.Show/hide details
Pre-teaching Period Start not applicable
Teaching Period 27-Feb-2017 to 28-May-2017
Assessment Period End 23-Jun-2017
Last date to Self-Enrol 10-Mar-2017
Census Date 31-Mar-2017
Last date to Withdraw without fail 05-May-2017

Timetable can be viewed here.
For information about these dates, click here.
Time Commitment: Contact Hours: 2 x one hour online lectures per week, 2 x one hour interactive lectures per week, 1 x one hour computer laboratory class per week.
Total Time Commitment:

170 hours


12.5 credit points from any of the following:

Study Period Commencement:
Credit Points:
Semester 1
Corequisites: None
Recommended Background Knowledge:

25 points of first year Biology subjects

Non Allowed Subjects: None
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability


Dr Ben Phillips, Dr Davide Ferrari


Email: phillipsb@unimelb.edu.au ; dferrari@unimelb.edu.au

Subject Overview:

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.

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
  • 6 x 45min online quizzes, held fortnightly throughout the semester (15%)
  • 3 x 500 word assignments, due weeks 5, 9, 11 (25%)
  • 2 hour exam, held in examination period (60%)
Prescribed Texts:

Whitlock and Schluter, The Analysis of Biological Data.

Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
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
Related Majors/Minors/Specialisations: Master of Engineering (Environmental)
Science-credited subjects - new generation B-SCI and B-ENG.
Selective subjects for B-BMED

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