Critical Thinking with Data (MAST20034)

Undergraduate level 2Points: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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This subject teaches students to become critical interpreters and users of data-based evidence. Future data scientists working across many disciplines will develop skills in identifying the strengths and weaknesses of arguments based on quantitative evidence, and learn to evaluate reasoning that uses probabilistic ideas and the results of statistical analysis.  They will develop skills in interpretation, principled reporting and communication of statistical evidence.

Data-based evidence is foundational in all of science.  The growth of “big data” and interest in data science has accentuated the need for a well-developed understanding of how scientific studies are designed, analysed and communicated.  The media, other academic research and many aspects of everyday life also use and build on data presented and processed in many different ways.

The subject examines methods of judging the quality of data-based evidence, and the strength of conclusions drawn from it, including concerns in establishing causality. It provides students with frameworks for evaluating study quality and deals with quantifying uncertainty in conclusions, describing how data-based evidence can contribute to the accumulation of scientific knowledge.  

The subject emphasises the skills needed to use, interpret and communicate statistical and data science related ideas and findings in real world contexts.

Intended learning outcomes

On completion of this subject, students should be able to:

  • think critically about quantitative data in a range of scientific contexts;
  • apply the principles of collecting data as evidence (through controlled experiments, surveys and observational studies);
  • critically examine the evidence in data (including graphical representation, summary measures, and statistical modelling);
  • appropriately draw conclusions from the evidence in data (based on modelling, estimates, confidence intervals, p-values, and meta-analysis);
  • critically assess claims based on quantitative data;
  • present and communicate quantitative findings effectively.

Generic skills

  • analytical skills: the ability to critically evaluate and discuss the methodologies and research findings based on data;
  • problem solving skills: including the ability to engage with unfamiliar contexts, identify relevant resources and conduct research;
  • collaborative skills: the ability to work in a team;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments;
  • presentation skills: both written and oral.

Last updated: 14 March 2025

Eligibility and requirements

Prerequisites

A minimum of one of

Code Name Teaching period Credit Points
MAST10010 Data Analysis
Semester 2 (On Campus - Parkville)
 12.5
MAST20031 Analysis of Biological Data
Semester 1 (On Campus - Parkville)
 12.5
ECON10005 Quantitative Methods 1
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
ECOM20001 Econometrics 1
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
MAST10024 Mathematics & Statistics for Biomedicine
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
No longer available

OR

Note: the following subject/s can also be taken concurrently (at the same time)

Code Name Teaching period Credit Points
MAST20005 Statistics
Semester 2 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
 12.5

Corequisites

None

Non-allowed subjects

None

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 14 March 2025

Assessment

DescriptionTimingPercentage

5 fortnightly on-line revision quizzes

  • 5 hours
Throughout the teaching period5%

Four short assignments amounting to a total of up to 800 words due at regular intervals

  • 800 words
Throughout the teaching period20%

A group project involving production of a poster and a 5-minute oral presentation due after mid-semester

  • 15 hours (of work required)
Second half of the teaching period15%

A 2.5-hour written examination

  • 2.5 hours
During the examination period60%

Last updated: 14 March 2025

Dates & times

  • Semester 2
    Principal coordinatorPaul Fijn
    CoordinatorAge Zanca
    Mode of deliveryOn Campus (Parkville)
    Contact hours48 contact hours 2 x one hour lectures per week, 1 x two hour tutorial per week.
    Total time commitment170 hours
    Teaching period28 July 2025 to 26 October 2025
    Last self-enrol date 8 August 2025
    Census date 1 September 2025
    Last date to withdraw without fail26 September 2025
    Assessment period ends21 November 2025

    Semester 2 contact information

    paul.fijn@unimelb.edu.au; adriana.zanca@unimelb.edu.au

What do these dates mean

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  • Your tuition fees, academic transcript and statements.
  • And for Commonwealth Supported students, your:
       -  Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
     

    Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.

Last updated: 14 March 2025

Further information

Last updated: 14 March 2025