Critical Thinking With Data

Subject UNIB10006 (2010)

Note: This is an archived Handbook entry from 2010.

Credit Points: 12.50
Level: 1 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2010:

Semester 1, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Lectures, practice classes, on-line materials.

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 3 x one hour lectures per week, 1 x one hour practice class per week.
Total Time Commitment: Estimated total time commitment of 120 hours
Prerequisites: None
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements: It is University policy to take all reasonable steps to minimise the impact of disability upon academic study and reasonable steps will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact upon their active and safe participation in a subject are encouraged to discuss this with the relevant subject coordinator and the Disability Liaison Unit.

Coordinator

Dr Robert Maillardet

Contact

First Year Coordinator

Email: fycoord@ms.unimelb.edu.au

Subject Overview:

This subject teaches students to become critical users of data-based evidence. Future journalists, political scientists, sociologists, lawyers, health professionals, psychologists, environmental scientists, business people, engineers, scientists and teachers will develop skills in identifying the strengths and weaknesses of arguments and reports based on quantitative evidence, and learn to evaluate reasoning that uses probabilistic ideas.

Data-based evidence is found in the media, in academic research and in many aspects of everyday life. The subject examines ways of judging the quality of quantitative information, and the strength of conclusions drawn from it, including concerns in establishing causality. It discusses how variability may be characterised and modelled in a wide variety of settings including public opinion, health, sport, legal disputes, and the environment. It covers good and bad ways of examining evidence in data. The subject deals with judging the likelihood of events, common pitfalls in thinking about probability, measuring risk in medical contexts and quantifying uncertainty in conclusions. It describes how data-based evidence can contribute to the accumulation of knowledge.
Objectives:

On completion of this subject students should be able to

  • think critically about quantitative data in a broad range of contexts;

and should understand

  • the principles behind collecting data as evidence (through controlled experiments, surveys and observational studies);
  • how to examine the evidence in data (including graphical representation, summary measures, and the concepts of variation and modelling);
  • how to think about and describe the uncertainty in data (including probability, risk and psychological influences affecting human judgements about risk);
  • how to draw conclusions from the evidence in data (including confidence intervals, p-values and meta analysis);
  • how to critically assess media reports based on quantitative data.
Assessment:

Three written assignments due at regular intervals during semester amounting to a total of up to 600 words (15%), 15 on-line assessment tasks at regular intervals during semester (20%), a group project involving production of a poster and a 4-minute oral presentation due after mid-semester (10%), one 1200 word written assignment due at the end of semester (15%), and a 2 hour written examination in the examination period (40%).
Prescribed Texts: None
Breadth Options:

This subject potentially can be taken as a breadth subject component for the following courses:

You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills: Students with a breadth of knowledge across disciplines must be able to understand and critically evaluate the methodologies and research findings based on data. This subject aims to provide students with these critical thinking skills. It will be important for any student wishing to develop generic research and problem-solving skills. The subject will expose students to the application of data-based evidence across a range of disciplines, and contribute to their developing interdisciplinary understanding.
Related Course(s): Bachelor of Information Systems
Bachelor of Science and Bachelor of Information Systems

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