MATHS 1005 - Data Literacy

North Terrace Campus - Semester 2 - 2020

In an increasingly data-centric world, a working understanding of data analytics and quantitative methods is essential, for all members of society. When presented with claims in the media that are accompanied by statistics, diagrams, and outputs from technologies like ?artificial intelligence? and ?machine learning?, how can we learn to separate useful information from pseudoscience? In other words, how can we learn to not be fooled by statistics? The aim of this course is to improve students? ?data literacy?, through a largely non-technical introduction to some of the foundational concepts in statistical thinking. The course will teach students from all backgrounds how to interpret and critically appraise claims made by machine learning and quantitative data science methods, and understand both the possibilities and pitfalls of these emerging sciences. It assumes no technical background and is taught largely through case studies of applications of data science outside of academia. The course teaches some fundamental quantitative methods for dealing with and interpreting data, as well as visualisation techniques using computer software tools such as Tableau. Topics include: how to translate mathematical jargon into understandable language; measuring and talking about uncertainty using probability; how to easily make clear charts and data visualisations; demystifying fundamental statistical ideas (correlation versus causation, distinguishing between ?significant? and ?important? results); explaining and predicting with statistical models; the ethics of data science.

  • General Course Information
    Course Details
    Course Code MATHS 1005
    Course Data Literacy
    Coordinating Unit School of Mathematical Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 5 hours per week
    Available for Study Abroad and Exchange Y
    Restrictions Not Available to BMaCompSc, BMaSc, BMaSc(Adv) students
    Course Description In an increasingly data-centric world, a working understanding of data analytics and quantitative methods is essential, for all members of society. When presented with claims in the media that are accompanied by statistics, diagrams, and outputs from technologies like ?artificial intelligence? and ?machine learning?, how can we learn to separate useful information from pseudoscience? In other words, how can we learn to not be fooled by statistics?

    The aim of this course is to improve students? ?data literacy?, through a largely non-technical introduction to some of the foundational concepts in statistical thinking. The course will teach students from all backgrounds how to interpret and critically appraise claims made by machine learning and quantitative data science methods, and understand both the possibilities and pitfalls of these emerging sciences. It assumes no technical background and is taught largely through case studies of applications of data science outside of academia. The course teaches some fundamental quantitative methods for dealing with and interpreting data, as well as visualisation techniques using computer software tools such as Tableau.

    Topics include: how to translate mathematical jargon into understandable language; measuring and talking about uncertainty using probability; how to easily make clear charts and data visualisations; demystifying fundamental statistical ideas (correlation versus causation, distinguishing between ?significant? and ?important? results); explaining and predicting with statistical models; the ethics of data science.
    Course Staff

    Course Coordinator: Dr Melissa Humphries

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes

    No information currently available.

    University Graduate Attributes

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  • Learning & Teaching Activities
    Learning & Teaching Modes

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    Workload

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    Learning Activities Summary

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  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary

    No information currently available.

    Assessment Detail

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    Submission

    No information currently available.

    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

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    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

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  • Policies & Guidelines
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