MATHS 7105 - Data Literacy

North Terrace Campus - Trimester 1 - 2022

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 simple spreadsheets. 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 7105
    Course Data Literacy
    Coordinating Unit School of Mathematical Sciences
    Term Trimester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 5 hours per week
    Available for Study Abroad and Exchange
    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 simple spreadsheets.
    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 Shenal Dedduwakumara

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course, you will be able to:
    1. Understand the foundations of basic probability.
    2. Be able to critically analyse and improve data collection designs.
    3. Be familiar with Excel and use it to create appropriate graphics to visualise patterns in data.
    4. Understand the importance of statistics in modern scientific research.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    all

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    all

    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

    3

    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

    4

    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.

    4

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    all

    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

    2,4
  • Learning Resources
    Required Resources
    All required resources will be provided through MyUni.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course introduces content in online topic videos. Workshops build on the online content by providing exercises and example problems to enhance the understanding obtained. These are further supported through practical sessions where computational literacy is developed.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.


    Activity  Quantity Workload hours
    Content Videos 3-5 weekly 36
    Workshops 6 in class (6 self-paced) 24
    Computer Labs 6 in class (6 self-paced) 24
    Quizzes 12 12
    Assignments 3 48
    TOTALS 144
    Learning Activities Summary
    You will be required to complete the online learning activities available on MyUni prior to regular face-to-face learning sessions. Through these autonomous tasks, you will have time to process new concepts and build foundational knowledge around them. In the face-to-face sessions, you will get a chance to apply that learning to build new skills and address real-world problems. Learning activities, both online and face-to-face, are scaffolding so the learning builds throughout the course. Through this learning experience, you will be asked to draw on a range of lower-order and higher-order thinking skills.
  • 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
    Assessment Item Distributed Due Date Weighting Learning Outcomes
    Assignment 1 Week 2 Week 4 20% All
    Major Quiz 1 Week 6 Week 6 10% All
    Assignment 2 Week 5 Week 8 20% All
    Final Report Week 9 Week 12 30% All
    Major Quiz 2 Week 12 Week 12 10% All
    Weekly quizzes Monday each week Friday each week 10% All
    Assessment Detail
    Full descriptions of the assessment tasks and associated grading rubrics are in the Assignments space on the MyUni course site. You will have opportunities to get further clarification on assessment tasks as needed.
    Submission
    All submissions will be via electronic submission on MyUni. Any written assignments will be tested for plagiarism through Turnitin. Make sure your submissions adhere to the University of Adelaide Academic Integrity policies.
    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.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    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.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.