MATHS 1005 - Data Literacy
North Terrace Campus - Semester 1 - 2021
General Course Information
Course Code MATHS 1005 Course Data Literacy Coordinating Unit School of Mathematical Sciences Term Semester 1 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 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 Coordinator: Dr Melissa Humphries
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
- Understand the foundations of basic probability.
- Be able to critically analyse and improve data collection designs.
- Be familiar with Excel and use it to create approriate graphics to visualise patterns in data.
- Understand the importance of statistics in modern scientific research.
University Graduate Attributes
No information currently available.
Learning & Teaching Activities
Learning & Teaching ModesThis 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.
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
No information currently available.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Component Weighting Outcomes Assessed Assignment x2 40% All Final Report 30% All Major Quiz x2 20% All Weekly Quizzes x10 10% All
Assessment Related RequirementsAggregate score of at least 50%
Assignment Item Distributed Due Date Weighting Assignment 1 Week 2 Week 4 20% Major Quiz 1 Week 6 Week 6 10% Assignment 2 Week 5 Week 8 20% Final Report Week 9 Week 12 30% Major Quiz 2 Week 12 Week 12 10%
SubmissionAll submissions will be via electronic submission on MyUni. Any written assignments will be tested for plagiarism through Turnitin.
Late assignments will not be accepted.
Assignments will have a two week turn-around time for feedback to students.
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.
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.
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