MATHS 1006 - Data Taming & Prediction

North Terrace Campus - Semester 2 - 2021

This course is a practical introduction to the practice of wrangling, finding relationships in, and making predictions from, messy datasets using statistical methods. The course introduces the principles ?tidy data", types of data and data formats, exploratory data analysis, data transformation, as well as model fitting and prediction using statistical machine learning tools. A focus will be to introduce R programming for data science applications, particularly through real-world case studies.

  • General Course Information
    Course Details
    Course Code MATHS 1006
    Course Data Taming & Prediction
    Coordinating Unit School of Mathematical Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Course Description This course is a practical introduction to the practice of wrangling, finding relationships in, and making predictions from, messy datasets using statistical methods. The course introduces the principles ?tidy data", types of data and data formats, exploratory data analysis, data transformation, as well as model fitting and prediction using statistical machine learning tools. A focus will be to introduce R programming for data science applications, particularly through real-world case studies.
    Course Staff

    Course Coordinator: Associate Professor Lewis Mitchell

    Lecturer: Louise Campbell
    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, students will be able to:
    1. Describe the principles of data taming and approaches used to tidy data.
    2. Identify the different types of data and data variables.
    3. Select from data analysis and visualisation techniques to create a linear model and make predictions from it.
    4. Execute techniques to transform, reduce and summarise data in order to visualise it.
    5. Articulate the ideas that data scientists consider when looking at data.
    6. Communicate professionally on the application of linear models through the use of real-world case studies.
    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1,3,4
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    2,3
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    6
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    5
  • Learning Resources
    Required Resources
    All required resources will be provided through MyUni.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course uses a flipped classroom approach -- there will be prescribed material to consume in preparation for a weekly active learning session, as well as tutorials and computer labs.
    Workload

    No information currently available.

    Learning Activities Summary

    No information currently available.

  • 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
    Assignments 25%
    Quizzes 10%
    Tutorial / Practical participation 5%
    Mid semester quiz 20%
    Final report 40%
    Assessment Detail

    No information currently available.

    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.

  • 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.