MATHS 7107 - Data Taming

North Terrace Campus - Trimester 2 - 2022

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 of 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 7107
    Course Data Taming
    Coordinating Unit School of Mathematical Sciences
    Term Trimester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange N
    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 of 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: 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, 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)

    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.

    1,3,4

    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.

    2,3

    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.

    6

    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.

    5

    Attribute 7: Digital capabilities

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

    1,2,3,4,5
  • 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

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

    This is a 3-unit course. In the semester or trimester format, you are expected to allocate the following study time to fully meet the Course Learning Outcomes (CLOs) for this course. Please note that students work at different paces, so this indicates the approximate time required to complete this course.

    Learning Activity Hours / Week Duration Total
    Online Learning Activities 2 hours 12 weeks 24 hours
    Face to Face Learning Activities 2 hours 12 weeks 24 hours
    Independent Study 4 hours 12 weeks 24 hours
    Assessment Tasks 5 hours 12 weeks 60 hours
    Total 156 hours


    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.
  • 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
    Task Due Weighting Learning Outcomes
    Assignments (x 5) Even Weeks 25% All
    Weekly Quizzes (x 12) Weekly 10% All
    Tutorial / Practical participation Weekly 5% All
    Mid-semester quiz Week 9 20% All
    Final report Week 13 40% 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
    Unless otherwise specified, submit all of your assessments to the Assignments space in the MyUni course site for this course. For written assessments, your submissions will go through Turnitin to check for originality. 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.