DATA 7302OL - Real Data: Modern Methods for Finding Hidden Patterns

Online - Online Teaching 2 - 2022

This course builds upon DATA 7201OL Data Taming, to introduce advanced modern techniques for extracting meaningful information from real-world, messy datasets. The course covers methods such as generalised linear models, classification, advanced regression techniques, and unsupervised statistical learning. A particular focus will be data wrangling techniques for non-standard, big, ?messy data?: natural language processing, networks and longitudinal data. The course teaches advanced R programming techniques for data science.

  • General Course Information
    Course Details
    Course Code DATA 7302OL
    Course Real Data: Modern Methods for Finding Hidden Patterns
    Coordinating Unit Mathematical Sciences
    Term Online Teaching 2
    Level Postgraduate Coursework
    Location/s Online
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange N
    Prerequisites Carousel 1 Courses: COMP SCI 7212OL, COMP SCI 7210OL, DATA 7201OL & DATA 7202OL
    Restrictions Graduate Diploma in Data Science (Applied) OL OR Master of Data Science (Applied) OL Only
    Course Description This course builds upon DATA 7201OL Data Taming, to introduce advanced modern techniques for extracting meaningful information from real-world, messy datasets. The course covers methods such as generalised linear models, classification, advanced regression techniques, and unsupervised statistical learning. A particular focus will be data wrangling techniques for non-standard, big, ?messy data?: natural language processing, networks and longitudinal data. The course teaches advanced R programming techniques for data science.
    Course Staff

    Course Coordinator: Dr John Maclean

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    1. create a predictive model for classification (predict classes) from real data using the TidyModels package in R
    2. create a predictive model for regression (predict numbers) from real data using the TidyModels package in R
    3. identify when predictive modelling is not giving accurate predictions due to overfitting
    4. apply cross-validation to avoid overfitting
    5. contrast the performance of prediction models to assess their viability
    6. analyse unsupervised data to find the patterns and represent the patterns visually
    7. communicate results of the interpretation and analysis of predictive modelling.
    University Graduate Attributes

    No information currently available.

  • Learning & Teaching Activities
    Learning & Teaching Modes

    No information currently available.

    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
    Assessment consists of
    Type of assessment Week(s) due Weighting
    Quizzes 1,2,3,4,5,6 20% total
    Case Study 1 4 30%
    Case Study 2 end of course 50%


    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

    Counselling for Fully Online Postgraduate Students

    Fully online students can access counselling services here:

    Phone: 1800 512 155 (24/7) 

    SMS service: 0439 449 876 (24/7) 

    Email: info@assureprograms.com.au

    Go to the Study Smart Hub to learn more, or speak to your Student Success Advisor (SSA) on 1300 296 648 (Monday to Thursday, 8.30am–5pm ACST/ACDT, Friday, 8.30am–4.30pm ACST/ACDT)

  • 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.