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Course Outlines

STATS 7022 - Data Science PG

North Terrace Campus - Trimester 2 - 2024

This course will introduce the fundamental concepts of modern data science. It will provide students with tools to deal with real, messy data, an understanding of the appropriate methods to use, and the ability to use these tools safely. Topics will include data structures; regression models including lasso regression, ridge regression and non-linearity with splines; classification models including logistic regression, linear discriminant analysis, support vector machines and random forests; and unsupervised learning methods such as principal component analysis, k-means and hierarchical clustering. The practical skills will be focused on data science in R.

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  • General Course Information
    Course Details
    Course Code STATS 7022
    Course Data Science PG
    Coordinating Unit 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 Y
    Prerequisites MATHS 7027 and MATHS 7107
    Assumed Knowledge STATS 7107
    Assessment Ongoing assessment and examination.
    Course Staff

    Course Coordinator: Mr Max Glonek

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course, students will:
    1. Demonstrate an understanding of the foundational principles of machine learning
    2. Recognise which method to use for a given data analysis problem.
    3. Demonstrate an understanding the statistical underpinning of the chosen method.
    4. Implement safely any chosen method and interpret the results.
    5. Be confident to apply the methods to large datasets.
    6. Apply the theory in the course to solve a range of problems at an appropriate level of difficulty.
    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, 2, 3, 4, 5, 6

    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, 5, 6

    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, 6

    Attribute 7: Digital capabilities

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

    		 1, 2, 3, 4, 5, 6

    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.

    		 4
  • Learning Resources
    Required Resources
    All required resources are provided in MyUni. There is no requirement to buy a textbook.
    Recommended Resources
    1. James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning: with Applications in R 1st ed. (Springer New York)
    2. Hastie, Tibshirani, Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd ed. (Springer New York)
    3. Kuhn, Johnson: Applied Predictive Modelling 1st ed. (Springer New York)
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course uses a flipped-classroom model. Each week, students are expected to watch weekly topic videos in their own time. Material presented in the topic videos is then reinforced through a weekly interpretation seminar, and a weekly implementation practical.
    Workload

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

    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 Quantity Workload Hours
    Weekly Topic Videos 12 24
    Weekly Workshops 12 24
    Weekly Computer Exercises 12 24
    Assignments 3 33
    Online Tests 3 33
    Online Quizzes 12 12
    TOTAL 150
    Learning Activities Summary

    The course will cover the following topics:

    • Overview of modelling frameworks
    • Data preprocessing
    • Model theory
    • Resampling
    • Penalised regression
    • Classification modelling
    • LDA/SVM
    • Non-parametric methods
    • Decision trees
    • Random forests
    • Feature selection
    • Unsupervised learning
  • 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
    Weighting
    Written assignments
    15%
    Online quizzes
    5%
    Mid-trimester tests
    30%
    Practical exam
    20%
    Written exam
    30%
    Assessment Related Requirements

    An aggregate score of 50% is required to pass the course.

    This course also contains an exam hurdle. Students must achieve a grade of at least 40% in the written exam in order to pass the course.

    Assessment Detail

    No information currently available.

    Submission

    All written assignments are to be e-submitted following the instructions on MyUni.

    See MyUni for more comprehensive details regarding assignment submission, the late policy, etc.

    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 .

  • 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

    This section contains links to relevant assessment-related policies and guidelines - all university policies.

  • 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鈥檚 disciplinary procedures.

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