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Course Outlines

STATS 7022 - Data Science PG

North Terrace Campus - Trimester 1 - 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 1
    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 r