STATS 7022 - Data Science PG
North Terrace Campus - Trimester 1 - 2024
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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 Course Planner.
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course, students will:- Demonstrate an understanding of the foundational principles of machine learning
- Recognise which method to use for a given data analysis problem.
- Demonstrate an understanding the statistical underpinning of the chosen method.
- Implement safely any chosen method and interpret the results.
- Be confident to apply the methods to large datasets.
- 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.
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Learning Resources
Required Resources
All required resources are provided in MyUni. There is no requirement to buy a textbook.Recommended Resources
- James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning: with Applications in R 1st ed. (Springer New York)
- Hastie, Tibshirani, Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd ed. (Springer New York)
- Kuhn, Johnson: Applied Predictive Modelling 1st ed. (Springer New York)
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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
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Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Assessment WeightingWritten assignments 15%Online quizzes 5%Online 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 practical exam and 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 Access Adelaide.
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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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- International Student Support
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
- YouX Student Care - Advocacy, confidential counselling, welfare support and advice
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Elder Conservatorium of Music Noise Management Plan
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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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.
