APP MTH 7124 - Decision Science PG

North Terrace Campus - Trimester 3 - 2022

This course is focused on equipping students with simulation techniques to underpin decision making. Simulation is widely used to model systems, to evaluate risk, and to optimise objective functions, with the goal to inform decisions. Building up from uniform random generation, some of the key simulation techniques used for efficient simulation to support decision-making will be presented. Topics covered are: Uniform random number and random variable generation; random process generation; discrete-event simulation; basic statistical analysis of simulation data; variance reduction techniques; rare-event simulation; randomized optimization; applications in systems modelling, risk analysis and optimisation.

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Course Details

Course Code	APP MTH 7124
Course	Decision Science PG
Coordinating Unit	Mathematical Sciences
Term	Trimester 3
Level	Postgraduate Coursework
Location/s	North Terrace Campus
Units	3
Contact	Up to 3 hours per week
Available for Study Abroad and Exchange	Y
Assumed Knowledge	(MATHS 1012 or MATHS 1004) and (MATHS 2103 or MATHS 2107). Proficiency in at least one of R, Python, MATLAB or Julia.
Assessment	Ongoing assessment and examination.

Course Staff

Course Coordinator: Dr Shenal Dedduwakumara

This course is focused on equipping students with simulation
techniques to underpin decision-making. Simulation is widely used to
model systems, to evaluate risk, and to optimise objective functions,
with the goal to inform decisions. Building up from uniform random
generation, some of the key simulation techniques used for efficient
simulation to support decision-making will be presented.
Students will be able to utilise: uniform random number and random
variable generation; random process generation; discrete-event
simulation; basic statistical analysis of simulation data;
applications in systems modelling and risk analysis.

Course Timetable

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

Course Learning Outcomes

On successful completion of this course, you will be able to:

1	Communicate how randomness and controlled variation can be used to model complex systems in domains such as industry, health and transportation.
2	Create a model of a real-world problem specified in words and implement it as a discrete-event simulation.
3	Validate results from a discrete-event simulation.
4	Use simulation to explore scenarios, elicit and compare possibilities.
5	Derive quantative information with measures of confidence from systematic simulation.
6	Design simulation-based workflows to support decision-making in real-world contexts.

最新糖心Vlog 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
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.	4, 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.	1
Attribute 5: Intercultural and ethical competency Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.	1
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.	1

Learning & Teaching Modes

This course is delivered in a semester, trimester and intensive format, although enrolment options may be limited by availability.

This course offers opportunities for you to learn through blended learning approaches, meaning some of the learning is done autonomously online and some of the learning is done through face-to-face engagement. This blended approach is used to create a rich scaffolded and supportive learning experience.

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	1 hour	12 weeks	12 hours
Face-to-face learning activities	3 hours	12 weeks	36 hours
Independent study	4 hours	12 weeks	48 hours
Assessment tasks	5 hours	12 weeks	60 hours
Expected total student workload			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. Throughout 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.

Learning activities, both online and face-to-face, are scaffolding to the learning builds throughout the course. Through this learning experience, you will be asked to draw on a range of lower-order and higher-order thinking skills.

The 最新糖心Vlog'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 Task	Weighting	Individual/Group	Week Due	Learning Outcome
Quizzes	20%	Individual	Weeks 2, 4, 6, 8, 10	1, 3, 4, 5
Programming Assignment	40%	Individual	Week 10	1, 2, 3, 6
Project Report	40%	Individual	Week 12	1, 2, 3, 4, 5, 6

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 最新糖心Vlog 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 .

Student Feedback

The 最新糖心Vlog 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 最新糖心Vlog 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 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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