BUSANA 7001 - Predictive and Visual Analytics
North Terrace Campus - Semester 2 - 2023
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General Course Information
Course Details
Course Code BUSANA 7001 Course Predictive and Visual Analytics Coordinating Unit Finance and Banking Term Semester 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 CORPFIN 7033 Assessment Exam/assignments/tests/tutorial work as prescribed at first lecture Course Staff
Course Coordinator: Associate Professor Sigitas Karpavicius
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course, students will be able to:
1. Use prediction models to solve forecasting queries.2. Use visual methods to present complex data relationships.3. Utilise state-of-the-art statistical packages for managing and presenting data.4. Critically benchmark and compare different prediction models.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 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.
1,2,4 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.
4 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.
3,4 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.
. Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency
Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.
. Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
1,2,3,4 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
Text book: Konasani, V. R. and Kadre, S. (2015). ‘Practical Business Analytics Using SAS: A Hands-on Guide’ (a free
electronic copy is available from the library).
Computer: Students will need a computer connected to the Internet (in order to use 'SAS OnDemand for Academics' and 'SAS Visual Analytics').Recommended Resources
Text book: Camm, J.D., Cochran, J.J., Fry, M.J., Ohlmann, J.W. (2021). Business Analytics, 4th Edition. -
Learning & Teaching Activities
Learning & Teaching Modes
This course will be delivered via a 3-hour seminar each week. Each week, new methods and tools used to analyse data will be introduced. The first half of the course will cover traditional data analysis methods. The second half of the course will focus on more complex tools, including machine learning.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. The University
expects full-time students (i.e. those taking 12 units per semester) to devote a total of 48 hours per week to their studies. This means that
you are expected to commit approximately 9 hours for a three-unit course or 13 hours for a four-unit course, of private study outside of your regular classes. Students in this course are expected to attend all lectures throughout the semester plus one tutorial class each week.Learning Activities Summary
The following topics will be covered:
1. Introduction; R vs SAS vs SAS Visual Analytics
2. Correlations and visual presentation
3. Descriptive statistics and predictions
4. Predictive analytics using multiple regressions
5. Predictive analytics using logit regressions
6. Time-series analysis
7. Time-series forecasting
8. Monte Carlo simulation, scenario analysis
9. Optimization
10. Machine learning and variable importance
11. Network analysis and path analysis -
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 Task Task Type Due Weighting Learning Outcome Assignment Individual Week 5
25% 1, 2, 3, 4 Online Test 1 Individual Week 6 25% 1, 3, 4 Group Assignment Group Week 11 25% 1, 2, 3, 4 Online Test 2 Individual Final teaching week 25% 1, 3, 4 Assessment Detail
- Both online tests will be conducted via MyUni.
- Individual and group assignments involve analysing contemporaneous business problems using advanced statistical methods and software.
- To gain a pass for this course, a mark of at least 50% overall needs to be obtained.
Submission
Assignements should be submitted via MyUni.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 .
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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
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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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
- 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鈥檚 disciplinary procedures.
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