BUSANA 7001 - Predictive and Visual Analytics
North Terrace Campus - Semester 1 - 2024
General Course Information
Course Code BUSANA 7001 Course Predictive and Visual Analytics Coordinating Unit Finance and Banking Term Semester 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 CORPFIN 7033 Course Description This course allows students to develop hands-on experience in constructing prediction models to assist business decision-making. Both traditional econometric models and emerging AI methods will be utilized. The course will also focus on representing data that appeals to the human visual-perceptual system. This includes utilising visual analytics tools to explore and analyse complex data sets using multivariate, text-based, geospatial, network, and graph-based data.
Course Coordinator: Associate Professor Sigitas Karpavicius
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn 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.
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.
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.
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.
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.
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.
Required ResourcesText 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 ResourcesText book: Camm, J.D., Cochran, J.J., Fry, M.J., Ohlmann, J.W. (2021). Business Analytics, 4th Edition.
Learning & Teaching Activities
Learning & Teaching ModesThis 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.
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 SummaryThe 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
10. Machine learning and variable importance
11. Network analysis and path analysis
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 Task Task Type Due Weighting Learning Outcome Assignment Individual
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
- 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.
SubmissionAssignements should be submitted via MyUni.
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.
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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