BUSANA 7000 - Fundamentals of Business Analytics
North Terrace Campus - Semester 2 - 2023
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General Course Information
Course Details
Course Code BUSANA 7000 Course Fundamentals of Business 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 Corequisites CORPFIN 7033 Assessment Exam/assignments/tests/tutorial work as prescribed at first lecture Course Staff
Course Coordinator: Mr Pide Lun
Course Instructors
Shihe Li
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
1. Explain the background of business analytics
2. Demonstrate programming methods to locate, warehouse and wrangle data
3. Utilize decision analysis as a means to build solutions for business queries
4. Relate ethical principles to the collection, storage and use of data by business and government
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.
2-3 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.
1-3 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.
1-3 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.
1-3 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.
3 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.
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Learning Resources
Required Resources
Resources necessary for each topic covered in the course are advertised in the MyUni course website.Recommended Resources
(e-book version) Camm, J. D., Cochran, J. J., Fry, M. J. and Ohlman, J. W., (2020). Business Analytics (4th ed.). Boston, MA: Cengage Learning. Enrolled students may be eligible for a discounted price offered by Cengage.
DataCamp.com. Available at
Wickham, H. and Grolemund, G., (2017). R for Data Science. Available at https://r4ds.had.co.nz/Online Learning
Please refer to the MyUni course website for online resources used in the course. -
Learning & Teaching Activities
Learning & Teaching Modes
This course will be taught entirely online and include a mixture of pre-prepared lecture material that students are expected to learn prior to live (online) workshop sessions with the students on a weekly basis.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.
Students should allocate up to eight hours per week of study time to do appropriate readings, solving set questions, problems,
and assignments in addition to attendance at scheduled class sessions.Learning Activities Summary
Topic Details (approximately follows week-by-week breakdown of classes)
1. Introduction to Business Analytics and R
2. Basic Data Manipulation
3. Introduction to Visual and Descriptive Analytics
4. Using Loops and Conditional Statements in R
5. Data Wrangling
6. Web Scraping
7. Writing efficient R code & Review of R packages
8. Decision Analysis and Making Data-Driven Decisions
9. Cluster Analysis
10. Introduction to Text Mining & Analysis
11. Statistical Inference
12. Cybersecurity and Ethics
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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 Task Task Type Due Weighting Learning Outcome DataCamp task Individual Week 4 10% 2 Online Test 1 Individual Week 7 25% 1 & 2 Online Test 2 Individual Week 13 35% 2, 3 & 4 Assignment Group Week 13 30% 1, 2 & 3 Assessment Related Requirements
Important information related to the assessments:
1. To pass this course, a cumulative score of at least 50% is required.Assessment Detail
DataCamp Task (Week 4):
Covering topics 2, 3, and 4
Online Test 1 (Week 7):
Covering topics 1 - 6
Online Test 2 (Week 13):
Covering topics 8 - 12
Group Assignment (Week 13):
Covering topics 1 - 11Submission
All assessment tasks must be submitted by the deadline.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.
The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
