CORPFIN 2503 - Business Data Analytics
North Terrace Campus - Semester 2 - 2024
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General Course Information
Course Details
Course Code CORPFIN 2503 Course Business Data Analytics Coordinating Unit Finance and Banking Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites ECON 1008 or ECON 1008OUA Assessment Mid-term test, group assignment and final examination 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 Course Planner.
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course, students will be able to:
1. Explain the background of business analytics.2. Demonstrate programming methods to locate, warehouse and wrangle data.
3. Utilise data-driven means to build solutions for business queries.
4. Relate ethical principles to the collection, storage and use of data.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.
1-4 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.
1-4 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.
1-4 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.
1-4 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.
1-4 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
Recommended Resources
(e-book version) Camm, J. D., Cochran, J. J., Fry, M. J. and Ohlman, J. W., (2023). Business Analytics (5th ed.). Boston, MA: Cengage Learning. Enrolled students may be eligible for a discounted price offered by Cengage.
DataCamp.com. Available at https://www.datacamp.com
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
The approach in this course is to first establish the basic knowledge of analytical tools (e.g., SAS® software) and then to build upon these to analyse real-world issues. This will be done through lectures, tutorials, assignment, mid-term test, and examination.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
Topic Details (approximately follows week-by-week breakdown of classes)
Topic 1 - Introduction to Business Analytics
Topic 2 - Introduction to R and R studio
Topic 3 - R Essentials
Topic 4 - Data Wrangling
Topic 5 - Basic Statistics
Topic 6 - Visualisation in R
Topic 7 - Review of R packages
Topic 8 - Decision Analysis and Making Data-Driven Decisions
Topic 9 - Cluster Analysis
Topic 10 - Introduction to Text Mining & Analysis
Topic 11 - Statistical Inference
Topic 12 - Cybersecurity and Ethics -
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 Individual Week 4
10% 2 Test 1 Individual Week 7 25% 1, 2 Group Assignment Group Week 9 - Week 13 30% 1, 2, 3, 4 Test 2 Individual Week 13 35% 3, 4 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
Test 1 (Week 7):
Covering topics 1 - 6
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 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.
