BUSANA 7000 - Fundamentals of Business Analytics

North Terrace Campus - Semester 1 - 2024

Students will be introduced to essential computing and programming skills necessary to solve business analytic problems. Students will also be introduced to the fundamentals of data-driven decision making, providing a basis to transform qualitative business queries into quantifiable analytic solutions. In addition, the concepts of data warehousing and data wrangling will be covered as well as topics on cybersecurity and ethics.

  • General Course Information
    Course Details
    Course Code BUSANA 7000
    Course Fundamentals of Business 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
    Corequisites CORPFIN 7033 - can be taken as a Pre-Requisite
    Course Description Students will be introduced to essential computing and programming skills necessary to solve business analytic problems. Students will also be introduced to the fundamentals of data-driven decision making, providing a basis to transform qualitative business queries into quantifiable analytic solutions. In addition, the concepts of data warehousing and data wrangling will be covered as well as topics on cybersecurity and ethics.
    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.

  • 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.


    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.

  • 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., (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
    This course will be delivered via a 3-hour seminar each week. One half of a session will be used for a lecture on a given topic and the other half of the session will be devoted to tutorial exercises. It is important that students review the lecture material beforehand such that it enables them to practice the exercises and participate during the tutorial sessions.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    As a guide, students are expected to spend 9 hours each week for this course. This includes attending the face-to-face classes, completing the online components of the course, and the time required for self-directed study.
    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 Basic Data Manipulation
    Topic 3 - Introduction to Visual and Descriptive Analytics
    Topic 4 - Using Loops and Conditional Statements in R
    Topic 5 - Data Wrangling
    Topic 6 - Web Scraping
    Topic 7 - Writing efficient R code & 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:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Due Weighting Learning Outcome
    DataCamp task Individual

    Week 4

    10% 2
    Test 1 Individual Week 7 25% 1, and 2
    Test 2 Individual Week 13 35% 2, 3, and 4
    Assignment Group Week 13 30% 1, 2, and 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

    Test 1 (Week 7):
    Covering topics 1 - 6

    Test 2 (Week 13):
    Covering topics 8 - 12

    Group Assignment (Week 13):
    Covering topics 1 - 11
    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.

  • 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.

  • Student Support
  • Policies & Guidelines
  • 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.