成人大片

BUSANA 7004 - Business Analytics for Managers

North Terrace Campus - Trimester 3 - 2023

An under-utilised resource within many organisations is the data it possesses. This is despite the fact that good data decisions can bring a significant competitive advantage to the company. This course will help you understand the basic concepts of data analytics and focus on applying analytics techniques to improve decision-making and drive business impact. In doing so, the course will cover the three pillars of business analytics (descriptive, predictive and prescriptive analytics) as well as introduce the role that artificial intelligence and machine learning play in extracting value from big data.

  • General Course Information
    Course Details
    Course Code BUSANA 7004
    Course Business Analytics for Managers
    Coordinating Unit Adelaide Business School
    Term Trimester 3
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Course Staff

    Course Coordinator: Dr Ilker Cingillioglu

    Course Timetable

    The full timetable of all activities for this course can be accessed from .

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

    1 Utilise basic statistical theory to represent and solve business data problems.
    2 Construct and test hypotheses.
    3 Understand where and how both structured and unstructured data can be used to provide business solutions.
    4 Be competent at extracting business data and representing it in a meaningful manner.
    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.

    3,4

    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.

    3,4

    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.

    4

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    3,4
  • Learning Resources
    Required Resources
    This course uses a number of resources. A reference text is also recommended to students in the class with details made available in the course webpages. 
  • Learning & Teaching Activities
    Learning & Teaching Modes
    In this course a mix of learning methods, including topic presentations, interactive discussion, the solving of assigned questions and problems, and sharing of business experiences and observation is used.
    Workload

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

    Three intensive classes are spread across three months, with each intensive consisting of 12 hours of teaching. 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
    Module 1 - The Value of Data Science
    Module 2 - The Data Pipeline and Ethics
    Module 3 - Descriptive and Visual Analytics
    Module 4 - Data Wrangling
    Module 5 - Statistical Inference
    Module 6 - Supervised Learning
    Module 7 - Unsupervised Learning
    Module 8 - Deep Learning
    Specific Course Requirements
    Access to a laptop and also Microsoft Excel.
  • 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 Date
    Course Learning Outcome(s) being assessed
    Assessment
    Weighting
    Homework Formative and Summative

    Throughout the teaching period with deadlines set for each piece of work a day before the following teaching session.

    1,2,3 30%
    Research Assignment Summative Two weeks following the last class 1,2,3, 4 40%
    Online Test Summative Last class 1,2,3 30%
    Assessment Detail
    Homework: Short exercises, mostly done online, to reinforce the content taught in the different teaching sessions.
    Research Assignment: Students choose a business-analytics project that suits their interests and are required to deliver a formal report.
    Online Test: Summative test where students are given data and must provide answers to a set of questions.
    Submission
    All submissions occur online either through the course's MyUni webpages or through the course textbook publisher's education gateway.
    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 .

  • 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鈥檚 disciplinary procedures.

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