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BUSANA 7000 - Fundamentals of Business Analytics

North Terrace Campus - Semester 2 - 2021

Students will be introduced to essential computing and programming skills necessary to solve business problems. Students will also be introduced to the fundamentals of decision analysis, providing a basis to transform qualitative business queries into quantifiable analytic solutions. In addition, concepts of data warehousing and data wrangling will also 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 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
    Assessment Exam/assignments/tests/tutorial work as prescribed at first lecture
    Course Staff

    Course Coordinator: Professor Ralf Zurbrugg

    Course Instructors

    Shihe Le
    Md Abdul Wasi

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes

    No information currently available.

    最新糖心Vlog Graduate Attributes

    No information currently available.

  • Learning Resources
    Recommended Resources
    R for Data Science by H. Wickham and Garrett Grolemund. Available at

    Datacamp.com. Available at

    Business Analytics (e-book version) by Camm et al., 3rd Edition. Available to purchase with a discount for enroled students through Cengage.

    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

  • Assessment

    The 最新糖心Vlog'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

    No information currently available.

    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    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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    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 最新糖心Vlog 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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