最新糖心Vlog

BUSANA 7003 - Business Analytics Project

North Terrace Campus - Semester 2 - 2023

This course involves students choosing an applied problem to analyse and providing high-level data analytics reports to assist in a solution. This course acts as a functional capstone course for Business Analytics, providing students with the opportunity to evidence their acquisition of the majority of the award learning outcomes. The course will provide students with an applied project, exposure to real-world data problems, and experience in professionally presenting their work.

  • General Course Information
    Course Details
    Course Code BUSANA 7003
    Course Business Analytics Project
    Coordinating Unit Finance and Banking
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 6
    Contact Up to 5 hours per week
    Available for Study Abroad and Exchange N
    Prerequisites BUSANA 7000, BUSANA 7001, BUSANA 7002, CORPFIN 7033
    Assessment Research project
    Course Staff

    Course Coordinator: Ms Marta Khomyn

    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. Demonstrate the analytics knowledge and skills obtained throughout the programme to recalibrate solutions to a business problem.
    2. Demonstrate academic learning and practical challenges in implementing data analytics in an organisation.
    3. Communicate the results of a business analytics project.
    最新糖心Vlog Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    最新糖心Vlog 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

    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.

    1,2

    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

    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.

    1,2,3

    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 7: Digital capabilities

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

    1,2,3

    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.

    1,2,3
  • Learning Resources
    Required Resources
    Data Analytics Made Accessible: 2022 edition, Anil Maheshwari. 


    Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations: 2016 edition, Scott Berinato. 


    Bad Data: Why We Measure the Wrong Things and Often Miss the Metrics That Matter: 2020 edition, Peter Schryvers
    Online Learning
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The Business Analytics Project course relies on face-to-face teaching and modern digital learning techniques, with a three-hour interactive seminars and two-hour workshops scheduled every week for the duration of the course. Attendance and active participation both seminars and workshops are crucial, as they enhance understanding and assessment performance, and foster valuable skills for communicating and interpreting the data.
    Workload

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

    The information below is meant as guidance to help students achieve an appropriatre level of learning quality within the Business Analytics Project course.

    Full-time students at the 最新糖心Vlog, typically enrolled in four courses equating to 12 units per term, are expected to dedicate around 48 hours per week to their studies. In relation to the Business Analytics Project course, this translates to roughly 24 hours of independent study outside of the scheduled classes, facilitating deeper exploration of course materials and concepts.
    Learning Activities Summary
    The Business Analytics Project topics aim to replicate the real-life experience of implementing a data analytics project in an organisation. The course objective is directly related to successfully addressing the business objectives of a company using data analytics tools. The project relies heavily on varied data sources, including a WRDS and TRTH databases.

    The schedule of lecture topics for this course is as follows:

    Topic 1: Project management for Business Analytics


    Topic 2: Presenting data insights effectively


    Topic 3: Sourcing, cleaning, and enriching the data


    Topic 4: Retrieving and managing data with SQL


    Topic 5: Supervised machine learning


    Topic 6: Unsupervised machine learning


    Topic 7: Deep learning and neural networks


    Topic 8: Interim project presentations


    Topic 9: Lying with the data


    Topic 10: Running experiments


    Topic 11: The ethics of working with the data


    Topic 12: Final project presentations




  • 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
    The assessment for this course includes two short assignements (each worth 10% of the grade), an interim Data Analytics Project presentation (30% of the grade), and a final Data Analytics Project (50% of the grade). The Data Analytics Project is based on a real life industry scenario.
    Assessment Related Requirements


    Assessment Asessment Description
    Short assignment 1 In this short assignment, you will be presented with several datasets, and will be asked to clean the data, investigate the outliers, and perform basic exploratory analysis. The assessment will ask you to provide short answers to specific data-related questions.

    Short assignment 2 In this short assignment, you will be presented with case studies related to Ethics in Data Analytics projects. You will have to prepare a short presentation analyzing specific case study questions. You will present in class.

    Interim project presentation In this presentation, you will deliver the summary of preliminary results of the Data Analytics Project, including data visualizations and model outputs.

    Industry partner assessment of the Data Analytics Project  In this assessment, you will deliver the final results of the Data Analytics Project to the industry partner, including data visualizations and model outputs. You will also submit the final project report, input datasets, and code. The industry partner assessment will focus on how well your team as a whole answered the data analytics question of interest.

    Academic assessment of the Data Analytics Project  In this assessment, you will deliver the final results of the Data Analytics Project for academic examination, including data visualizations and model outputs.. You will also submit the final project report, input datasets, and code. The academic examination will assess each team member individually, based on their contribution to answering the data analytics question of the Project.

    Assessment Detail
    Assessment task Grading type Weighting (% of the total grade) Learning outcome

    Short assignment 1

    Individual 10%
    2

    Short assignment 2

    Individual 10% 1,2,3

    Interim project presentation

    Individual 30% 1,2,3

    Group assessment of the Data Analytics Project

    Group 25% 1,2,3

    Individual assessment of the Data Analytics Project

    Individual 25% 1,2,3



    Submission
    All assessments for this course must be submitted via MyUni, using the file formats specified in the assessment descriptions. 
    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 最新糖心Vlog 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 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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