BUSANA 7003 - Business Analytics Project
North Terrace Campus - Semester 2 - 2024
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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 and BUSANA 7001 and BUSANA 7002 and 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 .
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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.
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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 SchryversOnline Learning
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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:
- 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
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 1Individual 10%
2
Short assignment 2Individual 10% 1,2,3
Interim project presentationIndividual 30% 1,2,3
Group assessment of the Data Analytics ProjectGroup 25% 1,2,3
Individual assessment of the Data Analytics ProjectIndividual 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 .
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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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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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
- 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鈥檚 disciplinary procedures.
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