BUSINFO 703 : Data Visualisation for Business

Business and Economics

2023 Quarter Four (1238) (15 POINTS)

Course Prescription

Develops skills in unsupervised machine learning techniques, e.g., cluster analysis, factor analysis, and text mining, which enable unstructured and structured data to be leveraged to provide insights. Uses storytelling and visualisation techniques to translate data patterns in order to inform managerial decision-making.

Course Overview

Business analytics involves analysing and modelling data and communicating critical insights to the target audience. Appropriately visualising data and models allows analytics teams to explain challenges and solutions to various stakeholders. This course explores the power of storytelling via data visualisation. Students will apply theory and practical skills to retrieve and transform data to create data models, then design, develop and critique visual narratives based on data.

Building upon the unsupervised machine learning skills introduced in BUSINFO 700, this course explores methods and algorithms for clustering, association analysis, anomaly detection, dimensionality reduction and content generation. Subsequently, students will learn how to visualise supervised and unsupervised machine learning outcomes.

This course familiarises students with visualisation software (MS Power BI) and machine learning packages (using Python).

Course Requirements

Prerequisite: BUSINFO 700

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity

Learning Outcomes

By the end of this course, students will be able to:
  1. Critically evaluate and suggest improvements to visual representations of data (Capability 2 and 4.2)
  2. Identify and explain unethical instances of data visualisation (Capability 2 and 5.2)
  3. Recommend data modelling and transformation techniques to generate suitable visualisations (Capability 1 and 2)
  4. Generate a compelling narrative using data (Capability 1, 3, 4.2 and 5.1)
  5. Apply unsupervised machine learning techniques on small and large datasets (Capability 1, 2 and 5.1)
  6. Develop and deliver effective, engaging presentations to a specified audience (Capability 1, 3, 4.1, 4.2 and 4.3)

Assessments

Assessment Type Percentage Classification
Assignment One 30% Individual Coursework
Test 40% Individual Test
Assignment Two 30% Group Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
Assignment One
Test
Assignment Two

Workload Expectations

This is a 15-point course; students should expect to spend around 15 hours per week over ten weeks.

Each week comprises a 2-hour lecture and a 2-hour lab component.

In addition to attending lectures and labs, students should be prepared to spend time on pre-reading, practice, self-assessments, preparation, assignments and a test.

Delivery Mode

Campus Experience

At scheduled activities, including lectures and labs, attendance is expected to complete the course components.

Lectures will be available as recordings (barring technical issues). Other learning activities, including labs, will not be available as recordings.

The course does not include live online events.

Attendance on campus is required for the test.

All activities for the course are scheduled as a standard weekly timetable.

Any changes to delivery from in-person to online mode will be announced on Canvas as soon as feasible.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Please refer to 'Reading Lists' in Canvas for course-specific content.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Based on the student feedback in the past runs, the following changes are planned for this run:

  • A practice test to familiarise students with expectations regarding examinable content
  • Aligning lab content with lecture content every week
  • Piazza setup for student collaboration and faster resolution of queries

Other Information

This course uses Power BI Desktop (free download) for visualisation and Google Colab (free online access) for unsupervised machine learning.

Lab computers (and FlexIT) have Power BI Desktop software pre-installed. Students should note that this software requires Windows, so Mac users may need to use Parallels or Docker to install it on personal laptops.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Since this course deals with visualisation, colour-blind participants might find some colour palettes challenging. While we will try to utilise 'colour-blind friendly' palettes during the test and labs, students may be exposed to 'real-world' examples that may not be as accommodating. Please reach out to the teaching team if you need further support.

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 13/07/2023 11:42 a.m.