BUSAN 302 : Data Mining and Decision Support

Business and Economics

2020 Semester One (1203) (15 POINTS)

Course Prescription

Business modelling to solve challenging problems faced by identified stakeholders. Students will explore these challenges by decomposing unstructured complex problems, evaluating and prioritising alternatives, allocating scarce resources, and justifying and defending solutions provided.

Course Overview

The purpose of this course is to acquire knowledge to apply appropriate Data Mining / Machine Learning techniques to gain information insights to various problems faced by an organisation. The focus of this course is to firstly identify a problem from a given case study that needs solving; secondly, consider various possible designs and select the most appropriate solution; thirdly, design the architecture for a system based on proven information systems frameworks, such as decision support systems and recommender systems frameworks; and finally, to specify a solid plan for building and evaluating the system designed. No implementation of the system is expected. Knowledge and experience of  state of the art machine learning tools, from a key vendor, will be gained in the labs with opportunities and encouragement to explore other tools.

Course Requirements

Prerequisite: 15 points from BUSAN 201, INFOMGMT 292, INFOSYS 222 Restriction: INFOMGMT 393, INFOSYS 330

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Graduate Profile: Bachelor of Commerce

Learning Outcomes

By the end of this course, students will be able to:
  1. Analyse information needs of an organisation, department or functional division, and individual stakeholders involved in the business. (Capability 1, 2 and 3)
  2. Acquire knowledge on how to learn technology such as, data analytics data warehousing and a database language (Capability 1)
  3. Explain and apply concepts and principles related to decision support, data mining and data warehousing. (Capability 1, 2, 3 and 4.2)
  4. Design and implement a Decision Support System in a collaborative and networked environment using state of the art data warehousing, data mining and business intelligence tools. (Capability 1, 2, 3 and 4.2)
  5. Conduct research on one aspect of data mining and decision support and then suggest a design solution to a decision problem taking into consideration human, organizational, and technical issues, utilizing discussed technologies (Capability 1, 2 and 4.2)

Assessments

Assessment Type Percentage Classification
Lab Assignment: T-SQL 10% Individual Coursework
Assignment 1: Data Warehousing 15% Individual Coursework
Assignment 2: Machine Learning and Emerging Technologies 25% Individual Coursework
Final Exam 50% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5
Lab Assignment: T-SQL
Assignment 1: Data Warehousing
Assignment 2: Machine Learning and Emerging Technologies
Final Exam

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures, a 2 hour tutorial, 3 hours of reading and thinking about the content and 2 hours of work on assignments and/or test preparation per week.

Learning Resources

“Sams Teach Yourself SQL in 10 Minutes”, By Ben Forta.  ISBN-13: 978-0672336072; ISBN-10: 0672336073

"Sams Teach Yourself Transact-SQL in 21 Days ", by Ronald R. Plew (Author), Bryan Morgan (Author), Jeff Perkins (Author), Ryan K. Stephens (Author, Editor) ISBN-13: 978-0672311109 ; ISBN-10: 0672311100

Other Information

Good reading:
Dhar V., R Stein, 1997. “Seven Methods for transforming corporate data in to Business Intelligence”, Prentice Hall, NJ. (out of publication – available in the library).

Digital 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.

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.

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 at http://disability.auckland.ac.nz

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.

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.

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 you may be asked to submit your 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. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 05/02/2020 12:17 p.m.