BUSINFO 704 : Predictive Business Analytics

Business and Economics

2022 Quarter Four (1228) (15 POINTS)

Course Prescription

Provides insights into the most commonly used supervised machine learning techniques, e.g., linear regression, logistic regression, random forest techniques, neural networks. Applies these techniques to model data for predicting relevant events. Addresses caveats of the techniques and how to evaluate model validity and outcomes.

Course Overview

Predictive analytics is being used throughout the business world. After this course, students will have an understanding of different machine learning techniques and how to appropriately apply these techniques to solve business problems.
By the end of the course, students will: Be familiar with linear regression logistic regression, random forest techniques and neural networks and will understand when to apply these models appropriately. Be able to detect errors in models and provide well-reasoned arguments about model validity.  Interpret and communicate the outcomes of predictive modelling in managerial terminology. The R programming language will be used throughout the course to give students experience in applying algorithms to data.


Course Requirements

No pre-requisites or restrictions

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. Outline key analytical modelling techniques for decision making, considering the key trade-offs between different modelling tools (Capability 1 and 4.2)
  2. Manage and assess the trade-off between modelling assumptions and tractability in authentic case studies. (Capability 1)
  3. Demonstrate critical and creative thinking in being able to formulate, justify, and evaluate models for decision making. (Capability 2 and 3)
  4. Present and articulate opinions about key modelling assumptions and likely decision biases in using the models. (Capability 1 and 5.1)
  5. Be able to work in a team to complete an open-ended modelling project. (Capability 3 and 4.3)

Assessments

Assessment Type Percentage Classification
Labs 40% Individual Coursework
Quiz 10% Individual Coursework
Group Assignment 1 15% Group Coursework
Group Assignment 2 25% Group & Individual Coursework
Reflection 10% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5
Labs
Quiz
Group Assignment 1
Group Assignment 2
Reflection

Workload Expectations

Each week the class will meet in class for two hours and then for two more in the computer labs. Class time will be used for a combination of in-class exercises, lectures, and applied discussions of case studies. In addition to attending classes and labs, students should be prepared to spend about another nine hours per week on activities related to this course. These activities include carrying out the required readings and class preparation activities, preparing assignments and the final project, and preparing for the mid-term exam. The course as a whole represents approximately 150 hours of study.

Delivery Mode

Campus Experience

Attendance is required at scheduled activities including labs to receive credit for components of the course.
The course will include live online events including group discussions and tutorials.
Attendance on campus is required for the quiz, if the university is open. Otherwise, the quiz will be arranged online.
The activities for the course are scheduled as a standard weekly timetable.

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.

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.

 

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

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 12/09/2022 06:37 a.m.