BUSAN 302 : Big Data and Machine Learning

Business and Economics

2022 Semester Two (1225) (15 POINTS)

Course Prescription

Provides essential skills to build data-driven digital innovations that augment business decisions. This involves identifying problems faced by different groups of individuals from different spheres of life, analysing the problem space and data needs, building a prototype for a selected design, and using machine learning tools and cloud-based big data analytics.

Course Overview

The purpose of this course is to acquire knowledge to apply appropriate Big Data, 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; and finally, to specify a solid plan for building and evaluating the system designed. No executable implementation of the system is expected. A basic knowledge of Big Data and tools will be given. 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
Capability 5: Independence and Integrity
Graduate Profile: Bachelor of Commerce

Learning Outcomes

By the end of this course, students will be able to:
  1. Analyse information needs of an organization, 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, Big Data, Data Analytics, Data Cleansing and storage, together with a database language. (Capability 1)
  3. Explain and apply concepts and principles related to Big Data, and Machine Learning. (Capability 1, 2, 3 and 4.2)
  4. Design a Prototype in a collaborative and networked environment using state of the art Big Data and Machine Learning tools (Capability 1, 2, 3, 4.2, 5.1 and 5.2)
  5. Critically evaluate and suggest a design solution to a decision problem in an organization taking into consideration human, organizational, and technical issues, utilizing discussed technologies (Capability 1, 2, 3, 4.2, 5.1 and 5.2)

Assessments

Assessment Type Percentage Classification
Assignments 15% Individual Coursework
Assignments 25% Group Coursework
Quizzes 20% Individual Coursework
Reports 40% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5
Assignments
Assignments
Quizzes
Reports

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.

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 / tutorials.

Attendance on campus is required for the quiz.

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 31/05/2022 10:16 p.m.