INFOSYS 722 : Data Mining and Big Data
Business and Economics
2020 Semester Two (1205) (15 POINTS)
Course Prescription
Course Overview
- Decision Making, Big Data, Machine Learning, and Data Mining – foundational concepts.
- Big Data and Data Mining Computing Environment – hardware, software, distributed systems and analytical tools.
- Turning data into insights that deliver value - through methodologies, processes, algorithms and approaches for big data analytics.
- Big Data and Data Mining in Practice – how the world’s most successful companies use big data analytics to deliver extraordinary results.
- Apply the knowledge gained through the design and implementation of a prototype.
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 |
| Capability 6: | Social and Environmental Responsibilities |
Learning Outcomes
- Understand and apply foundational concepts of Decision Making and Decision Support from a variety of disciplines and understand and apply fundamental principles of Data Mining, Machine Learning, and Big Data. (Capability 1, 4.2 and 4.3)
- Compare, contrast and synthesize a process for Data Mining and understand the key components of the computing environment for Big Data and Data Mining including hardware, software, distributed systems, and analytical tools. (Capability 1, 2, 3 and 4.2)
- Understand the process of turning data into insights that deliver value using predictive modelling, segmentation, incremental response modelling, time series data mining, text analytics, and recommendations. (Capability 1, 2 and 3)
- Understand, discuss, and reflect on how successful companies have applied big data and data mining methodologies, algorithms, and enabling technologies to deliver extraordinary results and value. (Capability 2, 3 and 4.3)
- Design and implement a prototypical Big Data Analytics Solution to address one of the 17 Sustainable Development Goals (https://sdgsinaction.com/) of the UN or a decision making situation facing an organization of your choice. (Capability 2, 3 and 6)
- Write a research paper to reflect the design science research practice followed in the course. The paper needs to detail (a) the practical problem (b) the research problem (c) the research objectives (d) the literature that explores potential solutions and methodologies that addresses your objectives (e) the research methodology adopted (f) the design of the processes that converts data into insights and (g) the description of the implementation using various algorithms and enabling technologies (h) your interpretation of the patterns and results and (i) your proposed actions based on the discovered knowledge. Gain experience of academic writing following formatting guidelines of an A* journal and giving citations as required. (Capability 2, 3, 4.2 and 5.1)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Iteration 1: Proposal | Individual Coursework | |
| Iteration 2: ISAS | 25% | Individual Coursework |
| Iteration 3: OSAS | 25% | Individual Coursework |
| Iteration 4: BDAS | 30% | Individual Coursework |
| Research Paper | 20% | Individual Coursework |
| 5 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | |||||
| Iteration 1: Proposal | ||||||||||
| Iteration 2: ISAS | ||||||||||
| Iteration 3: OSAS | ||||||||||
| Iteration 4: BDAS | ||||||||||
| Research Paper | ||||||||||
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 36 hours of lectures, 22 hours of lab/tutorials, 92 hours of reading, programming, and working on assignments.
Learning Resources
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.