DATASCI 792A/B : Dissertation

Science

2020 Semester One (1203) / Semester Two (1205) (45 POINTS)

Course Prescription

No prescription

Course Overview

Students will work with their supervisor(s) (from Statistics, Computer Science, or both) on a research project in Data Science. By the end of the two semesters, they will write a dissertation (typically 25-50 pages) describing their research. Students whose primary supervisor is in the Statistics department will also present a 15 minute talk on the department's postgraduate talks day.


Course Requirements

To complete this course students must enrol in DATASCI 792 A and B, or DATASCI 792

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
Graduate Profile: Master of Data Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Demonstrate knowledge of existing work in the research area (Capability 1, 2, 3, 4, 5 and 6)
  2. Apply data science knowledge and techniques to tackle the problems in the research project (Capability 1, 2 and 3)
  3. Write a dissertation about their research (Capability 1, 4 and 5)
  4. Learn new skills/techniques that are needed to carry out the research project (Capability 1, 2, 3 and 4)

Assessments

Assessment Type Percentage Classification
Dissertation 100% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4
Dissertation

Learning Resources

  • Library access and resources
  • Internet access (e.g., to published papers potentially behind a paywall)
  • Access to a computer for computational work and/or writing
  • Additional resources specific to the project (e.g., access to proprietary data)

Special Requirements

None

Workload Expectations

This is a 45 point project and students should expect to spend about 360 hours on it overall. The nature of that work will vary throughout the project but typically includes reading and practice at the beginning, development and application of data science methods to the research questions in the middle, and writing at the end.

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.

Copyright

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

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

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course 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.

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

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 28/06/2020 10:58 p.m.