FORENSIC 708 : Special Topic: Forensic Science in a Digital World
Science
2024 Semester Two (1245) (15 POINTS)
Course Prescription
Course Overview
Although primarily intended for students studying Forensic Science, this course is suitable for any post-graduate student with an undergraduate degree in science that has an interest in understanding the role of data science and statistics in forensic science. The skills developed in this course will be particularly useful for those wishing to pursue a career applying data science approaches to the natural sciences in multiple sectors and research areas. It is not intended that students will emerge from this course with in-depth data science skills, but rather a good understanding of the principles and applications of data science and statistics in forensic science.
A key focus will be on the management and security of data and good ethical practise applied to data. Methods may include machine learning, artificial intelligence, Bayesian inference, data visualisation. Applications may include wastewater analysis, DNA sequencing, drug identification, biometrics, and crime detection and prevention.
Prior knowledge of basic statistics is assumed. Familiarity with statistical programming language R will be beneficial. It is not anticipated that students will be experts in either.
Capabilities Developed in this Course
| Capability 1: | People and Place |
| Capability 2: | Sustainability |
| Capability 3: | Knowledge and Practice |
| Capability 4: | Critical Thinking |
| Capability 5: | Solution Seeking |
| Capability 6: | Communication |
| Capability 7: | Collaboration |
| Capability 8: | Ethics and Professionalism |
Learning Outcomes
- Demonstrate an understanding of of and apply Māori data sovereignty principles and data management best practises to data. (Capability 1, 2, 3, 7 and 8)
- Describe and apply the principles of good experimental design (Capability 2, 3, 4 and 8)
- Learn and apply Statistical methods, algorithms and models to data to find patterns and gain insights from data (Capability 2, 3, 4 and 5)
- Develop and apply data visualisation methodology to forensic data (Capability 2, 3, 4 and 5)
- Be able to undertake bioinformatic methods to DNA sequence data for sequence alignment and variant detection (Capability 1, 3, 4 and 5)
- Explain and communicate complex data science concepts to a range of audiences using a variety of media (Capability 3, 6 and 7)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Workshops | 40% | Group & Individual Coursework |
| Presentation | 20% | Group & Individual Coursework |
| Assignments | 40% | Individual Coursework |
| 3 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | |||||
| Workshops | ||||||||||
| Presentation | ||||||||||
| Assignments | ||||||||||
- Principles of good data management
- Statistical principles for analysing data
- Data science applications
- Bioinformatics
Tuākana
Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials,
one-on-one sessions, test and exam preparation and more. Explore your options at
https://www.auckland.ac.nz/en/science/study-with-us/pacific-in-our-faculty.html
https://www.auckland.ac.nz/en/science/study-with-us/maori-in-our-faculty.html
Special Requirements
Attendance at the workshops and completion of the assignments is a compulsory part of this course.
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 24 hours of lectures, a 18 hours of workshops and tutorials, 40 hours of reading and thinking about the content and 68 hours of work on assignments. There are no exams or tests for this course.
Delivery Mode
Campus Experience or Online
This course is offered in two delivery modes:
Campus Experience
Attendance is required at scheduled activities including workshops, student presentations and tutorials to complete components of the course.
Lectures will be available as recordings. Other learning activities including workshops and presentations will not be available as recordings.
The course may include live online events including group discussions and tutorials.
The activities for the course are scheduled as a [standard weekly timetable/block delivery].
Online
Attendance is expected at scheduled online activities including tutorials.
Tutorials may include live online events including group discussions and these will be recorded.
Where possible, study material will be released progressively throughout the course.
This course runs to the University semester/quarter timetable and all the associated completion dates and deadlines will apply.
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
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.
This is a new course for 2024. Student feedback will be critical in evaluating its successful implementation.
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 for potential plagiarism or other forms of academic misconduct, 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.
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.
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.
The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.
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.