Part A: Course Overview
Course Title: The Data Science Professional
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
COSC2792 |
City Campus |
Postgraduate |
171H School of Science |
Face-to-Face |
Sem 1 2021 |
COSC2792 |
City Campus |
Postgraduate |
175H Computing Technologies |
Face-to-Face |
Sem 1 2023, Sem 1 2024, Sem 1 2025 |
Course Coordinator: Professor Feng Xia
Course Coordinator Phone: -
Course Coordinator Email: feng.xia@rmit.edu.au
Course Coordinator Location: -
Course Coordinator Availability: By appointment, by email
Pre-requisite Courses and Assumed Knowledge and Capabilities
None
Course Description
We live in an era of rapid technological change which will increasingly see intelligent/data-driven computer systems take over control and decision making in diverse areas from business, law and security to healthcare and food production. This blended style course aims to equip you with the philosophical and ethical foundation needed by AI/Data Science professionals who will be at the forefront of transformation through AI and Data Science.
The role of an AI Professional and Data Scientist is an increasingly ubiquitous job role in many organisations of varying size. As well as a variety of technical skills, the practicing AI/Data Scientist also requires knowledge and awareness of key ethical, privacy and governance considerations for managing data, and data security and related techniques for implementing such. Moreover, a Data Scientist in an organisation must be an effective communicator both with other business units and with management, displaying an ability to both understand business needs and to communicate analyses effectively to inform decision making.
This course will: introduce concepts related to legal, ethical, privacy, governance issues for data collections; describe techniques for effectively managing such issues; provide you with techniques for effective communication and other considerations required of the AI practitioner or Data Scientist in a professional organisation.
The course will also: introduce the history of AI and Data Science, fundamental philosophical issues; consider current ethical issues in AI and building a responsible data science practice; explore the wider legal, social and economic impacts of widescale deployment of intelligent systems, and teach students to be innovative thinkers, leveraging the available datasets for social good.
Objectives/Learning Outcomes/Capability Development
Program Learning Outcomes
This course contributes to the program learning outcomes for the following program(s):
MC267 - Master of Data Science
PLO 1 Enabling Knowledge
PLO 2 Critical Analysis
PLO 4 Communication
PLO 6 Responsibility
For more information on the program learning outcomes for your program, please see the program guide.
Upon successful completion of this course you should be able to:
- Apply relevant standards, ethical, social, privacy and governance considerations, and thus demonstrate an understanding of issues related to the practice of an AI and Data Science professional.
- Analyse and discuss social impact and professional issues related to AI and Data Science (DS) and the deployment of AI and DS systems. In particular, evaluate the implications of delegating control and decision making to AI and DS systems, including issues on fairness, bias, transparency, accountability, and explainability of AI and DS systems.
- Analyse and evaluate professional practice case studies, and to assess the work of peers.
- Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and oral presentations.
- Use research principles and choose appropriate methods to analyse, theorise and justify conclusions about new situations in AI and Data Science professional practice and/or research.
Overview of Learning Activities
The learning activities included in this course are:
- Key concepts will be provided online (mix of text, audios, videos).
- Workshop sessions are interactive and discussion based, where students will be put into small groups to analyse existing AI and Data Science case studies.
- There will also be group presentation and peer review during the workshop sessions. These allow exploration of concepts with teaching staff and peers, and give feedback on students' progress and understanding.
- Students are expected to go through the given materials before the lectorials, as well as conducting self-study to work through and research beyond the course as presented in classes and learning materials.
- Assignments, as described in Overview of Assessment, requiring an integrated understanding of the subject matter.
Overview of Learning Resources
The course is supported by the Canvas learning management system which provides specific learning resources. See also the RMIT Library Guide at http://rmit.libguides.com/compsci
You will make use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through Canvas and may be provided with copies of additional materials in class or via email. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.
Overview of Assessment
Note: This course has no hurdle requirements.
Assessment Tasks
Assessment Task 1: AI/Data Science case study (group)
Weighting: 20%
This assessment task supports CLOs 1,2,3,4,5
Assessment Task 2: AI/Data Science for social good project (group)
Weighting: 45%
This assessment task supports CLOs 1, 2, 3, 4, 5
Assessment Task 3: Interviews and reflections (individual)
Weighting: 30%
This assessment task supports CLOs 1, 2, 3, 4, 5
Assessment Task 4: Micro-Credentials
Weighting: 5%
This assessment task supports CLOs 2, 4
If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.