Course Title: The Data Science Professional

Part A: Course Overview

Course Title: The Data Science Professional

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


171H School of Science


Sem 1 2021

Course Coordinator: Prof Flora Salim

Course Coordinator Phone: +61 3 9925 0291

Course Coordinator Email:

Course Coordinator Location: 14.08.09

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities


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

Upon successful completion of this course you should be able to:

  • CLO1 Apply relevant standards, ethical, privacy and governance considerations, and thus demonstrate an understanding of issues related to the practice of a Data Science professional.
  • CLO2 Analyse security and governance issues related to maintaining a data collection and apply appropriate standards and techniques to maintain these.
  • CLO3 Analyse and evaluate professional practice case studies, and to assess the work of peers.
  • CLO4 Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and oral presentations.
  • CLO5 Use research principles and choose appropriate methods to analyse, theorise and justify conclusions about new situations in Data Science professional practice and/or research.

You are expected to develop the following Program Learning Outcomes for MC267 Master of Data Science:

Enabling Knowledge (PLO1)

You will gain skills as you apply knowledge with creativity and initiative to new situations. In doing so, you will:

  • Demonstrate mastery of a body of knowledge that includes recent developments in computer science and information technology;
  • Understand and use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools;

Critical Analysis (PLO2)

You will learn to accurately and objectively examine, and critically investigate computer science, information technology (IT) and statistical concepts, evidence, theories or situations, in particular to:

  • Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems;
  • Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements;

Communication (PLO4)

You will learn to communicate effectively with a variety of audiences through a range of modes and media, in particular to:

  • Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.

Responsibility (PLO6)

You will be required to accept responsibility for your own learning and make informed decisions about judging and adopting appropriate behaviour in professional and social situations. This includes accepting the responsibility for independent life-long learning and a high level of accountability. Specifically, you will learn to:

  • Effectively apply relevant standards, ethical considerations, and an understanding of legal and privacy issues to designing software applications and IT systems;
  • Reflect on experience and improve your own future practice;

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

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 myRMIT 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

The assessment for this course comprises individual and group assignments, including a major project, presentations and peer reviews, as well as a list of Micro Creds to complete.Each of the assessment items requires you to demonstrate your knowledge of theoretical concepts, including application to new situations. Additionally, the assignments require you to research a scenario/problem, analyse it based on a professional framework, evaluate solutions, apply relevant standards, and create a professional report. Peer review involves oral and written feedback on the presentations delivered by other teams, as well as report(s) written by other students. The final project requires students to ideate, develop, and present an AI/Data Science-based idea and approach to a grand challenge faced by the society.

Note: This course has no hurdle requirements.

Assessment Task 1: AI/Data Science case study (group) – 20%
In this task, students will critically analyse and report (both oral presentation and written report) on an AI or DataScience case study, analysing the pitfalls of an existing AI system or digital solution. The assessment involves a presentation, a report, and peer review.  

This assessment task supports CLOs 1,2,3,4,5

Assessment Task 2: AI/Data Science for social good project (group) – 50%
In this task, students will critically analyse and report (both oral presentation and written report) on a chosen grand challenge faced by the society today. Students will apply design thinking and adopt an AI/Data science approach to solve the problem, focusing on AI for social good.The assessment involves a presentation, a report, and peer review, broken into two milestones: initial report, and final report and presentation.

This assessment task supportsCLOs 1, 2, 3, 4, 5

Assessment Task 3: Interviews and reflections (individual) – 25%
In conjunction with the group work (task 1 and 2), students need to submit a written reflection (5% each), with questions being given when they commence the task online. This will assess student’s contribution to their group project. At the end of the course, students will need to complete a recorded interview (15%) on practical case studies given to them. The recording will have a time limit.

This assessment task supports CLOs 1, 2, 3, 4, 5

Assessment Task 4: Micro-Credentials -- 5%
The micro-credentials will ensure completion of essential mini courses on academic integrity, ethical cities, and presenting user story.

This assessment task supports CLOs 2, 4