Course Title: The Data Science Professional

Part A: Course Overview

Course Title: The Data Science Professional

Credit Points: 12.00

Course Coordinator: Prof Lawrence Cavedon

Course Coordinator Phone: +61 3 9925 2325

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

The role of a Data Scientist is an increasingly ubiquitous job role in many orgnaisations of varying size. As well as a variety of technical skills, the practicing 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 Data Scientist in a professional organisation.

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 explained in lectures, classes or online where syllabus material will also be presented and the subject matter will be illustrated with demonstrations, examples and case studies;
  • Tutorials and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide practice in the application of theory and procedures, allow exploration of concepts with teaching staff and peers, and give feedback on your progress and understanding;
  • Assignments, as described in Overview of Assessment (below), requiring an integrated understanding of the subject matter; and
  • Private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual and technical problems.

Total study hours

A total of 120 hours of study is expected during this course, comprising:

Teacher-directed hours (48 hours): lectures, tutorials and laboratory sessions. Each week there will be 2 hours of lectures and 2 hours of tutorial work. You are encouraged to participate during lectures through asking questions, commenting on the lecture material based on your own experiences and through presenting solutions to written exercises. The sessions will introduce you to the tools necessary to undertake the assignment work.

Student-directed hours (72 hours): You are expected to be self-directed, studying independently outside class.

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, presentations and peer reviews, lab tests and a formal examination.

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 given scenario/problem, analyse it based on a professional framework, evaluate solutions, apply relevant standards, and create a professional report. Peer review will involve written feedback on the report(s) written by other teams. The lab tests involve analysis of case studies, group discussion with a moderator who gives feedback on communication dynamics, debates and role-play scenarios.

Note: This course has no hurdle requirements.


Assessment Tasks

Assessment Task 1: Class assessments

There will be up to 6 small assessment tasks, including short tests, individual and group oral or written presentations

Weighting 20%

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

Assessment Task 2: Major Assignment

In this task, you will design, implement, critically analyse and report on a substantial Data Science application.

Weighting 30%

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

Assessment Task 3: End-of-semester Examination

Weighting 50%

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