Course Title: The Data Science Professional

Part A: Course Overview

Course Title: The Data Science Professional

Credit Points: 12.00

Important Information:

Please note that this course may have compulsory in-person attendance requirements for some teaching activities. 

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption. 

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT:

Please read the Student website for additional requirements of in-person attendance: 

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 

Course Coordinator: Mr. Tomas Turek

Course Coordinator Phone: -

Course Coordinator Email:

Course Coordinator Location: -

Course Coordinator Availability: by appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities


Course Description

The role of a Data Scientist is an increasingly ubiquitous job role in many organisations 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

This course contributes to the following Program Learning Outcomes for BP340 Bachelor 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,
• Understand and use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools;
• Recognise and use research principles and methods applicable to data science.

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 manage large amounts of data arising from various sources
• Evaluate and compare solutions to data analysis problems on the basis of organisational and user requirements;
• Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of statistical problems.

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 technical and nontechnical 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 managing and processing data;
• Contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions;
• Reflect on experience and improve your own future practice;
• Locate and use data and information and evaluate its quality with respect to its authority and relevance.   

On completion of this course you should be able to:

  1. 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; 
  2. 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;
  3. Assignments, as described in Overview of Assessment (below), requiring an integrated understanding of the subject matter; and
  4. Private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual and technical problems. 

Overview of Learning Activities

Key concepts will be explained in lectures, classes or online, where course material will be presented and the subject matter will be illustrated with demonstrations and examples.

Tutorials, workshops and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide you practice in the application of theory and procedures. You will explore the concepts with teaching staff and other students, and receive feedback on your progress from data scientists working in industry. You will develop an integrated understanding of the subject matter through private study by working through the course as presented in classes. Comprehensive learning materials will aid you in 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. Use the RMIT Bookshop’s textbook list search page to find any recommended textbook(s).  



Overview of Assessment

Assessment Task 1: Data Science case study (group)
Weighting: 20%
This assessment task supports CLOs 1,2,3,4,5

Assessment Task 2: 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