Unit rationale, description and aim

Effective data scientists need to be able to design and manage evidence-based projects to support change and growth in organisations to promote agility and success. This unit is intended to be the first of two units taken in sequence, the second unit being Data Science Project B. Both units are designed to foster problem-based self-learning and research. In this unit students will explore the data science research literature, identifying an area of study and conducting the first stages of the data science research life cycle. Students will draw on knowledge gained through their prior and current studies, as well as undertaking independent research to address gaps in knowledge. The unit will equip students with the knowledge and skills to undertake the exploratory and planning stages of an extensive data science research-oriented project. The aim of this unit is to assist students to conceptualise and plan data science projects.

2026 10

Campus offering

No unit offerings are currently available for this unit.

Prerequisites

Nil

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

Critically review and synthesize research literatu...

Learning Outcome 01

Critically review and synthesize research literature
Relevant Graduate Capabilities: GC1, GC2, GC3, GC7, GC9, GC10, GC11

Construct appropriate research methodologies using...

Learning Outcome 02

Construct appropriate research methodologies using contemporary data science techniques to address a practical problem
Relevant Graduate Capabilities: GC1, GC2, GC3, GC6, GC7, GC9, GC10, GC11

Assess and synthesize information gained through d...

Learning Outcome 03

Assess and synthesize information gained through data analysis
Relevant Graduate Capabilities: GC1, GC2, GC3, GC6, GC7, GC9, GC10, GC11

Develop a robust project plan

Learning Outcome 04

Develop a robust project plan
Relevant Graduate Capabilities: GC1, GC2, GC3, GC7, GC9, GC10, GC11

Content

Topics will include:

·        Research preparation including

o   Research ethics and principles

o   Types of scientific research

o   Research questions and hypotheses

o   Literature review

·        Problem identification and articulation of the research question

·        Data Collection and Preparation

·        Undertake data wrangling

·        Develop a project timeline

·        Exploratory data analysis and visualisation

·        Data requirements and acquisition

·        Statistical modelling

·        Risk assessment and mitigation

·        Development of project plan

Assessment strategy and rationale

Assessments in this units are designed to replicate contemporary industry practice in data design projects. In these environments data scientist will be required to evaluate and synthesise evidence to develop projects to support the needs of clients. Consequently, students begin this process with a literature review which assists them to acquire the skills of developing a broad picture of current evidence to inform project development. Following this, assessment 2 asks them to generate specific research questions and form a preliminary hypothesis. The third assessment requires students to compare their hypothesis with initial evidence outputs generated from the project. Finally, for assessment 4, students will continue to develop their skills of reflection and project refinement through development of a plan to complete the analysis (in data science project B) and report on risks and mitigation strategies to inform future directions of the project. Students must pass this assessment to pass the unit. Formative feedback will be provided to students to allow iterative development of their project plan. In mirroring workplace practice and using a project-based approach, this assessment authentically represents requirements of them in the workplace and supports work readiness.

To pass the unit, students must demonstrate achievement of every unit learning outcome and obtain a minimum mark of 50% 

Overview of assessments

Type – Literature Review Purpose – Investigate ...

Type – Literature Review

Purpose – Investigate problem area synthesising relevant literature. 

Weighting

20%

Learning Outcomes LO1
Graduate Capabilities GC1, GC2, GC3, GC7, GC9, GC10, GC11

Type – Research Hypothesis Development Purpose ...

Type – Research Hypothesis Development

Purpose – Articulate research questions/hypotheses and provide justification

Weighting

20%

Learning Outcomes LO1, LO2
Graduate Capabilities GC1, GC2, GC3, GC6, GC7, GC9, GC10, GC11

Type – Data Analysis Description Purpose – Desc...

Type – Data Analysis Description

Purpose – Description and outcomes of exploratory data analysis undertaken

Weighting

40%

Learning Outcomes LO1, LO2, LO3
Graduate Capabilities GC1, GC2, GC3, GC6, GC7, GC9, GC10, GC11

Type – Project Delivery and Report Purpose – De...

Type – Project Delivery and Report

Purpose – Deliver project plan and risk and mitigation strategies

Weighting

20%

Learning Outcomes LO1, LO2, LO3, LO4
Graduate Capabilities GC1, GC2, GC3, GC7, GC9, GC10, GC11

Learning and teaching strategy and rationale

The teaching approach within this unit puts the student at the forefront of their learning. This is achieved by using a just-in-time learning approach that integrates asynchronous interactive online elements with face-to-face learning experiences that focuses on the research process. Access to relevant knowledge is provided through prior learning and co-requisite studies, and resources that enable students to build their understandings in a flexible manner. Students are given the opportunity to build upon this knowledge through social learning experiences conducted in face-to-face classes such as tutorials and workshops. These opportunities enable students to build more complex understandings through peer interactions, structured and unstructured learning experiences. This blended learning approach allows students to develop research skills and lifelong learning habits aligned to vocational practices in data science. 

Representative texts and references

Representative texts and references

Alby, T. (2023) Data Science in Practice. Chapman & Hall.

 

Gray, D. & Shellshear, E. (2024) Why Data Science Projects Fail: The harsh realities of implementing AI and analytics, without the hype. Chapman & Hall.

 

Memon. Q.A. & Khoja, S.A. (2020). Data Science Theory, Analysis and Applications. CRC Press

 

Saavedra, D.M. (2022) How To Think About Data Science. Chapman & Hall.

 

Sharaff, A. & Sinha, G.R. (Eds.) (2022) Data Science and Its Applications. Chapman & Hall.

 

Williams, S. (2020). Data Action: Using data for public good. The MIT Press.

 

Yu, B. & Barter, R.L. (2024). Veridical Data Science: The practice of responsible data analysis and decision making. MIT Press.

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