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 second of two units taken in sequence, the first unit being Data Science Project A. Both units are designed to foster problem-based, self-learning and research. In this unit students continue to develop the knowledge and skills needed to undertake a data science research project. During the unit, students will be employing a combination of theoretical, analytical and computing skills relevant to their project, developing a model of their data, evaluating the model and interpret the outcomes to address the articulate research hypotheses. The culmination of this research project is a comprehensive written report detailing the stages of the research and addressing the research questions/hypotheses. The aim of this unit is to assist students to conceptualise, plan and implement data science projects.

2026 10

Campus offering

No unit offerings are currently available for this unit.

Prerequisites

DTSC641 Data Science Project A Research Essentials

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.

Build a data model using appropriate techniques to...

Learning Outcome 01

Build a data model using appropriate techniques to enable extraction of deep understanding of the underlying insights within the data being analysed
Relevant Graduate Capabilities: GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11

Evaluate the model using appropriate techniques to...

Learning Outcome 02

Evaluate the model using appropriate techniques to determine its effectiveness, accuracy and performance
Relevant Graduate Capabilities: GC1, GC2, GC8

Communicate the research undertaken, including met...

Learning Outcome 03

Communicate the research undertaken, including methodologies, data sources and model parameters to ensure reproducibility and transparency
Relevant Graduate Capabilities: GC1, GC2, GC3, GC7, GC11, GC12

Content

Topics will include:

·        Data modelling and analysis

·        Model Evaluation and interpretation of Results

·        Deployment and Communication of Findings

Assessment strategy and rationale

Assessments are aligned to the latter stages of the research process, guiding students through the process of implement the plan developed in Data Science Project A. Assessment 1 Build a data model to explain the data set developed in Data Science Project A. Assessment 2 requires students to validate and evaluate the model to ensure it is effective, accurate and is efficient. Assessment 3 is the culmination of the research project, where students produce a report detailing all stages of the project, the outcomes and implications. This series of assessments scaffolds and completes students' understanding of the research process in the context of a data science project.

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

Overview of assessments

Type – Project Progress Presentation Purpose – ...

Type – Project Progress Presentation

Purpose – This assessment allows students to demonstrate progress achieved in the preceding unit, Project A, and articulate the plan to complete the project.

This is an individual assessment

Weighting

10%

Learning Outcomes LO1, LO3
Graduate Capabilities GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11, GC12

Type – Report Purpose – Prepare a comprehensive...

Type – Report

Purpose – Prepare a comprehensive report articulating all stages of the research project and present to the outcomes to your peers

This is an individual assessment

Weighting

60%

Learning Outcomes LO1, LO2, LO3
Graduate Capabilities GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11

Type – Final Project Presentation Purpose – Thi...

Type – Final Project Presentation

Purpose – This task requires students to present the final version of their project, reflect on the methods used, the outcomes and their project journey, evaluating their acquired skills throughout this project.

This is an individual assessment

Weighting

30%

Learning Outcomes LO1, LO2, LO3
Graduate Capabilities GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11, GC12

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 continue 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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