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.
Campus offering
No unit offerings are currently available for this unit.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
Evaluate the model using appropriate techniques to...
Learning Outcome 02
Communicate the research undertaken, including met...
Learning Outcome 03
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
10%
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
60%
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
30%
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.