SEB108 Foundations of Data Analysis and Visualisation

Unit Outline: Semester 2 - 6 Week D 2024, Gardens Point, Internal

Overview

In science, new understanding grows from our analysis and interpretation of data. This unit will provide you with an opportunity to learn how the scientific method and data are related, and how we can extract meaningful information from data. You will work with a real-world data set to develop your skills in data analysis and visualisation using a relevant coding language while addressing an industry-defined question.

Learning Outcomes

On successful completion of this unit you will be able to:

1. Apply a structured approach to defining a hypothesis and addressing it through data-driven inquiry.
2. Develop insight through data analysis using appropriate computational tools.
3. Communicate findings to an industry partner using appropriate data visualisations and an industry standard report format.

Content

Learning Approaches

Your learning in this unit will be carefully scaffolded to support you in developing a foundational understanding of data analysis and visualisation techniques, which will be extended and built upon in your future discipline-specific studies. SEB108 will focus on inquiry-based learning, in which you will define and test a hypothesis through your approach to data analysis and visualisation in an industry-facing context.

You can expect to spend between 10 -15 hours per week preparing for and attending all scheduled learning activities, completing assessment tasks and undertaking your own independent study to consolidate your learning.

Feedback on Learning and Assessment

Oral and/or written feedback will be provided as you work to develop your hypothesis and create a structured inquiry incorporating coding and data visualisation. You will receive individual written feedback from academic staff on your report, along with general feedback from the industry partner.

Assessment

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Workbook

Your workbook will reflect the planning and execution of your data analysis project, including the development of your hypothesis, your approach to investigation, and commented code snippets from your data analysis as well as other relevant information.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: Report

Following a standard format, your report will summarise your data analysis project using both written text and visual representations of your data analysis.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Academic Integrity

Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.

Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.

Requirements to Study

Costs

There are no out of the ordinary costs associate with studying this unit.

Resources

Risk Assessment Statement

This unit carries no out of the ordinary risks.

Course Learning Outcomes

ST01 Bachelor of Science

1. Develop a broad, multidisciplinary understanding of science and a specialised, in-depth knowledge of at least one discipline.
   Relates to: ULO1, ULO2, Workbook
2. Use higher order thinking skills to design, plan, and conduct investigations and evaluate data to address scientific questions and challenges.
   Relates to: ULO1, ULO2, Workbook
3. Develop and demonstrate key competencies in scientific practices and relevant technologies.
   Relates to: ULO2, Workbook
4. Communicate scientific findings, concepts and evidence-based reasoning to diverse audiences using a variety of methods.
   Relates to: ULO3, Report

SV02 Bachelor of Science

1. Develop a broad, multidisciplinary understanding of science and a specialised, in-depth knowledge of at least one discipline.
   Relates to: ULO1, ULO2, Workbook
2. Use higher order thinking skills to design, plan, and conduct investigations and evaluate data to address scientific questions and challenges.
   Relates to: ULO1, ULO2, Workbook
3. Develop and demonstrate key competencies in scientific practices and relevant technologies.
   Relates to: ULO2, Workbook
4. Communicate scientific findings, concepts and evidence-based reasoning to diverse audiences using a variety of methods.
   Relates to: ULO3, Report