2024 unit offering information will be available in November 2023
Data management and analysis are essential tools for scientists. This unit provides students with basic skills in data management, illustration, analysis, and interpretation that will be foundational for careers in science and science-related disciplines. The unit will examine different types of data and how to compare and present them in a scientific manner using a range of graphing techniques. Case studies will be examined from regenerative agriculture, marine science, ecology, education and biomedical science. The unit covers introductory-level analyses such as t-tests, ANOVA, correlation and regression, and Chi-square tests. Emphasis is placed on matching data with appropriate statistical tests to provide students with knowledge on how to answer simple scientific hypotheses. The unit also gives an overview of experimental designs and sampling strategies for data collection that can be applied across disciplines.
1. Scientific measurements and data
2. Data management, tables and graphs
3. Statistically comparing data between two or more groups
4. Analysing relationships: correlation and regression
5. Analysing categorical data
6. Experimental and survey designs
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
|On completion of this unit, students should be able to:|
|1||demonstrate an understanding of how to calculate descriptive statistics for different data types|
|2||apply skills in Excel to organise, analyse and display data and results in a professional manner|
|3||identify and differentiate core principles of sampling and experimental design|
|4||present and interpret results of statistical analyses in a professional manner.|
On completion of this unit, students should be able to:
- demonstrate an understanding of how to calculate descriptive statistics for different data types
- apply skills in Excel to organise, analyse and display data and results in a professional manner
- identify and differentiate core principles of sampling and experimental design
- present and interpret results of statistical analyses in a professional manner.
Teaching and assessment
Commonwealth Supported courses
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