Course Title: Biostatistics
Part A: Course Overview
Course Title: Biostatistics
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
MATH1300 |
City Campus |
Postgraduate |
145H Mathematical & Geospatial Sciences |
Face-to-Face |
Sem 1 2006 |
MATH1300 |
City Campus |
Postgraduate |
145H Mathematical & Geospatial Sciences |
Face-to-Face or Internet |
Sem 1 2013, Sem 2 2013 |
MATH1300 |
City Campus |
Postgraduate |
145H Mathematical & Geospatial Sciences |
Internet |
Sem 2 2006, Sem 2 2007, Sem 2 2008, Sem 2 2009, Sem 2 2010, Sem 1 2011, Sem 2 2011, Sem 1 2012, Sem 2 2012, Sem 1 2014, Sem 1 2015, Sem 2 2015, Sem 1 2016, Sem 2 2016 |
MATH1300 |
City Campus |
Postgraduate |
171H School of Science |
Face-to-Face |
Sem 1 2019, Sem 2 2019, Sem 1 2021, Sem 2 2021, Sem 1 2022, Sem 2 2022, Sem 1 2023, Sem 2 2023 |
MATH1300 |
City Campus |
Postgraduate |
171H School of Science |
Face-to-Face or Internet |
Sem 1 2018, Sem 2 2018, Sem 1 2020, Sem 2 2020 |
MATH1300 |
City Campus |
Postgraduate |
171H School of Science |
Internet |
Sem 1 2017, Sem 2 2017 |
MATH2209 |
Bundoora Campus |
Postgraduate |
171H School of Science |
Face-to-Face |
Sem 2 2023, Sem 1 2024, Sem 2 2024, Sem 1 2025 |
Course Coordinator: Dr. Stella Stylianou
Course Coordinator Phone: +61 3 9925 6227
Course Coordinator Email: stella.stylianou@rmit.edu.au
Course Coordinator Availability: By appointment, by email
Pre-requisite Courses and Assumed Knowledge and Capabilities
None
Course Description
This course will introduce you to the topic of statistics as applied in biological, medical and epidemiological fields. The course will begin with an introduction to summary statistics, data visualisation and probability as a measure for uncertainty. The course will then build upon these topics by introducing statistical data investigations, sampling, sampling distributions and confidence intervals as the basis for statistical inference. The course will finish with a series of modules looking at common hypothesis testing methods for different types of data. The course emphasises conceptual understanding, interpretation of statistical output and the use of statistical software packages for statistical computation.
Objectives/Learning Outcomes/Capability Development
This course contributes to the program learning outcomes for the following program:
MC158 - Master of Laboratory Medicine
PLO2. Apply systematic knowledge of core cellular and molecular processes underlying health and disease in a global context including First Nations people.
PLO4. Adapt critical analysis skills in problem solving methodologies and artefacts.
PLO5. Critically evaluate the principles relating to scientific integrity, ethical issues and legal framework.
PLO6. Apply an effective connected interpersonal and teamwork in the field of laboratory medicine to maintain a safe working environment.
PLO7. Interpret and present complex information through collaboration with individuals and multidisciplinary groups in the diagnostic laboratory environment.
PLO9. Apply the principles and methods of scientific inquiry, research design and performance in a laboratory project in the field of laboratory medicine.
MC111P03 - Master of Biotechnology
PLO 1.1 - Understanding Science - You will demonstrate an advanced understanding of biological sciences by articulating the methods of science, explaining why current biological knowledge is both contestable and testable through further inquiry, and explaining the role and relevance of biotechnology in society
PLO 1.3 - Understanding Science - You will demonstrate knowledge of research principles and methods applicable to biological sciences
For more information on the program learning outcomes for your program, please see the program guide.
On completion of this course, you should be able to:
- Identify and pose statistical questions requiring complex investigation in the biosciences and related fields
- Apply advanced biostatistical methods to explore, analyse and visualise data and test statistical hypotheses
- Interpret biostatistical analysis and draw conclusions that are most appropriate on the nature of the data and in the presence of uncertainty
- Communicate statistical concepts and analyses results in a variety of formats and to a range of diverse audiences in an individual and/or team context
- Reflect on the major professional, ethical and integrity-based issues that arise during the practice of biostatistical analysis
Overview of Learning Activities
This course is delivered face-to-face (or online) through the learning management system (Canvas). This will give you access to course information, communication tools, online notes, and assessment activities. The learning activities will be blended and include self-directed learning material, real-time synchronous lectorials (Face to face or Online) and practical classes (Face to face or Online). You will need to complete all the essential set text reading, review all the videos, and attend the scheduled classes to ensure you have understood the key concepts in order to complete the assessment tasks. Completion of the regular exercise submissions, online practical assessments and a major course project will help you develop and assess your understanding. The course modules emphasise conceptual understanding and the use of technology for statistical computation. The course notes and real-time synchronous classes feature detailed explanations of concepts, video demonstrations, and worked examples. This online material will make your individual study activity more flexible. Course communication will take place through emails with the course coordinator/lecturer and an online community, where students can interact with the lecturer and their peers.
Overview of Learning Resources
RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.
There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.
Overview of Assessment
Assessment Tasks
Assessment Task 1: Module Assignments (11)
Weighting 20%
This assessment task supports CLOs 1, 2, 3 & 4
Assessment Task 2: Summative Assessments (2)
Weighting 40%
This assessment task supports CLOs 1, 2, 3 & 4
Assessment Task 3: Project
Weighting 40%
This assessment task supports CLOs 1, 2, 3, 4 & 5
If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.