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

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 following Program Learning Outcomes for Master of Medical Laboratory, Biomedical, Health and Applied Science streams.

Personal and professional awareness 

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge. 

Knowledge and technical competence 

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools. 

Problem-solving 

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution. 

Communication 

  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers. 

Information literacy 

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance. 
     


On completion of this course, you should be able to:

  1. Identify and pose statistical questions requiring complex investigation in the biosciences and related fields
  2. Apply advanced biostatistical methods to explore, analyse and visualise data and test statistical hypotheses
  3. Interpret biostatistical analysis and draw conclusions that are most appropriate on the nature of the data and in the presence of uncertainty
  4. 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
  5. 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.