SEB113 Quantitative Methods in Science
Unit synopsis
Mathematics and Statistics underpins Science and Engineering research and practice. In Quantitative Methods in Science you will learn to apply the tools and skills of mathematics and statistics, to analyse, model and represent data for scientific purposes. It develops your practical quantitative problem-solving skills in real multidisciplinary scientific contexts. You will apply and augment your quantitative skills using real-world data collected during field- and laboratory work. This unit also builds awareness of how the different Science disciplines use and represent data, which will facilitate your choice of a discipline major in second semester.
- Faculty
- Science and Engineering Faculty
- School/Discipline
- Mathematical Sciences
- Study area
- Science and mathematics
- Credit points
- 12
Dates and locations
| Teaching period | Dates | Locations |
|---|---|---|
| Semester 1, 2018 | 19 February 2018 - 22 June 2018 |
|
| Semester 2, 2018 | 23 July 2018 - 16 November 2018 |
|
| Semester 1, 2019 | 25 February 2019 - 21 June 2019 |
|
| Semester 2, 2019 | 22 July 2019 - 15 November 2019 |
|
Fees
- Commonwealth supported place (CSP) student contribution amount
- 2018: $1,148
- Domestic fee-paying student fee
- 2018: $4,284
- International student fee
- 2018: $4,452
Previous study requirements
- Anti-requisites
- MAB101
Unit outlines
Coordinator
- Name
- Dr Belinda Spratt
- b.spratt@qut.edu.au
- Phone
- 31383035
Rationale
The outstanding societal, economic, and environmental contribution of science to improving and prolonging humankind's existence is certainly rooted in its unique methodology. Quantitative methods play a key role in science. Therefore, this unit focuses on the fundamental mathematical and statistical approaches used routinely in science, with a particular emphasis on experimental data. It introduces analytical, modelling and simulation skills.
With reference to theory, field and experimental settings, you explore and develop practical problem-solving skills to quantify and model real problems in science.
Aims
The aim of this unit is to introduce you to and develop your quantitative skills of analysis, simulation and modelling which underpins all scientific practice. You will understand how quantitative techniques are applied in different science disciplines with the opportunity to investigate a problem or issue in a specific field. Computation is taught in a modern, free and open source computing environment in order to demonstrate the principles of data science and reproducible research.
Learning outcomes
On completion of this unit, you will provide evidence that you can:
1. Apply appropriate research methods, technologies and tools to inform scientific investigation.
2. Analyse, interpret, model and simulate numerical data for scientific purposes and problems.
3. Apply selected quantitative research methods and outcomes to solve scientific problems derived from theory, field and laboratory settings.
4. Evaluate scientific data and mathematical models to visualise their representation and communicate them to scientific peer groups and/or the broader community using appropriate conventions.
Content
The focus on this unit is on applying mathematical and statistical methods in scientific contexts. You will look for meaningful scientific 'patterns' in the data using mathematical functions, conduct data analysis using calculus, and model the data to increasing levels of complexity using algebra (equations). You will also learn how to select and apply appropriate quantitative methods in these contexts. We will refresh and deepen your understanding of statistical methods, functions, algebra and calculus which are fundamental to measurement in experimental science.
Approaches to teaching and learning
The focus of this unit is on learning and applying quantitative methods in science using data obtained from scientific observation, examples and practice in field, laboratory and theory settings. All quantitative (mathematical and statistical) knowledge and skills will be taught and practised in context, related to real examples, problems and observations. For this purpose, academic staff from mathematical and science fields will be working collaboratively to align scientific examples and applications for all quantitative methods so that you develop conceptual scientific understanding with quantitative skills. This will engage you to think and apply quantitative methods as a novice scientist with awareness of how different science disciplines use and represent data.
The unit adopts a blended learning approach consisting of a sequence of a two (2) hour lecture, a one (1) hour computer lab, and a two (2) hour collaborative workshop. Access to lecture, lab and workshop material is mediated by an adaptive release system, presenting students with pre-lecture preparatory material each week that aims to bridge the assumed knowledge and the week's lecture topic. The computer lab worksheets are written such that students can work through them in their own time and at their own pace outside of the timetabled session, which is then used for troubleshooting and discussion of ideas. Collaborative workshops make use of the lecture and lab material with students undertaking and discussing quantitative analyses of case studies from the natural sciences. The workshop activities and roles are structured in such a way as to reinforce the collaborative and investigative nature of the scientific method.
A diagnostic quiz and student attitudes survey is released pre-semester to provide students with information about how their skills compare to the assumed knowledge and to reflect on their engagement with quantitative methods in the past, present and future. The student attitudes survey is taken again at the end of the semester to give students a chance to reflect on their experiences within the unit.
Assessment items
| Name | #1: Report |
|---|---|
| Description | Collaborative Scientific Article
The collaborative scientific article is the final piece of assessment for the unit, with students self-organising into groups to produce a quantitative report and an R script that contains all code required to generate the results in the article (a key concept in the reproducibility of scientific inquiry). As a group, students will choose a data set from the natural sciences, either experimental or observational in nature. Students will then develop a scientific question of interest, convert this into a statistical model and analyse and interpret the results. |
| Weighting | 30% |
| Due date | End of semester |
| Internal or external | Internal |
| Group or individual | Group |
| Relates to learning outcomes | 1,2,3,4 |
| Name | #2: Problem Solving Task |
|---|---|
| Description | Problem solving tasks will progressively demonstrate quantitative skills to select, analyse and model data in larger, more comprehensive scientific contexts and problems. |
| Weighting | 40% |
| Due date | Throughout the sem |
| Internal or external | Internal |
| Group or individual | Individual |
| Relates to learning outcomes | 1,2,3 |
| Name | #3: Quiz/Test |
|---|---|
| Description | Quizzes will be used throughout semester for progressive practice, testing and immediate feedback of your knowledge and ability to apply quantitative methods and skills to small scientific problems and issues. You will reflect on the strengths and weaknesses revealed by the quizzes. |
| Weighting | 30% |
| Due date | End of semester |
| Internal or external | Internal |
| Group or individual | Individual |
| Relates to learning outcomes | 2,3 |
Academic integrity
QUT is committed to maintaining high academic standards to protect the value of its qualifications. To assist you in assuring the academic integrity of your assessment you are encouraged to make use of the support materials and services available to help you consider and check your assessment items. Important information about the university's approach to academic integrity of assessment is on your unit Blackboard site.
A breach of academic integrity is regarded as Student Misconduct and can lead to the imposition of penalties.
Resource materials
There are no required textbooks for SEB113. A list of recommended and additional resources will be provided via QUT Readings.
The following books are recommended as references during the semester. You do not need to purchase these books, as they are available at no charge to students as eBooks through the QUT Library or the authors' website:
· Faraway, J. J. Linear Models with R. Chapman and Hall/CRC, 2016.
· Wickham, H. & Grolemund, G. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media, 2017
· Strang, G. Calculus. Wellesley-Cambridge Press, 1991.
The following book is recommended to those students wishing to have a general overview of the use of statistics in modern scientific research, with case studies from a range of natural sciences:
· Diggle, P. and Chetwynd, A. Statistics and Scientific Method: an introduction for students and researchers. Oxford University Press, 2011.
All computation will be performed with the R software inside the RStudio integrated development environment. Both of these pieces of software are required for performing analyses, are available in QUT's SEF computer labs and are available online at no charge for students to install on to their personal computers.
Risk assessment statement
There are no out of the ordinary risks associated with this unit. You will be made aware of evacuation procedures and assembly areas in the first few lectures. In the event of a fire alarm sounding, or on a lecturer's instruction, you should leave the room and assemble in the designated area which will be indicated to you. You should be conscious of your health and safety at all times whilst on campus or in the field.
Coordinator
- Name
- Dr Belinda Spratt
- b.spratt@qut.edu.au
- Phone
- 31383035
Rationale
The outstanding societal, economic, and environmental contribution of science to improving and prolonging humankind's existence is certainly rooted in its unique methodology. Quantitative methods play a key role in science. Therefore, this unit focuses on the fundamental mathematical and statistical approaches used routinely in science, with a particular emphasis on experimental data. It introduces analytical, modelling and simulation skills.
With reference to theory, field and experimental settings, you explore and develop practical problem-solving skills to quantify and model real problems in science.
Aims
The aim of this unit is to introduce you to and develop your quantitative skills of analysis, simulation and modelling which underpins all scientific practice. You will understand how quantitative techniques are applied in different science disciplines with the opportunity to investigate a problem or issue in a specific field. Computation is taught in a modern, free and open source computing environment in order to demonstrate the principles of data science and reproducible research.
Learning outcomes
On completion of this unit, you will provide evidence that you can:
1. Apply appropriate research methods, technologies and tools to inform scientific investigation.
2. Analyse, interpret, model and simulate numerical data for scientific purposes and problems.
3. Apply selected quantitative research methods and outcomes to solve scientific problems derived from theory, field and laboratory settings.
4. Evaluate scientific data and mathematical models to visualise their representation and communicate them to scientific peer groups and/or the broader community using appropriate conventions.
Content
The focus on this unit is on applying mathematical and statistical methods in scientific contexts. You will look for meaningful scientific 'patterns' in the data using mathematical functions, conduct data analysis using calculus, and model the data to increasing levels of complexity using algebra (equations). You will also learn how to select and apply appropriate quantitative methods in these contexts. We will refresh and deepen your understanding of statistical methods, functions, algebra and calculus which are fundamental to measurement in experimental science.
Approaches to teaching and learning
The focus of this unit is on learning and applying quantitative methods in science using data obtained from scientific observation, examples and practice in field, laboratory and theory settings. All quantitative (mathematical and statistical) knowledge and skills will be taught and practised in context, related to real examples, problems and observations. For this purpose, academic staff from mathematical and science fields will be working collaboratively to align scientific examples and applications for all quantitative methods so that you develop conceptual scientific understanding with quantitative skills. This will engage you to think and apply quantitative methods as a novice scientist with awareness of how different science disciplines use and represent data.
The unit adopts a blended learning approach consisting of a sequence of a two (2) hour lecture, a one (1) hour computer lab, and a two (2) hour collaborative workshop. Access to lecture, lab and workshop material is mediated by an adaptive release system, presenting students with pre-lecture preparatory material each week that aims to bridge the assumed knowledge and the week's lecture topic. The computer lab worksheets are written such that students can work through them in their own time and at their own pace outside of the timetabled session, which is then used for troubleshooting and discussion of ideas. Collaborative workshops make use of the lecture and lab material with students undertaking and discussing quantitative analyses of case studies from the natural sciences. The workshop activities and roles are structured in such a way as to reinforce the collaborative and investigative nature of the scientific method.
A diagnostic quiz and student attitudes survey is released pre-semester to provide students with information about how their skills compare to the assumed knowledge and to reflect on their engagement with quantitative methods in the past, present and future. The student attitudes survey is taken again at the end of the semester to give students a chance to reflect on their experiences within the unit.
Assessment items
| Name | #1: Report |
|---|---|
| Description | Collaborative Scientific Article
The collaborative scientific article is the final piece of assessment for the unit, with students self-organising into groups to produce a quantitative report and an R script that contains all code required to generate the results in the article (a key concept in the reproducibility of scientific inquiry). As a group, students will choose a data set from the natural sciences, either experimental or observational in nature. Students will then develop a scientific question of interest, convert this into a statistical model and analyse and interpret the results. |
| Weighting | 30% |
| Due date | End of semester |
| Internal or external | Internal |
| Group or individual | Group |
| Relates to learning outcomes | 1,2,3,4 |
| Name | #2: Problem Solving Task |
|---|---|
| Description | Problem solving tasks will progressively demonstrate quantitative skills to select, analyse and model data in larger, more comprehensive scientific contexts and problems. |
| Weighting | 40% |
| Due date | Throughout the sem |
| Internal or external | Internal |
| Group or individual | Individual |
| Relates to learning outcomes | 1,2,3 |
| Name | #3: Quiz/Test |
|---|---|
| Description | Quizzes will be used throughout semester for progressive practice, testing and immediate feedback of your knowledge and ability to apply quantitative methods and skills to small scientific problems and issues. You will reflect on the strengths and weaknesses revealed by the quizzes. |
| Weighting | 30% |
| Due date | End of semester |
| Internal or external | Internal |
| Group or individual | Individual |
| Relates to learning outcomes | 2,3 |
Academic integrity
QUT is committed to maintaining high academic standards to protect the value of its qualifications. To assist you in assuring the academic integrity of your assessment you are encouraged to make use of the support materials and services available to help you consider and check your assessment items. Important information about the university's approach to academic integrity of assessment is on your unit Blackboard site.
A breach of academic integrity is regarded as Student Misconduct and can lead to the imposition of penalties.
Resource materials
There are no required textbooks for SEB113. A list of recommended and additional resources will be provided via QUT Readings.
The following books are recommended as references during the semester. You do not need to purchase these books, as they are available at no charge to students as eBooks through the QUT Library or the authors' website:
· Faraway, J. J. Linear Models with R. Chapman and Hall/CRC, 2016.
· Wickham, H. & Grolemund, G. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media, 2017
· Strang, G. Calculus. Wellesley-Cambridge Press, 1991.
The following book is recommended to those students wishing to have a general overview of the use of statistics in modern scientific research, with case studies from a range of natural sciences:
· Diggle, P. and Chetwynd, A. Statistics and Scientific Method: an introduction for students and researchers. Oxford University Press, 2011.
All computation will be performed with the R software inside the RStudio integrated development environment. Both of these pieces of software are required for performing analyses, are available in QUT's SEF computer labs and are available online at no charge for students to install on to their personal computers.
Risk assessment statement
There are no out of the ordinary risks associated with this unit. You will be made aware of evacuation procedures and assembly areas in the first few lectures. In the event of a fire alarm sounding, or on a lecturer's instruction, you should leave the room and assemble in the designated area which will be indicated to you. You should be conscious of your health and safety at all times whilst on campus or in the field.
Apply
Single-unit study
You can apply to study this unit for personal or professional development
Apply now for single-unit study
Cross-institutional study
You can apply to study this unit for credit towards a course at another university