MXB107 Probability and Statistics
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | MXB107 |
|---|---|
| Assumed Knowledge: | Specialist Mathematics or MXB100, or concurrently enrolled in MXB100. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $4,872 |
Unit Outline: Semester 1 2026, Gardens Point, Internal
| Unit code: | MXB107 |
|---|---|
| Credit points: | 12 |
| Assumed Knowledge: | Specialist Mathematics or MXB100, or concurrently enrolled in MXB100 |
| Coordinator: | Matthew Begun | m.begun@qut.edu.au |
Overview
Probability and statistics are essential for understanding uncertainty, making informed decisions, and analysing data across diverse fields, from business and healthcare to engineering and social sciences. This unit provides foundational skills to analyse data, test hypotheses, and draw reliable conclusions - critical for success in today’s data-driven world. Students will gain hands-on experience with industry-leading software, specifically R, for data analysis. This unit focuses on core concepts and practical skills, which will be developed further inDSB102 Statistical Machine Learning, which introduces basic regression. Students will develop a critical, ethical approach to data, with attention to diverse perspectives. This unit also prepares students for specialised topics in later units, such as MXB241 Stochastic Processes and MXB242 Regression and Design, building skills to address complex problems in applied fields.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate a broad and coherent knowledge of introductory probability, statisticsal modelling, and inference techniques.
- Select, apply, and justify appropriate statistical methods, interpret results in context, and demonstrate critical thinking throughout these processes in both abstract and applied contexts.
- Use appropriate software tools to analyse, visualise, interpret and document data, methods and results in statistical contexts.
- Present and communicate in written and graphical formats to specialist and non-specialist audiences.
- Identify and evaluate the assumptions, limitations, and ethical considerations inherent in statistical analyses.
Content
In this unit, you will explore the following topics:
Part I: Probability and Distributions (Weeks 1-6)
- Introduction to probability, events, and basic rules.
- Independence and conditional probability concepts.
- Law of Total Probability and Bayes' Theorem.
- Random variables and distributions (univariate).
- Special discrete distributions.
- Special continuous distributions and the Central Limit Theorem.
Part II: Descriptive Statistics and Inference (Weeks 7-12)
- Numerical summaries of data and basic visualisations.
- Bivariate data analysis and introduction to correlation.
- Sampling distributions and large-sample estimation.
- Large-sample hypothesis testing for mean and proportion.
- Small-sample inference with t-distributions.
- Categorical data analysis and chi-square tests.
Learning Approaches
The total volume of learning in this unit is 150 hours, in line with the Australian Qualifications Framework for a 12-credit-point unit. An example breakdown of this 150 hours is:
24 hours (2 hours x 12 weeks) live-streamed lecture classes
24 hours (2 hours x 12 weeks) practical classes
65 hours self-directed independent learning (5 hours x 13 weeks)
36 hours summative assessment (2 hours x 10 weeks portfolio contributions, 14 hours exam preparation, 3 hours exam)
Lectures, which are live-streamed for online students and in-person for on-campus students, will demonstrate problem formulation and problem solving, clarifying key theoretical topics covered in the accompanying lecture videos. In the practical classes, students will work through problems guided by teaching staff, apply R software for data analysis, and receive assistance with assessment-related tasks. The focus will be on building critical statistical skills through active engagement with real data sets, and students will also be encouraged to work collaboratively with their peers.
Attend optional learning support sessions (see free STEM Support for Science Students) in addition to lecture and practical classes, as needed throughout the year to support your self-directed independent learning.
Feedback on Learning and Assessment
Practical Classes: Teaching staff will provide feedback during practical classes while you work through worksheet questions. Peer discussions are encouraged for non-assessed questions, offering informal feedback to aid your learning.
Summative Assessment: Marks on assessments reflect your progress toward the unit’s learning outcomes. Written feedback will be provided on your portfolio submissions. You can request more detailed feedback during practical classes.
Formative Assessment: Formative feedback will be provided on in-semester assessment items through written comments on submitted assessments. Informal discussions during practical classes will also help clarify any questions and guide your progress.
Solutions to Problems: Solutions to worksheets and portfolio questions allow for self-assessment, helping you identify areas for improvement.
MXB107 Communication Channel: An online platform will be used for peer and staff interactions. Teaching staff will review posts weekly, answering questions and providing clarifications to support your learning.
Assessment
Overview
The assessment items in this unit are designed to determine your level of competency in meeting the unit learning outcomes while providing you with a range of tasks with varying levels of skill development and difficulty.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Probability Portfolio
The probability portfolio assesses content from weeks 1-5. The portfolio questions will be a selection of questions from practical class worksheets. You will submit your probability portfolio in week 6. The portfolio questions form exemplars for the short answer questions on the examination. Your submission will take the form of hand-written or typeset solutions submitted in electronic form.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of approved Generative AI tools is permitted for this assessment. The unit coordinator will provide guidance on their use - and any conditions that may apply - for this task. Please see the Assessment page on the canvas site for the unit for details.
Assessment: Statistics Portfolio
The statistics portfolio assesses content from weeks 7-11. The portfolio questions will be a selection of questions from practical class worksheets. You will submit your statistics portfolio in week 12. The portfolio questions form exemplars for the short answer questions on the examination. Your submission will take the form of hand-written or typeset solutions submitted in electronic form.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of approved Generative AI tools is permitted for this assessment. The unit coordinator will provide guidance on their use - and any conditions that may apply - for this task. Please see the Assessment page on the canvas site for the unit for details.
Assessment: Examination (invigilated)
The examination will cover all content (content from weeks 1-13), and assess understanding and application of foundational probability and statistics concepts, and interpretation of output and results in context. The probability portfolio, statistics portfolio, and practical class worksheets form exemplars for the exam.
You will not be permitted to take a resource sheet into the examination, the examination paper will instead contain a formula sheet, which is the same formula sheet provided to you at the beginning of the semester. You are encouraged to use and familiarise yourself with the formula sheet during the semester, prior to your examination. You will be permitted to use a calculator of any type.
The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.
The examination will be at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide testing centre information. The late submission period does not apply, and no extensions are available. if you can’t attend this exam due to special circumstances, you may apply to sit a deferred exam.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
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.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
All required learning materials will be provided on Canvas.
All statistical computation will be performed with R statistical software, which is available in QUT computer labs and available online at no charge for students to install on their personal computers.
There is no set text for this unit. There are many reference texts for this unit, many of which can be located in the library. There are also many online resources such as lecture notes and some e-books that can be found online. Example reference texts are listed below.
Resource Materials
Reference book(s)
Bertsekas, D.P. and J.N. Tsitsiklis: Introduction to Probability. 2nd edition. Athena Scientific 2008.
Ekstrom, C (2019) R Primer (2nd ed.) Taylor and Francis, Boca Raton, Florida.
Available to view online through QUT Library.
Grinstead, C.M. and J.L. Snell: Introduction to Probability. 2nd revised edition, American Mathematical Society, 1997. A free copy can be downloaded under GNU FDL.
Mendenhall, W., Beaver, R.J. and Beaver, B.M.: Introduction to probability and statistics. 15th edition, Cengage 2020.
Ross, Sheldon M.: A first course in probability. 8th edition, Pearson, 2008.
Schaeffer, R.L. and L.J. Young: Introduction to Probability and its Applications. 3rd edition, Brooks/Cole (or earlier editions authored by Schaeffer only).
Software
R Statistical Software download for free
RStudio Integrated Development Environment (IDE) download for free
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit, as all classes will be held in ordinary lecture theatres and computer laboratories. Emergency exits and assembly areas will be pointed out in the first few lectures. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Probability Portfolio, Statistics Portfolio
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
2: Engineering Application Ability
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
3: Professional and Personal Attributes
Relates to: Probability Portfolio, Statistics Portfolio
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
Relates to: Probability Portfolio, Statistics Portfolio
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Statistics Portfolio
MS01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Probability Portfolio, Statistics Portfolio - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Probability Portfolio, Statistics Portfolio
MV01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Probability Portfolio, Statistics Portfolio - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Probability Portfolio, Statistics Portfolio
Unit Outline: Semester 1 2026, Online
| Unit code: | MXB107 |
|---|---|
| Credit points: | 12 |
| Assumed Knowledge: | Specialist Mathematics or MXB100, or concurrently enrolled in MXB100 |
Overview
Probability and statistics are essential for understanding uncertainty, making informed decisions, and analysing data across diverse fields, from business and healthcare to engineering and social sciences. This unit provides foundational skills to analyse data, test hypotheses, and draw reliable conclusions - critical for success in today’s data-driven world. Students will gain hands-on experience with industry-leading software, specifically R, for data analysis. This unit focuses on core concepts and practical skills, which will be developed further inDSB102 Statistical Machine Learning, which introduces basic regression. Students will develop a critical, ethical approach to data, with attention to diverse perspectives. This unit also prepares students for specialised topics in later units, such as MXB241 Stochastic Processes and MXB242 Regression and Design, building skills to address complex problems in applied fields.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate a broad and coherent knowledge of introductory probability, statisticsal modelling, and inference techniques.
- Select, apply, and justify appropriate statistical methods, interpret results in context, and demonstrate critical thinking throughout these processes in both abstract and applied contexts.
- Use appropriate software tools to analyse, visualise, interpret and document data, methods and results in statistical contexts.
- Present and communicate in written and graphical formats to specialist and non-specialist audiences.
- Identify and evaluate the assumptions, limitations, and ethical considerations inherent in statistical analyses.
Content
In this unit, you will explore the following topics:
Part I: Probability and Distributions (Weeks 1-6)
- Introduction to probability, events, and basic rules.
- Independence and conditional probability concepts.
- Law of Total Probability and Bayes' Theorem.
- Random variables and distributions (univariate).
- Special discrete distributions.
- Special continuous distributions and the Central Limit Theorem.
Part II: Descriptive Statistics and Inference (Weeks 7-12)
- Numerical summaries of data and basic visualisations.
- Bivariate data analysis and introduction to correlation.
- Sampling distributions and large-sample estimation.
- Large-sample hypothesis testing for mean and proportion.
- Small-sample inference with t-distributions.
- Categorical data analysis and chi-square tests.
Learning Approaches
The total volume of learning in this unit is 150 hours, in line with the Australian Qualifications Framework for a 12-credit-point unit. An example breakdown of this 150 hours is:
24 hours (2 hours x 12 weeks) live-streamed lecture classes
24 hours (2 hours x 12 weeks) practical classes
65 hours self-directed independent learning (5 hours x 13 weeks)
36 hours summative assessment (2 hours x 10 weeks portfolio contributions, 14 hours exam preparation, 3 hours exam)
Lectures, which are live-streamed for online students and in-person for on-campus students, will demonstrate problem formulation and problem solving, clarifying key theoretical topics covered in the accompanying lecture videos. In the practical classes, students will work through problems guided by teaching staff, apply R software for data analysis, and receive assistance with assessment-related tasks. The focus will be on building critical statistical skills through active engagement with real data sets, and students will also be encouraged to work collaboratively with their peers.
Attend optional learning support sessions (see free STEM Support for Science Students) in addition to lecture and practical classes, as needed throughout the year to support your self-directed independent learning.
Feedback on Learning and Assessment
Practical Classes: Teaching staff will provide feedback during practical classes while you work through worksheet questions. Peer discussions are encouraged for non-assessed questions, offering informal feedback to aid your learning.
Summative Assessment: Marks on assessments reflect your progress toward the unit’s learning outcomes. Written feedback will be provided on your portfolio submissions. You can request more detailed feedback during practical classes.
Formative Assessment: Formative feedback will be provided on in-semester assessment items through written comments on submitted assessments. Informal discussions during practical classes will also help clarify any questions and guide your progress.
Solutions to Problems: Solutions to worksheets and portfolio questions allow for self-assessment, helping you identify areas for improvement.
MXB107 Communication Channel: An online platform will be used for peer and staff interactions. Teaching staff will review posts weekly, answering questions and providing clarifications to support your learning.
Assessment
Overview
The assessment items in this unit are designed to determine your level of competency in meeting the unit learning outcomes while providing you with a range of tasks with varying levels of skill development and difficulty.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Probability Portfolio
The probability portfolio assesses content from weeks 1-5. The portfolio questions will be a selection of questions from practical class worksheets. You will submit your probability portfolio in week 6. The portfolio questions form exemplars for the short answer questions on the examination. Your submission will take the form of hand-written or typeset solutions submitted in electronic form.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of approved Generative AI tools is permitted for this assessment. The unit coordinator will provide guidance on their use - and any conditions that may apply - for this task. Please see the Assessment page on the canvas site for the unit for details.
Assessment: Statistics Portfolio
The statistics portfolio assesses content from weeks 7-11. The portfolio questions will be a selection of questions from practical class worksheets. You will submit your statistics portfolio in week 12. The portfolio questions form exemplars for the short answer questions on the examination. Your submission will take the form of hand-written or typeset solutions submitted in electronic form.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of approved Generative AI tools is permitted for this assessment. The unit coordinator will provide guidance on their use - and any conditions that may apply - for this task. Please see the Assessment page on the canvas site for the unit for details.
Assessment: Examination (invigilated)
The examination will cover all content (content from weeks 1-13), and assess understanding and application of foundational probability and statistics concepts, and interpretation of output and results in context. The probability portfolio, statistics portfolio, and practical class worksheets form exemplars for the exam.
You will not be permitted to take a resource sheet into the examination, the examination paper will instead contain a formula sheet, which is the same formula sheet provided to you at the beginning of the semester. You are encouraged to use and familiarise yourself with the formula sheet during the semester, prior to your examination. You will be permitted to use a calculator of any type.
The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.
The examination will be at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide testing centre information. The late submission period does not apply, and no extensions are available. if you can’t attend this exam due to special circumstances, you may apply to sit a deferred exam.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
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.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
All required learning materials will be provided on Canvas.
All statistical computation will be performed with R statistical software, which is available in QUT computer labs and available online at no charge for students to install on their personal computers.
There is no set text for this unit. There are many reference texts for this unit, many of which can be located in the library. There are also many online resources such as lecture notes and some e-books that can be found online. Example reference texts are listed below.
Resource Materials
Reference book(s)
Bertsekas, D.P. and J.N. Tsitsiklis: Introduction to Probability. 2nd edition. Athena Scientific 2008.
Ekstrom, C (2019) R Primer (2nd ed.) Taylor and Francis, Boca Raton, Florida.
Available to view online through QUT Library.
Grinstead, C.M. and J.L. Snell: Introduction to Probability. 2nd revised edition, American Mathematical Society, 1997. A free copy can be downloaded under GNU FDL.
Mendenhall, W., Beaver, R.J. and Beaver, B.M.: Introduction to probability and statistics. 15th edition, Cengage 2020.
Ross, Sheldon M.: A first course in probability. 8th edition, Pearson, 2008.
Schaeffer, R.L. and L.J. Young: Introduction to Probability and its Applications. 3rd edition, Brooks/Cole (or earlier editions authored by Schaeffer only).
Software
R Statistical Software download for free
RStudio Integrated Development Environment (IDE) download for free
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit, as all classes will be held in ordinary lecture theatres and computer laboratories. Emergency exits and assembly areas will be pointed out in the first few lectures. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Probability Portfolio, Statistics Portfolio
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
2: Engineering Application Ability
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
3: Professional and Personal Attributes
Relates to: Probability Portfolio, Statistics Portfolio
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated)
Relates to: Probability Portfolio, Statistics Portfolio
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Statistics Portfolio
MS01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Probability Portfolio, Statistics Portfolio - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Probability Portfolio, Statistics Portfolio
MV01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Probability Portfolio, Statistics Portfolio - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Probability Portfolio, Statistics Portfolio, Examination (invigilated) - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Probability Portfolio, Statistics Portfolio