IFQ720 Handling Uncertainty
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: | IFQ720 |
|---|---|
| Equivalent(s): | IFN720, IFZ720 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $3,528 |
Unit Outline: Session 1 2024, QUT Online, Online
| Unit code: | IFQ720 |
|---|---|
| Credit points: | 12 |
| Equivalent: | IFN720, IFZ720 |
Overview
Observations and measurements give an incomplete picture of reality, introducing uncertainty and random variation to data. Successful data analytics involves handling uncertain, unpredictable random data to yield meaningful insights.
Probability theory is a powerful framework for dealing with uncertainty when making data-based decisions. This unit introduces students to probability theory and statistical inference and the challenges and ethical issues of using uncertain data and interpreting and communicating analysis results. We will sample data from a population and use probability distributions to understand the inherent uncertainty in observed data and inform our decision-making process. These fundamental concepts will allow us to make inferences and build models that can explain current data and predict new observations.
This unit introduces essential skills, concepts and wisdom for anyone handling uncertain data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate sound knowledge of fundamental concepts in probability and statistical inference (SLO1 and CLO1)
- Identify and employ appropriate methods in statistical inference to industry-relevant data analysis scenarios using a modern programming language (SLO2 and CLO2)
- Apply problem solving and critical thinking skills to develop appropriate, reproducible solutions to analysing uncertain data (SLO3 and CLO3)
- Solve data analysis problems in self-directed contexts (SLO4 and CLO4)
- Demonstrate awareness of ethical issues in the field of data analytics (SLO6 and CLO6)
Content
Probability theory provides a deep and powerful framework for dealing with uncertainty and random variation. The unit introduces probability theory and statistical inference, as well as alerting students to some of the many ways in which uncertain data can be tricky to interpret and challenging to communicate to others. We will consolidate ideas about probability distributions (introduced in IFQ719 Exploring Data) and show what happens to these distributions as we gather more data. We will describe the concept of sampling data from a population and explain the amount of variation we would expect to see in sample statistics by chance alone. These fundamental concepts will allow us to make inferences from data and build models that can explain and predict what is going on.
Content will include
Fundamental concepts in making inferences from data, such as the notion of a population, a sample, and sampling variation,
The Central Limit Theorem and Law of Large Numbers, and how these are used in the machinery of hypothesis testing.
Models for explaining and making predictions about data, including models for regression, classification and understanding of the structure of data.
When we have finite data, there are trade-offs between model flexibility, overfitting and underfitting.
Ethical considerations in handling uncertainty.
Applying these topics to the practice of data analytics.
Topics List
Samples versus Populations: Types of Data and Sources of Uncertainty
Probability as a Model for Data Collection and Analysis
Statistical Inference: Estimation
Statistical Inference: Testing Hypotheses
Relationships Between Two or More Variables: Analysis of Variance (ANOVA)
Relationships Between Two or More Variables: Linear Regression
Relationships Between Two or More Variables: Classification via Logistic Regression
Making Predictions: Linear Regression and Classification Models; Model Diagnostics, Overfitting, Underfitting, Model Trade-offs and Ethical Considerations
Learning Approaches
This unit provides students with an asynchronous online study experience, with content delivered via various media, including, for example, readings, videos, and exercises carefully chosen to illustrate key skills and concepts.
Students will have the opportunity to reinforce concepts by participating in online discussions with peers and teaching staff. We introduce students to new ideas and skills via motivating examples that raise questions and challenges typical in data analytics practice. Students will receive structured practical exercises to build their skills in Python and develop critical thinking and reproducible work practices to handle uncertain data. Students will study cases that highlight important ethical considerations in working with uncertain data. Students' two assessment tasks will allow them to demonstrate their understanding of handling uncertain data, using Python to undertake the necessary computation and articulating their critical thinking and ethical awareness about practical problems or scenarios. By completing and studying tutorial exercises, you will gain important formative feedback in preparation for these assessments.
Feedback on Learning and Assessment
Students will receive automated feedback on some exercises and assessments, and written feedback on assignment task submissions. Students may seek additional feedback from the teaching staff in the unit.
Assessment
Overview
This unit is structured so that you can master new material through practical exercises prior to attempting the assessment tasks. The assessment tasks for the unit are organised so that you have the opportunity to gain experience and formative feedback from tutorial activities prior to undertaking the assessments needed to pass the unit.
Unit Grading Scheme
S (Satisfactory) / U (Unsatisfactory)
Assessment Tasks
Assessment: Exercises in Probability and Statistical Inference
This assessment involves intermediate level exercises using probability distributions and statistical inference. Submission for the S/US summative assessment due in Week 6. These latter exercises will build on those submitted earlier.
This is an assignment for the purposes of an extension.
Assessment: Handling Uncertainty Project
A more extensive, applied probability and statistical inference project based on a more sophisticated real world context. Submission for the S/US summative assessment due in Week 10.
This is an assignment for the purposes of an extension.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
This unit is largely self-contained.
Risk Assessment Statement
No out of the ordinary risks are associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ70 Graduate Certificate in Information Technology Practice (Study Area A)
- Demonstrate advanced, role-specific Information Technology (IT) discipline knowledge
Relates to: ULO1, Exercises in Probability and Statistical Inference, Handling Uncertainty Project - Identify and employ appropriate industry relevant methods and approaches to address IT problems
Relates to: ULO2, Exercises in Probability and Statistical Inference, Handling Uncertainty Project - Apply design, problem solving and critical thinking skills to develop appropriate IT solutions
Relates to: ULO3, Exercises in Probability and Statistical Inference, Handling Uncertainty Project - Solve complex IT problems in both self-directed and collaborative contexts
Relates to: ULO4, Handling Uncertainty Project - Demonstrate professional and career-oriented aptitude in the field of Information Technology
Relates to: ULO5, Handling Uncertainty Project
Unit Outline: Session 3 2024, QUT Online, Online
| Unit code: | IFQ720 |
|---|---|
| Credit points: | 12 |
| Equivalent: | IFN720, IFZ720 |
Overview
Observations and measurements give an incomplete picture of reality, introducing uncertainty and random variation to data. Successful data analytics involves handling uncertain, unpredictable random data to yield meaningful insights.
Probability theory is a powerful framework for dealing with uncertainty when making data-based decisions. This unit introduces students to probability theory and statistical inference and the challenges and ethical issues of using uncertain data and interpreting and communicating analysis results. We will sample data from a population and use probability distributions to understand the inherent uncertainty in observed data and inform our decision-making process. These fundamental concepts will allow us to make inferences and build models that can explain current data and predict new observations.
This unit introduces essential skills, concepts and wisdom for anyone handling uncertain data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate sound knowledge of fundamental concepts in probability and statistical inference (SLO1 and CLO1)
- Identify and employ appropriate methods in statistical inference to industry-relevant data analysis scenarios using a modern programming language (SLO2 and CLO2)
- Apply problem solving and critical thinking skills to develop appropriate, reproducible solutions to analysing uncertain data (SLO3 and CLO3)
- Solve data analysis problems in self-directed contexts (SLO4 and CLO4)
- Demonstrate awareness of ethical issues in the field of data analytics (SLO6 and CLO6)
Content
Probability theory provides a deep and powerful framework for dealing with uncertainty and random variation. The unit introduces probability theory and statistical inference, as well as alerting students to some of the many ways in which uncertain data can be tricky to interpret and challenging to communicate to others. We will consolidate ideas about probability distributions (introduced in IFQ719 Exploring Data) and show what happens to these distributions as we gather more data. We will describe the concept of sampling data from a population and explain the amount of variation we would expect to see in sample statistics by chance alone. These fundamental concepts will allow us to make inferences from data and build models that can explain and predict what is going on.
Content will include
Fundamental concepts in making inferences from data, such as the notion of a population, a sample, and sampling variation,
The Central Limit Theorem and Law of Large Numbers, and how these are used in the machinery of hypothesis testing.
Models for explaining and making predictions about data, including models for regression, classification and understanding of the structure of data.
When we have finite data, there are trade-offs between model flexibility, overfitting and underfitting.
Ethical considerations in handling uncertainty.
Applying these topics to the practice of data analytics.
Topics List
Samples versus Populations: Types of Data and Sources of Uncertainty
Probability as a Model for Data Collection and Analysis
Statistical Inference: Estimation
Statistical Inference: Testing Hypotheses
Relationships Between Two or More Variables: Analysis of Variance (ANOVA)
Relationships Between Two or More Variables: Linear Regression
Relationships Between Two or More Variables: Classification via Logistic Regression
Making Predictions: Linear Regression and Classification Models; Model Diagnostics, Overfitting, Underfitting, Model Trade-offs and Ethical Considerations
Learning Approaches
This unit provides students with an asynchronous online study experience, with content delivered via various media, including, for example, readings, videos, and exercises carefully chosen to illustrate key skills and concepts.
Students will have the opportunity to reinforce concepts by participating in online discussions with peers and teaching staff. We introduce students to new ideas and skills via motivating examples that raise questions and challenges typical in data analytics practice. Students will receive structured practical exercises to build their skills in Python and develop critical thinking and reproducible work practices to handle uncertain data. Students will study cases that highlight important ethical considerations in working with uncertain data. Students' two assessment tasks will allow them to demonstrate their understanding of handling uncertain data, using Python to undertake the necessary computation and articulating their critical thinking and ethical awareness about practical problems or scenarios. By completing and studying tutorial exercises, you will gain important formative feedback in preparation for these assessments.
Feedback on Learning and Assessment
Students will receive automated feedback on some exercises and assessments, and written feedback on assignment task submissions. Students may seek additional feedback from the teaching staff in the unit.
Assessment
Overview
This unit is structured so that you can master new material through practical exercises prior to attempting the assessment tasks. The assessment tasks for the unit are organised so that you have the opportunity to gain experience and formative feedback from tutorial activities prior to undertaking the assessments needed to pass the unit.
The grading schema used in QUT Bootcamps is satisfactory/unsatisfactory. All assessment is not weighted and as such becomes threshold.
Unit Grading Scheme
S (Satisfactory) / U (Unsatisfactory)
Assessment Tasks
Assessment: Exercises in Probability and Statistical Inference
This assessment involves intermediate level exercises using probability distributions and statistical inference. Submission for the S/US summative assessment due in Week 6. These latter exercises will build on those submitted earlier.
The grading schema used in QUT bootcamps is satisfactory/unsatisfactory. Students are provided with the opportunity to re-submit Assessment 1 if they receive an unsatisfactory grade as explained in the special conditions of assessment in each unit learning site.
This is an assignment for the purposes of an extension.
Assessment: Handling Uncertainty Project
A more extensive, applied probability and statistical inference project based on a more sophisticated real world context. Submission for the S/US summative assessment due in Week 10.
The grading schema used in QUT bootcamps is satisfactory/unsatisfactory.
This is an assignment for the purposes of an extension.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
This unit is largely self-contained.
Risk Assessment Statement
No out of the ordinary risks are associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ70 Graduate Certificate in Information Technology Practice (Study Area A)
- Demonstrate advanced, role-specific Information Technology (IT) discipline knowledge
Relates to: ULO1, Exercises in Probability and Statistical Inference, Handling Uncertainty Project - Identify and employ appropriate industry relevant methods and approaches to address IT problems
Relates to: ULO2, Exercises in Probability and Statistical Inference, Handling Uncertainty Project - Apply design, problem solving and critical thinking skills to develop appropriate IT solutions
Relates to: ULO3, Exercises in Probability and Statistical Inference, Handling Uncertainty Project - Solve complex IT problems in both self-directed and collaborative contexts
Relates to: ULO4, Handling Uncertainty Project - Demonstrate professional and career-oriented aptitude in the field of Information Technology
Relates to: ULO5, Handling Uncertainty Project