IFQ719 Exploring Data
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: | IFQ719 |
|---|---|
| Equivalent(s): | IFN719, IFZ719 |
| Assumed Knowledge: | Assumed knowledge: IFQ718, or concurrently enrolled in IFQ718. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $3,528 |
Unit Outline: Session 2 2024, QUT Online, Online
| Unit code: | IFQ719 |
|---|---|
| Credit points: | 12 |
| Equivalent: | IFN719, IFZ719 |
| Assumed Knowledge: | IFQ718, or concurrently enrolled in IFQ718 |
Overview
Data analytics is about making sense of data, and we have to explore data to make sense of it. This unit introduces you to concepts, critical thinking and skills that are fundamental to exploring data, as well as important ethical issues that can arise in that discovery process.
Visualisation is vital to exploratory data analysis. We will show you ways to use Python to get a visual impression of different kinds of data and to calculate various descriptive statistics, including measures of central tendency, spread and association. This knowledge will be essential to handling uncertainty in data. Our approach is grounded in practical application of these principles to real data sets.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate sound knowledge of fundamental data exploration methods in a modern programming language (SLO1 and CLO1)
- Identify and employ appropriate visualisation methods and descriptive statistics to industry-relevant data exploration scenarios (SLO2 and CLO2)
- Apply problem solving and critical thinking skills to develop appropriate, reproducible data exploration solutions (SLO3 and CLO3)
- Solve data exploration problems in self-directed contexts (SLO4 and CLO4)
- Demonstrate awareness of ethical issues in the field of data analytics (SLO6 and CLO6)
Content
The unit is an introduction to exploratory data analysis, where you will explore fundamental concepts in visualising and describing the variation and distribution of a wide range of data types, using modern visualisation practices in Python.
Content will include
- Effective visual communication practices for different kinds of data
- Visualising distributions of real data
- Descriptive statistics, including measures of central tendency, spread and association
- Exploring and visualising 2D, multi-dimensional, network, and spatial data from a wide range of application areas
- Methods for visualising and analysing extreme, missing and unexpected values in a dataset
- Applying these topics to the practice of data analytics
Learning Approaches
This unit is designed for asynchronous online study, with activities including short videos and exercises chosen to reinforce key skills and concepts. Students will also be able to participate in online discussions with peers and teaching staff.
Our approach to introducing new concepts and skills begins with motivating examples that raise questions and motivates data analytics practice. You will be given structured applied exercises to build your skills in using Python, critical thinking and reproducible work practices to explore data. Your two assessment tasks will give you a chance to demonstrate your understanding of data exploration concepts using Python and by articulating your critical thinking and awareness of practical challenges in honest and useful data visualisation. By completing and studying tutorial exercises, you will gain important formative feedback in preparation for these assessments.
Feedback on Learning and Assessment
You will receive written feedback on assignment task submissions. You 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 can 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: Data Exploration Exercises
This assessment involves intermediate level data exploration tasks based on real world data sets. Students will be given an opportunity to submit a set of exercises in week 4 for formative assessment. This will be followed by 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.
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.
Threshold Assessment:
To pass the unit, it is necessary to achieve a satisfactory grade for all assignments. You will be provided with the opportunity to re-submit Assignment 1 if you receive an unsatisfactory grade. This resubmission will be due 14 days from when the unsatisfactory grade is received
Assessment: Data Exploration Project
A more extensive, applied data exploration project based on a more sophisticated real world data set. Students will be given an opportunity to submit some part of the project in week 8 for formative assessment. This will be followed by the S/US summative assessment due in Week 10, which will rely on the content submitted in week 8.
This is an assignment for the purposes of an extension.
Threshold Assessment:
To pass the unit, it is necessary to achieve a satisfactory grade for all assignments.
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, Data Exploration Exercises, Data Exploration Project - Identify and employ appropriate industry relevant methods and approaches to address IT problems
Relates to: ULO2, Data Exploration Project - Apply design, problem solving and critical thinking skills to develop appropriate IT solutions
Relates to: ULO3, Data Exploration Exercises, Data Exploration Project - Solve complex IT problems in both self-directed and collaborative contexts
Relates to: ULO4, Data Exploration Exercises, Data Exploration Project - Demonstrate professional and career-oriented aptitude in the field of Information Technology
Relates to: ULO5, Data Exploration Project
Unit Outline: Session 4 2024, QUT Online, Online
| Unit code: | IFQ719 |
|---|---|
| Credit points: | 12 |
| Equivalent: | IFN719, IFZ719 |
| Assumed Knowledge: | IFQ718, or concurrently enrolled in IFQ718 |
Overview
Data analytics is about making sense of data, and we have to explore data to make sense of it. This unit introduces you to concepts, critical thinking and skills that are fundamental to exploring data, as well as important ethical issues that can arise in that discovery process.
Visualisation is vital to exploratory data analysis. We will show you ways to use Python to get a visual impression of different kinds of data and to calculate various descriptive statistics, including measures of central tendency, spread and association. This knowledge will be essential to handling uncertainty in data. Our approach is grounded in practical application of these principles to real data sets.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate sound knowledge of fundamental data exploration methods in a modern programming language (SLO1 and CLO1)
- Identify and employ appropriate visualisation methods and descriptive statistics to industry-relevant data exploration scenarios (SLO2 and CLO2)
- Apply problem solving and critical thinking skills to develop appropriate, reproducible data exploration solutions (SLO3 and CLO3)
- Solve data exploration problems in self-directed contexts (SLO4 and CLO4)
- Demonstrate awareness of ethical issues in the field of data analytics (SLO6 and CLO6)
Content
The unit is an introduction to exploratory data analysis, where you will explore fundamental concepts in visualising and describing the variation and distribution of a wide range of data types, using modern visualisation practices in Python.
Content will include
- Effective visual communication practices for different kinds of data
- Visualising distributions of real data
- Descriptive statistics, including measures of central tendency, spread and association
- Exploring and visualising 2D, multi-dimensional, network, and spatial data from a wide range of application areas
- Methods for visualising and analysing extreme, missing and unexpected values in a dataset
- Applying these topics to the practice of data analytics
Learning Approaches
This unit is designed for asynchronous online study, with activities including short videos and exercises chosen to reinforce key skills and concepts. Students will also be able to participate in online discussions with peers and teaching staff.
Our approach to introducing new concepts and skills begins with motivating examples that raise questions and motivates data analytics practice. You will be given structured applied exercises to build your skills in using Python, critical thinking and reproducible work practices to explore data. Your two assessment tasks will give you a chance to demonstrate your understanding of data exploration concepts using Python and by articulating your critical thinking and awareness of practical challenges in honest and useful data visualisation. By completing and studying tutorial exercises, you will gain important formative feedback in preparation for these assessments.
Feedback on Learning and Assessment
You will receive written feedback on assignment task submissions. You 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 can 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: Data Exploration Exercises
This assessment involves intermediate level data exploration tasks based on real world data sets. Students will be given an opportunity to submit a set of exercises in week 4 for formative assessment. This will be followed by 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.
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.
Threshold Assessment:
To pass the unit, it is necessary to achieve a satisfactory grade for all assignments. You will be provided with the opportunity to re-submit Assignment 1 if you receive an unsatisfactory grade. This resubmission will be due 14 days from when the unsatisfactory grade is received
Assessment: Data Exploration Project
A more extensive, applied data exploration project based on a more sophisticated real world data set. Students will be given an opportunity to submit some part of the project in week 8 for formative assessment. This will be followed by the S/US summative assessment due in Week 10, which will rely on the content submitted in week 8.
This is an assignment for the purposes of an extension.
Threshold Assessment:
To pass the unit, it is necessary to achieve a satisfactory grade for all assignments.
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, Data Exploration Exercises, Data Exploration Project - Identify and employ appropriate industry relevant methods and approaches to address IT problems
Relates to: ULO2, Data Exploration Project - Apply design, problem solving and critical thinking skills to develop appropriate IT solutions
Relates to: ULO3, Data Exploration Exercises, Data Exploration Project - Solve complex IT problems in both self-directed and collaborative contexts
Relates to: ULO4, Data Exploration Exercises, Data Exploration Project - Demonstrate professional and career-oriented aptitude in the field of Information Technology
Relates to: ULO5, Data Exploration Project