IFQ718 Data Carpentry
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: | IFQ718 |
|---|---|
| Equivalent(s): | IFN718, IFZ718 |
| 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: | IFQ718 |
|---|---|
| Credit points: | 12 |
| Equivalent: | IFN718, IFZ718 |
| Assumed Knowledge: | There is no assumed knowledge for this unit. |
Overview
Data analytics is all about making sense of data, but before we can explore and analyse it, we need to adequately capture and ‘wrangle’ or manipulate it. Data Carpentry provides you with the toolbox you need to undertake practical data analysis. This unit introduces you to fundamental concepts, critical thinking and computational skills that are essential for cleansing, manipulating and representing data, as well as the important ethical issues that can arise in that process. This knowledge is an essential foundation for all data analyses.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of data manipulation methods in a modern computational environment (maps to SALO1 and CLO1)
- Identify and employ modern software tools and libraries to cleanse, manipulate and represent data (maps to SALO2 and CLO2)
- Apply problem solving and critical thinking skills to develop appropriate, reproducible data manipulation solutions (maps to SALO3 and CLO3)
- Solve intermediate level data problems in a modern environment through self-directed professional activity (maps to SALO4 and CLO4)
Content
In this unit you will learn fundamental methods to cleanse, manipulate and represent data. You will learn how to investigate and prevent issues around data quality. You will develop a strong understanding of abstract data representations, that is, how to extract and select features from a range of data types (including text or images).
Content will include:
- Programming fundamentals in Python
- Data types and representation
- Data quality issues (such as missing data)
- Ingesting data
- Integrating different data sets
- Ethical issues in data carpentry
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity 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 challenges that are typical in data analytics practice. You will be given structured practical exercises to build your skills in using Python, critical thinking and reproducible work practices to cleanse, manipulate and represent data. You will study cases that highlight important ethical considerations in data manipulation. Your two assessment tasks will give you a chance to demonstrate your understanding of data manipulation concepts by using Python and by articulating your 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
You will receive automated feedback on some exercises and assessments, and 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 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.
Unit Grading Scheme
S (Satisfactory) / U (Unsatisfactory)
Assessment Tasks
Assessment: Analytical Exercises (Formative and Summative)
Intermediate level data analytics exercises. 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.
The grading schema used in QUT Bootcamps is satisfactory/unsatisfactory. Students are provided with the opportunity to re-submit Assessment 1 if they receive a fail grade as explained in the special conditions of assessment in each unit learning site.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
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: Analytics Project
A data analytics project. 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.
The grading schema used in QUT Bootcamps is satisfactory/unsatisfactory.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
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, Analytical Exercises (Formative and Summative), Analytics Project - Identify and employ appropriate industry relevant methods and approaches to address IT problems
Relates to: ULO2, Analytical Exercises (Formative and Summative), Analytics Project - Apply design, problem solving and critical thinking skills to develop appropriate IT solutions
Relates to: ULO3, Analytics Project - Solve complex IT problems in both self-directed and collaborative contexts
Relates to: ULO4, Analytics Project
Unit Outline: Session 4 2024, QUT Online, Online
| Unit code: | IFQ718 |
|---|---|
| Credit points: | 12 |
| Equivalent: | IFN718, IFZ718 |
| Assumed Knowledge: | There is no assumed knowledge for this unit. |
Overview
Data analytics is all about making sense of data, but before we can explore and analyse it, we need to adequately capture and ‘wrangle’ or manipulate it. Data Carpentry provides you with the toolbox you need to undertake practical data analysis. This unit introduces you to fundamental concepts, critical thinking and computational skills that are essential for cleansing, manipulating and representing data, as well as the important ethical issues that can arise in that process. This knowledge is an essential foundation for all data analyses.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of data manipulation methods in a modern computational environment (maps to SALO1 and CLO1)
- Identify and employ modern software tools and libraries to cleanse, manipulate and represent data (maps to SALO2 and CLO2)
- Apply problem solving and critical thinking skills to develop appropriate, reproducible data manipulation solutions (maps to SALO3 and CLO3)
- Solve intermediate level data problems in a modern environment through self-directed professional activity (maps to SALO4 and CLO4)
Content
In this unit you will learn fundamental methods to cleanse, manipulate and represent data. You will learn how to investigate and prevent issues around data quality. You will develop a strong understanding of abstract data representations, that is, how to extract and select features from a range of data types (including text or images).
Content will include:
- Programming fundamentals in Python
- Data types and representation
- Data quality issues (such as missing data)
- Ingesting data
- Integrating different data sets
- Ethical issues in data carpentry
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity 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 challenges that are typical in data analytics practice. You will be given structured practical exercises to build your skills in using Python, critical thinking and reproducible work practices to cleanse, manipulate and represent data. You will study cases that highlight important ethical considerations in data manipulation. Your two assessment tasks will give you a chance to demonstrate your understanding of data manipulation concepts by using Python and by articulating your 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
You will receive automated feedback on some exercises and assessments, and 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 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.
Unit Grading Scheme
S (Satisfactory) / U (Unsatisfactory)
Assessment Tasks
Assessment: Analytical Exercises (Formative and Summative)
Intermediate level data analytics exercises. 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.
The grading schema used in QUT Bootcamps is satisfactory/unsatisfactory. Students are provided with the opportunity to re-submit Assessment 1 if they receive a fail grade as explained in the special conditions of assessment in each unit learning site.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
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: Analytics Project
A data analytics project. 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.
The grading schema used in QUT Bootcamps is satisfactory/unsatisfactory.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
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, Analytical Exercises (Formative and Summative), Analytics Project - Identify and employ appropriate industry relevant methods and approaches to address IT problems
Relates to: ULO2, Analytical Exercises (Formative and Summative), Analytics Project - Apply design, problem solving and critical thinking skills to develop appropriate IT solutions
Relates to: ULO3, Analytics Project - Solve complex IT problems in both self-directed and collaborative contexts
Relates to: ULO4, Analytics Project