Outline

Overview

This unit focuses on methods and techniques to take into consideration the human elements in data science. Humans can act as both sources of data and its interpreters, introducing a range of complexities with regards to analysis. How do we account for the unreliability in data collected from humans? What can be done to address the subjects' concerns about their data? How can we create visualisations that facilitate understanding of the main findings? What are the limitations of any predictions? The ability to consider human factors is essential in any loop that involves people gathering, storing, or interpreting data for decision making. On completion of this unit, students will be able to identify and analyse the human factors in the data analytics loop, and will be able to derive solutions for the challenges that arise.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	(DATA2001 OR DATA2901) AND (DATA2002 OR DATA2902)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Basic statistics, database management, and programming
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Judy Kay, judy.kay@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Students must earn 40% on exam to pass unit.	55%	Formal exam period	1.5 hours
Outcomes assessed:
Small continuous assessment	Weekly workshop activities. 0.5 marks/week, count best 10 Better mark principle applies (see below)	5%	Multiple weeks	As per specification
Outcomes assessed:
Small continuous assessment	Weekly lab preparation and participation (LPP) 1.0 mark/week, count best 10.	10%	Multiple weeks	As per specification
Outcomes assessed:
Small continuous assessment	Assignment 2 individual checkpoints Assignment 2 individual checkpoints for contributions to group work	10%	Multiple weeks	As per specification
Outcomes assessed:
Presentation	Assignment 1 – Ethical assessment Video created by the group as described in detailed presentation.	10%	Week 05	Video length as in specification
Outcomes assessed:
Assignment	Assignment 2 – Analysis project Written report and all the code and co-ordination materials	10%	Week 13	As per specification
Outcomes assessed:
= group assignment ?

Assessment summary

Assignment 1 – Ethical assessment group assignment: Each group will analyse their allocated case study. The group will present a video report to their lab class.
Assignment 2 – project: Each group will use group processes as part of a data analysis project. They will use methods studied in class to produce:
- an ethical analysis;
- literate programming notebooks which document the data analysis, from exploratory analyses, through intermediate analysis steps, to the final analysis report;
- report of the group processes;
- a Github site with all work.
Weekly workshops: The Weekly Workshop Activities are designed to help you work in small groups to learn. They include formative assessments and other activities. They are worth 5%. The best 10 of your 13 marks will count, making each worth 0.5%. The better mark principle will be used for the total marks on the workshops so do not submit an application for Special Consideration or Special Arrangements if you miss a workshop. The better mark principle means that the total Workshops Mark counts if and only if it is better than or equal to 5% of your exam mark. If your total Workshops mark is less than 5% of your exam mark, then 5% of your exam mark will be used instead. This allows you to improve in the exam.
Weekly Lab Preparation and Participation (LPP): These are for each of the 12 labs. Each is worth 1%. The best 10 of your 12 marks will count. They are graded before each class to provide formative feedback and to assist in group coordination. As the teaching team grades these between the deadline and the teaching team meeeting, workshop and lab, no marks are awarded for late submissions – but the teaching team will be happy to provide feedback on late work.
Assignment 2 individual checkpoints: These are checkpoints required at Weeks 8, 10 and 12 with work that needs to be complete at that time so that the group can incorporate it. This means that no marks are awarded for late submissions – but the teaching team will provide feedback on late work and help individuals ensure they can contribute to the group and earn eligibility for the group mark for Assignment 2.
Final exam: The final exam is a written exam covering all materials in workshops, laboratories, and assignments.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard. It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the final examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

See above.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Week 1: introductions, HILDA big picture, pragmatics, assessment	Workshop (2 hr)
Week 02	Week 2: Ethics	Workshop (4 hr)
Week 03	Week 3: Data collection	Workshop (4 hr)
Week 04	Week 4: People 1	Workshop (4 hr)
Week 05	Week 5: Data engineering 1	Workshop (4 hr)
Week 06	Week 6. Data engineering 2	Workshop (4 hr)
Week 07	Week 7. Data engineering 3	Workshop (4 hr)
Week 08	Week 8. People 2	Workshop (4 hr)
Week 09	Week 9. Visualisation for analysts	Workshop (4 hr)
Week 10	Week 10. Visualisation for stakeholders 1, HCI methods 1	Workshop (4 hr)
Week 11	Week 11. Visualisation for stakeholders 2, HCI methods 2	Workshop (4 hr)
Week 12	Week 12: Advanced topics	Workshop (4 hr)
Week 13	Week 13: Review and about the exam	Workshop (4 hr)

Attendance and class requirements

All workshops and labs involve participation in activities which can contribute to the final grade.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

These will be provided in classes through the semester.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Identify ethical and legal issues in a data analytics task to answer the driving questions for the analysis.
LO2. Understand the diverse roles of people play in the full data analysis process and their implications for the ethical concerns and analysis methods.
LO3. Identify explicit and implicit requirements for carrying out a data analysis task to account for the perspectives of different stakeholders.
LO4. Understanding the particular challenges for data analysis when data is gathered from people.
LO5. Select analytic and statistical techniques appropriate for modelling uncertainty and bias in data, and students can justify their choice
LO6. Carry out (in guided stages) the whole design and implementation cycle for creating a human-in-the-loop pipeline to analyse a dataset to address a set of driving questions.
LO7. Use Literate Programming to communicate the thought process behind the data analysis process.
LO8. Communicate the process used to analyse a large data set, and to justify the methods used in the context of the humans gathering the data and interpreting the analysis
LO9. Communicate the results produced by an analysis pipeline, in oral and written form, accounting for the audience, making effective use of text, tables and visualisations.
LO10. Select appropriate techniques for evaluating the effectiveness of information reported to stakeholders, and ability to analyse and report the results and the choice of methods.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This is a relatively new subject with the first full cohort in 2020. Based on surveys (USS, in-class), interviews and focus groups (students, tutors), feedback from the consultative group, assessment performance (in-lecture, mini-assignments, assignments, exam, there have been many changes and refinements. In addition, materials have been updated with recent information. Kay changes these year are: --- Workshops are now weighted 5% with the better mark principle that Data science students will be familiar with from their mathematics and statistics subjects. --- Assignment 2 now has the former 10% group assessment and 10% for individual checkpoint contributions.

Additional information

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the final examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism. In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the similarity report available in ED (edstem.org). This program works in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

DATA3406: Human-in-the-Loop Data Analytics

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect