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Unit of study_

DATA3406: Human-in-the-Loop Data Analytics

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.

Details

Academic unit Computer Science
Unit code DATA3406
Unit name Human-in-the-Loop Data Analytics
Session, year
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Semester 2, 2021
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

Prohibitions
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None
Prerequisites
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(DATA2001 OR DATA2901) AND (DATA2002 OR DATA2902)
Corequisites
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None
Assumed knowledge
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Basic statistics, database management, and programming.

Available to study abroad and exchange students

No

Teaching staff and contact details

Coordinator Judy Kay, judy.kay@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home extended release) Type E final exam hurdle task Final exam
Students must earn 40% on exam to pass unit.
30% Formal exam period 24 hours
Outcomes assessed: LO1 LO11 LO10 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Small continuous assessment In class formative activities.
Each lecture -> 3 marks. Tally and cap at 30.
10% Multiple weeks Through whole class.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11
Small continuous assessment hurdle task Weekly mini-assignments and tute activities
Individual work that includes * required for eligibility for group mark
20% Multiple weeks Weeks 2 to 12 inclusive.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11
Assignment group assignment Assignment 1 – Ethical assessment
10% Week 05 Video length as in specification
Outcomes assessed: LO1 LO4 LO5 LO9
Assignment group assignment Assignment 2 – Analysis presentation
10% Week 12 Video length as in specification
Outcomes assessed: LO1 LO11 LO10 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Assignment group assignment Assignment 2 – Analysis report
Written report and all the code and co-ordination materials
20% Week 13 As per specification
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11
hurdle task = hurdle task ?
group assignment = group assignment ?
Type E final exam = Type E final exam ?
  • HILDA planning report: This assignment consists of two parts. Each group is required to analyses an allocated case study. The group is to then identify a data set for project 2. Both sections are to be reported in a set of slides which serve as a report and are used in a week 5 lab presentation.
  • HILDA project: This assessignment involves conducting a data analysis report using methods studied in class to produce a literate programming notebook that documents the processes and provides intermediate analysis steps, visualisations of the raw data and exploratory analyses, visual and text presentation of the final results and the presentation of these in the week 12 labratory.
  • Peerwise questions: Each student will create questions for allocated weeks of lecture material and answer a broad set of questions as a core part of learning lecture and lab content.
  • Lecture and labratory activities: There will be class activities in each of the 13 lectures and 12 labs and each will be graded as
    satisfactory or not. Each is of equal weight.
  • Final exam: The final exam is a written exam covering all materials in lectures, tutorials, 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 sydney.edu.au/students/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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Week 1: introductions, HILDA big picture, pragmatics, assessment Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11
Week 02 Week 2: Ethics Lecture and tutorial (4 hr) LO1 LO4 LO5
Week 03 Week 3: Data collection Lecture and tutorial (4 hr) LO1 LO4 LO6
Week 04 Week 4: People 1 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO5 LO7 LO8
Week 05 Week 5: Data engineering 1 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO6 LO7 LO8
Week 06 Week 6. Data engineering 2 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO6 LO7 LO8 LO9
Week 07 Week 7. Data engineering 3 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Week 08 Week 8. People 2 Lecture and tutorial (4 hr) LO3 LO5
Week 09 Week 9. Visualisation for analysts Lecture and tutorial (4 hr) LO2
Week 10 Week 10. Visualisation for stakeholders 1, HCI methods 1 Lecture and tutorial (4 hr) LO3
Week 11 Week 11. Visualisation for stakeholders 2, HCI methods 2 Lecture and tutorial (4 hr) LO3 LO5 LO10 LO11
Week 12 Week 12: Advanced topics Lecture and tutorial (4 hr) LO2 LO3 LO7
Week 13 Week 13: Review and about the exam Lecture (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11

Attendance and class requirements

All classes are compulsory and involve participation in activities which 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 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.

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

  • LO1. 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
  • LO2. use interactive visualisation to communicate the thought process behind complex analytical questions.
  • LO3. communicate the results produced by an analysis pipeline, in oral and written form, including meaningful diagrams
  • LO4. identify ethical and legal issues that may relate to a data analytics task
  • LO5. understand the diverse roles of humans in the data analysis process
  • LO6. understanding the technical issues that are present when data is gathered from or used by humans
  • LO7. demonstrate use of appropriate technologies to address the technical issues of human-centred data analysis
  • LO8. carry out (in guided stages) the whole design and implementation cycle for creating a human-in-the-loop pipeline to analyse a dataset
  • LO9. identify explicit and implicit requirements for carrying out a data analysis task to address specific stakeholder purposes
  • LO10. select statistical techniques appropriate for modelling uncertainty and bias in data, and students can justify their choice
  • LO11. select appropriate techniques for validating their uncertain models, and ability to justify the choice.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
This is a relatively new subject with the first full cohort in 2020. Based on surveys (USS, in-class), interviews (students, tutors), assessment performance (in-lecture, mini-assignments, assignments, exam, there have been many changes and refinements. In addition, materials have been updated with recent information. Also to better accommodate online delivery, there is a completely different type of exam that is creative and involves a type of question that will be introduced in Week 1 and practice through the semester.

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

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