SUBJECT OUTLINE
41004 AI/Analytics Capstone Project
Course area Delivery
Subject classification
Credit points Requisite(s)
UTS: Information Technology Autumn 2020; City
Artificial Intelligence and Data Analytics program 6cp
(31250 Introduction to Data Analytics AND 31272c Project Management and the Professional AND (90 Credit Points in spk(s): C10148 Bachelor of Science in Information Technology OR 90 Credit Points in spk(s): C10345 Bachelor of Science in Information Technology Diploma in Information Technology Professional Practice OR 90 Credit Points in spk(s): C10245 Bachelor of Science in Information Technology Bachelor of Laws OR 90 Credit Points in spk(s): C10239 Bachelor of Science in Information Technology Bachelor of Arts in International Studies OR 90 Credit Points in spk(s): C10224 Bachelor of Mathematics and Computing Bachelor of Arts in International Studies OR 90 Credit Points in spk(s): C10219 Bachelor of Business Bachelor of Science in Information Technology OR 90 Credit Points in spk(s): C10158 Bachelor of Mathematics and Computing OR 90 Credit Points in spk(s): C10152 Bachelor of Science in Information Technology Diploma in Information Technology Professional Practice))
Grade and marks
Result type
Attendance: 1–2hpw (on-campus meeting with mentor), 3 x 3hpw (on-campus presentation sessions) Recommended studies: knowledge of database technologies
Subject coordinator
Prof Guandong Xu
Email: Guandong.Xu@uts.edu.au
Ph: 9514 3788
Students may contact the subject coordinator on:
Project requirements and report; Coordinating with business mentors; Assignments and presentations due date.
All email sent to subject coordinators, tutors or lecturers must have a clear subject line that states the subject number followed by the subject of the email [e.g. Subject 32702, Request for Extension], and must be sent from your UTS email address.
Consultation hours: Check the UTSOnline Contact section for details on consultation hours.
Teaching staff
Tutor: Dr Shaowu Liu, shaowu.liu@uts.edu.au
Subject description
This subject brings together the full skill set learned by students in data analytics. Students undertake a data analytics project as part of a team, manage the investigation, document their progress, communicate their findings and reflect on their learning.
Projects are set out to solve real-world problems in academic research or industry and may include:
1. data generation/collection
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1. data generation/collection
2. data processing/cleaning
3. data modelling/analysis
4. interpretation and communication of results.
Throughout the project, students are supported by an academic mentor and assessment through written reports and an oral presentation is in line with expectations from academic/industry.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
1. Conceptualise, design, plan and implement a data analytics project;
2. Choose appropriate data analytics methods to achieve outcomes, apply them and evaluate their efficacy;
3. Communicate the results of the data analytics investigation verbally and in written form at a level appropriate for a business audience; and
4. Contribute effectively in a data analytics team to achieve the desired outcomes.
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
Socially Responsible: FEIT graduates identify, engage, interpret and analyse stakeholder needs and cultural perspectives, establish priorities and goals, and identify constraints, uncertainties and risks (social, ethical, cultural, legislative, environmental, economics etc.) to define the system requirements. (B.1)
Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
Teaching and learning strategies
This subject is project based. In the first week, students inform themselves on available projects, clarify project expectations, choose a project and form groups using UTSOnline.
At the start of the session, students will attend a class on methodological issues and introduce their projects and groups.
Throughout the remainder of the session, students develop their projects through collaborative group work assisted by weekly group meetings with their project mentors/sponsors. These meetings are a valuable source of verbal feedback and give students the opportunity to clarify their understanding and to discuss their research to gain deeper considerations to include in their projects. To complement the project based learning and to provide formal feedback, students will be able to access reading materials and a discussion board on UTSOnline and submit written reports (see activates detailed in the assessment section) and deliver formal presentations of the project outcomes at the end of
the session.
Content (topics)
In this subject students will apply Data Analytics Methodologies (CRISP-DM, SEMMA), Data Analytics Project Management (resource allocation, working in teams, planning, running meetings), use their skills for Data Interpretation and Communication of Results.
Program
Week/Session Dates Description
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1 9 Mar
Pause 16 Mar
2 23 Mar
3 30 Mar
4 6 Apr
5 13 Apr
6 20 Apr
StuVac 27 Apr
7 4 May
8 11 May
9 18 May
10 25 May
11 1 Jun
12 8 Jun
Students read project descriptions at UTS Online and inform themselves about the scope and expectations of each project.
Mentors give brief information about the projects in class.
Pause Week
Students choose projects and form groups after class, and organise a meeting with their project mentor to gather more detailed information on the project.
Group Meetings
Group meetings
Group meetings
Notes:
Assignment 1 due on 11:59pm, 17/04/2020
Group meetings
StuVac – no class
Mid-project update and presentation
Group meetings
Notes:
Assignment 2 due on 11:59pm, 15/05/2020
Group meetings
Group meetings
Group meetings
Final presentation and report
Notes:
Assignment 3 due on 11:59pm, 12/06/2020
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Assessment
Details about assignments and submission procedures are provided on the subject website. Assignments are to be submitted to UTS Online
Assessment task 1: Plan and proposal
Objective(s):
Type: Groupwork: Weight: Task:
Length: Due:
Criteria:
Further information:
Thisassessmenttaskaddressesthefollowingsubjectlearningobjectives(SLOs): 1 and 4
This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):
B.1 and E.1
Report
Group, group and individually assessed 20%
Based on the data mining problem and the group that you have formed, this task involves describing your group, the role of each team member, your proposal for solving the data mining problem and your plan for solving it.
Assignments will be assessed based on the quality and plausibility of the proposal and plan and the completeness of team roles.
The task requires submission of a report of 10 pages in an 11 or 12 point font.
11.59pm Friday 17 April 2020 Week 5
Assignments will be assessed based on the quality and plausibility of the proposal and plan and the completeness of team roles.
An electronic copy of the report should be submitted on UTS Online before the due date. Marks with feedback will be given within 2 to 3 weeks of submission.
Assessment task 2: Mid-project update and presentation
Objective(s):
Type: Groupwork: Weight:
This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4
This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):
C.1 and E.1
Report
Group, group and individually assessed 30%
Task: In this assignment you will give a progress report on your work in the project to the client. Your group 23/03/2020 (Autumn 2020) © University of Technology Sydney Page 4 of 8
Task:
Length: Due:
Further information:
In this assignment you will give a progress report on your work in the project to the client. Your group will describe the results of your data exploration and give initial findings as well as an updated project plan. This will involve a report as well as a short formal presentation to the client.
Assignments will be assessed based on depth of data exploration, quality of understanding of the data mining problem as expressed through the initial findings and the understandability, professionalism and depth of the client presentation.
A report of around 20 pages in an 11 or 12 point font and a 10 minute presentation to the client.
11.59pm Friday 15 May 2020 Week 8
An electronic copy of the report should be submitted on UTS Online before the due date. Marks with feedback will be given within 2 to 3 weeks of submission.
Assessment task 3: Final project and presentation
Objective(s):
Type: Groupwork: Weight: Task:
Length:
Due:
Further information:
Thisassessmenttaskaddressesthefollowingsubjectlearningobjectives(SLOs): 1, 2, 3 and 4
This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):
C.1 and E.1
Project
Group, group and individually assessed 50%
Your group will deliver a report to the client describing the results of the data analytics investigation. Your group will give a formal presentation describing the project and results to an executive panel from the client organisation (think of it as the company board).
A report of around 50 pages in an 11 or 12 point font and a 10 minute presentation to the client executive panel.
11.59pm Friday 12 June 2020 Week 12
An electronic copy of the report should be submitted on UTS Online before the due date. Marks with feedback will be given within 2 to 3 weeks of submission.
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Required texts
The following books are strongly recommended.
1. Introduction to Data Mining, P.-N. Tan, M. Steinbach and V. Kumar, Addison-Wesley, 2005.
2. Data Mining: Concepts and Techniques, J. Han, M. Kamber, and J. Pei, Morgan Kaufmann, 2012
Recommended texts
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You might find the following texts useful.
1. Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006.
2. Witten, I. H. and Frank, E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, CA, 2000.
3. Graham Williams (2011). Data Mining with Rattle and R, Springer.
References
The UTS Coursework Assessments Policy and Procedures at www.gsu.uts.edu.au/policies/coursework-assessments.html.
Graduate attribute development
For a full list of the faculty’s graduate attributes refer to the FEIT Graduate Attributes webpage.
For the contribution of subjects taken in the Bachelor of Engineering (Honours) or Master of Professional Engineering to the Engineers Australia Stage 1 Competencies, see the faculty’s Graduate Attributes and the Engineers Australia Stage 1 Competencies webpage.
Assessment: faculty procedures and advice Marking criteria
Marking criteria for each assessment task will be available on the Learning Management System: UTS Online.
Extensions
When, due to extenuating circumstances, you are unable to submit or present an assessment task on time, please contact your subject coordinator before the assessment task is due to discuss an extension. Extensions may be granted up to a maximum of 5 days (120 hours). In all cases you should have extensions confirmed in writing.
Special consideration
If you believe your performance in an assessment item or exam has been adversely affected by circumstances beyond your control, such as a serious illness, loss or bereavement, hardship, trauma, or exceptional employment demands, you may be eligible to apply for Special Consideration.
Late penalty
Work submitted late without an approved extension is subject to a late penalty of 10 per cent of the total available marks deducted per calendar day that the assessment is overdue (e.g. if an assignment is out of 40 marks, and is submitted (up to) 24 hours after the deadline without an extension, the student will have four marks deducted from their awarded mark). Work submitted after five calendar days is not accepted and a mark of zero is awarded.
For some assessment tasks a late penalty may not be appropriate – these are clearly indicated in the subject outline. Such assessments receive a mark of zero if not completed by/on the specified date. Examples include:
a. weekly online tests or laboratory work worth a small proportion of the subject mark, or
b. online quizzes where answers are released to students on completion, or
c. professionalassessmenttasks,wheretheintentionistocreateanauthenticassessmentthathasanabsolute
submission date, or
d. take-home papers that are assessed during a defined time period, or
e. pass/fail assessment tasks.
Querying results
If you wish to query the result of an assessment task or the final result for a subject:
Assessment task: query the result with the Subject Coordinator within 5 working days of the date of release of the result
Final subject result: submit an application for review within 5 working days of the official release of the final subject result.
Academic liaison officer
Academic liaison officers (ALOs) are academic staff in each faculty who assist students experiencing difficulties in
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Academic liaison officers (ALOs) are academic staff in each faculty who assist students experiencing difficulties in
their studies due to: disability and/or an ongoing health condition; carer responsibilities (e.g. being a primary carer for small children or a family member with a disability); and pregnancy.
ALOs are responsible for approving adjustments to assessment arrangements for students in these categories. Students who require adjustments due to disability and/or an ongoing health condition are requested to discuss their situation with an accessibility consultant at the Accessibility Service before speaking to the relevant ALO.
Statement about assessment procedures and advice
This subject outline must be read in conjunction with the Coursework Assessments policy and procedures.
Statement on copyright
Teaching materials and resources provided to you at UTS are protected by copyright. You are not permitted to re-use these for commercial purposes (including in kind benefit or gain) without permission of the copyright owner. Improper or illegal use of teaching materials may lead to prosecution for copyright infringement.
Statement on plagiarism Plagiarism and academic integrity
At UTS, plagiarism is defined in Rule 16.2.1(4) as: ‘taking and using someone else’s ideas or manner of expressing them and passing them off as … [their] own by failing to give appropriate acknowledgement of the source to seek to gain an advantage by unfair means’.
The definition infers that if a source is appropriately referenced, the student’s work will meet the required academic standard. Plagiarism is a literary or an intellectual theft and is unacceptable both academically and professionally. It can take a number of forms including but not limited to:
copying any section of text, no matter how brief, from a book, journal, article or other written source without duly acknowledging the source
copying any map, diagram, table or figure without duly acknowledging the source
paraphrasing or otherwise using the ideas of another author without duly acknowledging the source
re-using sections of verbatim text without using quote marks to indicate the text was copied from the source (even if a reference is given).
Other breaches of academic integrity that constitute cheating include but are not limited to:
submitting work that is not a student’s own, copying from another student, recycling another student’s work, recycling previously submitted work, and working with another student in the same cohort in a manner that exceeds the boundaries of legitimate cooperation
purchasing an assignment from a website and submitting it as original work
requesting or paying someone else to write original work, such as an assignment, essay or computer program, and submitting it as original work.
Students who condone plagiarism and other breaches of academic integrity by allowing their work to be copied are also subject to student misconduct Rules.
Where proven, plagiarism and other breaches of misconduct are penalised in accordance with UTS Student Rules Section 16 – Student misconduct and appeals.
Avoiding plagiarism is one of the main reasons why the Faculty of Engineering and IT is insistent on the thorough and appropriate referencing of all written work. Students may seek assistance regarding appropriate referencing through UTS: HELPS.
Work submitted electronically may be subject to similarity detection software. Student work must be submitted in a format able to be assessed by the software (e.g. doc, pdf (text files), rtf, html).
Further information about avoiding plagiarism at UTS is available.
Retention of student work
The University reserves the right to retain the original or one copy of any work executed and/or submitted by a student as part of the course including, but not limited to, drawings, models, designs, plans and specifications, essays, programs, reports and theses, for any of the purposes designated in Student Rule 3.9.2. Such retention is not to affect any copyright or other intellectual property right that may exist in the student’s work. Copies of student work may be
retained for a period of up to five years for course accreditation purposes. Students are advised to contact their subject
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retained for a period of up to five years for course accreditation purposes. Students are advised to contact their subject coordinator if they do not consent to the University retaining a copy of their work.
Statement on UTS email account
Email from the University to a student will only be sent to the student’s UTS email address. Email sent from a student to the University must be sent from the student’s UTS email address. University staff will not respond to email from any other email accounts for currently enrolled students.
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