36116 iLab: Internship Project
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2024 is available in the Archives.
Credit points: 12 cp
Result type: Grade, no marks
Requisite(s): 36100 Data Science for Innovation AND 36103 Statistical Thinking for Data Science AND 36106 Machine Learning Algorithms and Applications
Description
Students undertake a Data Science related internship with a host organisation for approximately 200 hours of work (12cp). This subject gives students an opportunity to apply what they have learnt from previous coursework in a real world project and experience how a data science project is conducted in a business environment.
The student must work as part of a project team in a professional environment, supervised by a representative of the host organisation (the host can be either an external industry partner or a UTS unit/faculty).
The internship project can be a research project, an analytical project of a given dataset, or providing consultation service, and can be conducted either individually or in a group.
The specific tasks, milestones, deliverables, terms and timeframe of the internship must be negotiated and agreed as a learning contract between the student and the host organisation, and approved by the subject coordinator before the project starts.
Finally, students report on the outcomes of their internship and their internship experience to develop an appreciation of how Data Science is applied in a workplace.
Enrolment in this subject is by e-request only, after an internship has been agreed between the student and the host organisation and approved by the Subject Coordinator in CareerHub.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply data science knowledge and skills effectively in a workplace |
|---|---|
| 2. | Identify the unknown patterns in big data sets and opportunities for analytic innovation by combining new sources of data and different analytic models |
| 3. | Contribute productively and analytically to data science and innovation projects in organisations |
| 4. | Engage respectfully and ethically and communicate professionally with organisational practices and stakeholders in real-world contexts |
| 5. | Create value for industry clients through problem finding and problem solving in organisations and applying human centered approaches to data science investigations |
| 6. | Reflect on experiences critically and communicate outcomes of the internship effectively |
| 7. | Take a constructive role in defining and executing solutions to current data challenges, balancing specific stakeholder needs and values with organisational priorities |
Contribution to the development of graduate attributes
The subject addresses the following graduate attributes (GA):
GA 1 Sociotechnical systems thinking
GA 2 Creative, analytical and rigorous sense making
GA 3 Create value in problem solving and inquiry
GA 4 Persuasive and robust communication
GA 5 Ethical citizenship and leadership
Teaching and learning strategies
Students engage in a professional workplace context and undertake experiential, work-based learning to develop their professional identities, and improve their understanding of the professional practice of data science.
During the internship, students will observe and learn about the application of data science, participate in workplace tasks, and receive ongoing feedback from their workplace and academic supervisors. Students will also critically reflect on their participation, workplace performance and their responses to feedback and business requirements in a reflective journal. This enables them to engage in a unique learning journey in a workplace-based learning environment during their placement.
There are no formal classes in this subject. On-going support and help from subject coordinator and industry supervisors are available throughout the placement.
Assessment
Assessment task 1: Internship plan
| Intent: | Assessment 1 supports you and you industry supervisor with preparing for a mutually beneficial internship experience |
|---|---|
| Objective(s): | This task addresses the following subject learning objectives: , 1 and 4 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 10% |
Assessment task 2: Progress report
| Intent: | Assessment 2 assists you and your industry supervisor with evaluating internship progress |
|---|---|
| Objective(s): | This task addresses the following subject learning objectives:
|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 10% |
Assessment task 3: Placement report
| Intent: | Assessment 3 supports you and your industry supervisor with project delivery |
|---|---|
| Objective(s): | This task addresses the following subject learning objectives:
|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 50% |
Assessment task 4: Reflection report
| Intent: | Assessment 4 supports you in analysing, self-evaluating and critically reflecting on the experience, and in identifying future development opportunities |
|---|---|
| Objective(s): | This task addresses the following subject learning objectives: 6 |
| Type: | Reflection |
| Groupwork: | Individual |
| Weight: | 30% |
Minimum requirements
Students must attempt all assessment tasks and achieve an overall pass mark in order to pass this subject.