94697 Data Science Internship A
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Credit points: 6 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 100 to 120 hours of work. 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.
As part of the assessment, students need to 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. | Contribute productively and analytically to data science and innovation projects in organisations |
| 3. | Engage respectfully and ethically and communicate professionally with organisational practices and stakeholders in real-world contexts |
| 4. | Demonstrate appropriate professional attitudes, behaviour and performance in a business environment |
| 5. | Reflect on experiences critically and communicate outcomes of the internship effectively |
Course intended learning outcomes (CILOs)
This subject contributes specifically to the development of the following course intended learning outcomes:
- Apply and assess data science concepts, theories, practices and tools for designing and managing data discovery investigations in professional environments that draw upon diverse data sources, including efforts to shed light on underrepresented components (2.4)
- Explore, interrogate, generate, apply, test and evaluate problem-solving strategies to extract economic, business, social, strategic or other value from data (3.1)
- Develop, test, justify and deliver data project propositions, methodologies, analytics outcomes and recommendations for informing decision-making, both to specialist and non-specialist audiences (4.3)
- Engage in active, reflective practice that supports flexible navigation of assumptions, alternatives and uncertainty in professional data science contexts (5.1)
- Take a leadership role in promoting positive change in data science contexts, recognising individual, organisational and community issues (5.3)
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, together with an understanding of its 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, project progress and their responses to feedback and business requirements in a reflective journal. This allows them to engage in a unique learning journey in a workplace based learning environment during their placement.
On-going support and help from subject coordinator, fellow students and industry supervisors are available throughout the semester.
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, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 2.4, 4.3 and 5.3 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 20% |
Assessment task 2: Placement report
| Intent: | Assessment 2 supports you and your industry supervisor with project delivery |
|---|---|
| Objective(s): | This task addresses the following subject learning objectives: 2 This assessment task contributes to the development of course intended learning outcome(s): 3.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 50% |
Assessment task 3: Reflection report
| Intent: | Assessment 3 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: 5 This assessment task contributes to the development of course intended learning outcome(s): 5.1 |
| 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.