22577 Introduction to Programming for Data Analysis and Artificial Intelligence
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Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): 22576c Fundamentals of Business Data Analytics
The lower case 'c' after the subject code indicates that the subject is a corequisite. See definitions for details.
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
In this subject, students focus on establishing a solid foundation in Python programming. They master fundamental principles, enabling them to interpret and modify Python code with confidence. However, this course goes beyond basic coding. Students delve into the dynamic realm of artificial intelligence (AI), empowering them to leverage AI for addressing both structured and unstructured data challenges. By immersing themselves in the practical application of AI, utilizing Large Language Models (LLMs) for unstructured problems, and employing random forest algorithms for structured datasets, students unlock the potential of programming and AI. This stimulating course sets the stage for proficient problem-solving in the ever-evolving technological landscape.
Subject learning objectives (SLOs)
| 1. | Create basic scripts using Python syntax and principles |
|---|---|
| 2. | Interpret and modify advanced Python scripts |
| 3. | Use LLMs to effectively resolve unstructured problems in AI |
| 4. | Build Python-based machine learning models to solve complex business problems |
| 5. | Critically evaluate the ethics of AI’s creation and use to ensure responsible application |
Contribution to the development of graduate attributes
Designed to address the growing need for business graduates to be technologically aware, this course cultivates the foundational skills students require to be data-literate. Graduates emerge with intellectual rigour and innovative problem-solving abilities, having learned the foundational python programming principles. Fostering communication and collaboration, the curriculum ensures students can navigate the evolving technological landscape with a team. The acquisition of professional and technological expertise is paramount, enabling graduates to excel in a business analytics role and is instilled via the critical evaluation of AI's ethical dimensions. This exciting journey propels students towards becoming adept, responsible, and innovative contributors to the global business community.
Teaching and learning strategies
This subject uses a variety of teaching and learning strategies to provide students with a hands-on approach to conduct data and analytics via an array of technologies and communicate the outcomes clearly. Classes are interactive and are used to impart important theoretical and practical concepts. Students work through and are assessed on several lab exercises that utilise data from real companies.
Feedback is provided regularly in several different formats: discussions of questions and problems with peers, feedback from the lecturer/tutor on ideas presented and results/feedback of assessment tasks and automated feedback through learning activities on the UTS LMS (Canvas). This feedback is provided in a timely manner throughout the session.
Pre-class work:
Students are required to work through learning content presented on the UTS LMS (Canvas) or other platforms before attending class and acquaint themselves with skills and technologies used in the subject (e.g., Excel, Powerpoint, Python, LLMs).
In-class:
In the class, students will complete questions individually or as a group on concepts and different business problems. Students will enhance their understanding and learning experience by engaging in the solution of the problems in small groups and presenting their ideas to peers or the entire class. Students are encouraged to become actively engaged learners and they need to understand both fundamental methods/concepts as well as the tools to be able to deliver solutions.
Content (topics)
- Learn the foundational skill to read and write python code
- Understand the core functions and libraries used in python to conduct data analysis
- Understand when and how to apply random forest algorithms to solve business problems
- Learn how to utilise complex LLMs to solve unstructured business problems through prompt optimisation, building bespoke GPTs and using code interpreter
- Evaluate AI usage case studies to promote ethical and responsible use of the technology
Assessment
Assessment task 1: Data analysis with Python (Individual)*
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 5 |
|---|---|
| Weight: | 30% |
| Length: | 20 minutes per quiz |
| Criteria: |
*Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Assessment task 2: Code Submission (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 4 |
|---|---|
| Weight: | 40% |
| Length: | A 250-word email. Students will also submit their code, there is no minimum requirement. |
| Criteria: |
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Assessment task 3: Problem solving with LLMs (Individual)
| Objective(s): | This addresses subject learning objective(s): 3 and 5 |
|---|---|
| Weight: | 30% |
| Length: | 3 page submission |
| Criteria: |
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Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
Readings for 22579 and 22577
References
N/A