430031 Python Programming for Data Processing
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Credit points: 6 cp
Result type: Grade and marks
Requisite(s): 12 credit points of completed study in spk(s): CBK91894 12cp Foundation Option (Business Analytics) AND 260776 Foundation of Business Analytics AND 260777 Data Processing Using SAS AND 570100 Data Ethics and Regulation
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 37373 Programming for Data Analysis AND 41039 Programming 1 AND 420047 Data Processing Using Python AND 43031 Python Programming for Data Processing
Description
The subject focuses on the basics of python programming with practical applications to data processing analysis and visualisation. Students learn basic programming concepts, common data structures, simple visualisation techniques, how to write custom programs using iPython notebooks, and how to perform data processing and exploratory data analysis using common Python packages and libraries with practical case studies. The subject is delivered online with companion workshops for Q/A.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply the fundamental principles and syntax of Python programming. (D.1) |
|---|---|
| 2. | Define and explain common data structures in Python. (D.1) |
| 3. | Design common Python libraries for data manipulation and visualisation. (D.1) |
| 4. | Design and code custom Python programs for data processing and analysis. (C.1) |
| 5. | Construct custom Python programs for simple data visualisation. (C.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
Teaching and learning strategies
This subject is taught fully online. Each week is self-paced through the learning management system. Students are expected to complete various learning activities throughout the week. Activities such as coding exercises provide opportunities to learn, and apply the knowledge gained in a practical manner. Online “Zoom” live sessions will be held throughout the subject to allow students to interact with staff and fellow students, ask questions and receive clarification and feedback. Students are encouraged to actively provide feedback and interact with staff and fellow students.
Content (topics)
- Introduction to programming in Python
- Decisions and iterations in Python
- Data structures in Python
- Functions and libraries
- Reading data from multiple sources
- Exception handling
- Data manipulation
- Data pre-processing
- Introduction to data visualisation
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Assessment
Assessment task 1: Quizzes
| Intent: | To apply Python fundamentals and libraries for data processing and visualisation. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 2 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 20% |
| Length: | 10 minutes each |
Assessment task 2: Python Project
| Intent: | Evaluate students' capacity to use acquired skills of Python programming learnt in the course. These include understanding the business problems and their exploration from the given dataset using a variety of techniques using Python programming language. Finally, to present them to the stakeholders using visualization techniques. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 2 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
| Type: | Laboratory/practical |
| Groupwork: | Individual |
| Weight: | 45% |
| Length: | Initial Abstract report must be 300 words or lesswords. There is no line limit on Python programs. |
Assessment task 3: Python Project Documentation
| Intent: | Evaluate students' capacity to document and pitch their findings effectively. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 35% |
| Length: | A report with a maximum of 1500 words. Video 2-3 mins |
Minimum requirements
To pass the subject, a student must achieve an overall mark of 50% or more.