43031 Python Programming for Data Processing
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Credit points: 6 cp
Result type: Grade and marks
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 37373 Programming for Data Analysis AND 41039 Programming 1 AND 42047 Data Processing Using Python AND 430031 Python Programming for Data Processing
Description
The subject focuses on the basics of Python programming with practical applications in data processing and visualisation. Students learn basic programming concepts, common data structures, simple visualisation techniques, how to write custom programs using Jupyter notebooks, and how to perform data processing and visualisation using common Python packages and libraries with practical case studies.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply the fundamentals of the Python programming language. (D.1) |
|---|---|
| 2. | Apply common Python libraries for data processing and visualisation. (D.1) |
| 3. | Design custom Python programs to pre-process and visualise real-world datasets. (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 will be delivered in collaborative face-to-face sessions as workshops focusing on hands-on tutorial approaches designed to help students learn about and immediately practice techniques for Python programming in data processing. Students will need to prepare using materials available on Canvas to use their class time effectively.
The workshops will cover the theoretical aspects of the weekly topics and primarily emphasise hands-on labs in data processing in Python, including writing Python code for data manipulation, processing, and visualisation. The workshops are conducted collaboratively, allowing students to interact and discuss while writing code to solve problems, thereby maintaining peer-to-peer collaboration within the cohort. Students will receive valuable feedback from both their peers and the tutor during these workshops. These workshops will provide the tools necessary to complete assignments. Regular quizzes throughout the semester will enable students to assess their progress to further seek advice and feedback to continue to achieve.
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
Assessment
Assessment task 1: Knowledge 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: | 15 minutes each |
Assessment task 2: Individual project task A – Data Processing
| Intent: | To design, implement, and execute data processing tasks using Python-related packages. |
|---|---|
| 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: | Report |
| Groupwork: | Individual |
| Weight: | 35% |
| Length: | There is no line limit on Python programs. A maximum of 1000 words for the report |
Assessment task 3: Individual project task B – Data Visualisation
| Intent: | To design, implement, and execute data pre-processing and visualisation tasks using Python-related packages. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 2 and 3 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: | 45% |
| Length: | There is no line limit on the Python program. A report with a maximum of 1500 words. |
Minimum requirements
To pass the subject, a student must achieve an overall mark of 50% or more.
Recommended texts
Navlani, A., Idris, I., & Fandango, A. (2021). Python data analysis?: perform data collection, data processing, wrangling, visualization, and model building using Python (Third edition.). Packt Publishing, Limited.
Nelli, F. (2023). Python Data Analytics?: With Pandas, NumPy, and Matplotlib (3rd ed. 2023.). Apress. https://doi.org/10.1007/978-1-4842-9532-8
Asif, M. (2021). Python for Geeks (1st edition.). Packt Publishing.