Data Analytics with Python
Duration
- One Semester or equivalent
Contact hours
- 24 hours
On-campus unit delivery combines face-to-face and digital learning.
Aims and objectives
This unit aims to develop students’ conceptual and practical understanding of the field of data analytics in the contexts of real-world applications. The students will learn about basic concepts, techniques and popular tools in various aspects of data analytics, following the lifecycle of a practical data analytics project which involves data collection, wrangling, analytics and visualisation.
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
| Hawthorn | |||||
|---|---|---|---|---|---|
| Activity Type | Activity | Total Hours | Number of Weeks | Hours Per Week | Optional - Activity Details |
| Face to Face Contact | Workshop | 24 | 8 weeks | 3 | |
| Unspecified Learning Activities | Independent Learning | 126 | 8 weeks | 15.75 | |
| Total Hours: | 150 | Total Hours (per week): | 18.75 | ||
| Swinburne Online | |||||
|---|---|---|---|---|---|
| Activity Type | Activity | Total Hours | Number of Weeks | Hours Per Week | Optional - Activity Details |
| Online | Directed Online Learning and Independent Learning | 150 | 10 weeks | 15 | |
| Total Hours: | 150 | Total Hours (per week): | 15 | ||
Assessment
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Content
- Introduction to advanced analytics
- Introduction to Python Programming
- Data ingestion and wrangling
- SQL for Data Analysis
- Data Visualisation using Matplotlib
- Data Visualisation using Seaborn
- Improving the Visualisations in Python
- Dashboard and Interactive Visualisations
Study resources
- References.
References
A list of reading materials will be made available in the Unit Outline.