Data Analytics with Python
Duration
- One Semester or equivalent
Contact hours
- 24 hours face to face + Blended
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 Learning Outcomes (ULO)
On successful completion of this module the learner will be able to:
1. Exhibit advanced data analysis processes using Python programming
2. Articulate relevant data analytics use cases using different Python libraries
3. Present exploratory visualisations using Python
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Hawthorn
Type | Hours per week | Number of Weeks | Total |
Face to Face Contact Workshop | 3 | 8 | 24 |
Online Contact Class (Live Online) | 1.5 | 8 | 12 |
Online Directed online learning (asynchronous) | 1.5 | 8 | 12 |
Unspecified Learning Activities Independent Learning | 12.75 | 8 | 102 |
TOTAL | 150 hours |
Swinburne Online
Type | Hours per week | Number of Weeks | Total |
Online Directed online learning | 15 | 10 | 150 |
TOTAL | 150 hours |
Assessment
Types | Individual/Group Role | Weighting | Unit Learning Outcomes (ULOs) |
Assignment 1 | Individual/Group | 40-60% | 1,2 |
| Assignment 2 | Individual | 40-60% | 1,2,3 |
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
- Reading materials.
Reading materials
A list of reading materials will be made available in the Unit Outline.