Data Science Using 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.
Prerequisites
COS60012 Data Analytics with PythonAims 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 gain the understanding of how to identify and define data science relevant tasks in practical scenarios, and acquire the ability to apply given techniques and tools to resolve given data analytics relevant tasks.
Unit Learning Outcomes (ULO)
On successful completion of this module the learner will be able to:
1. Design machine learning models to inform organisational decision-making
2. Apply relevant algorithms to analyse data and make predictions
3. Test, evaluate, improve and deploy an effective machine learning model
4. Justify the approach taken to create machine learning models within different organisational contexts
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Hawthorn
Type | Hours per week | Number of Weeks | Total |
Live Online Workshop | 1.5 | 8 | 12 |
On Campus Workshop (Comp Lab) | 3 | 8 | 24 |
Online Contact Activities | 1.5 | 8 | 12 |
Unspecified Activities Independent Learning | 12.75 | 8 | 102 |
TOTAL | 150 hours |
Swinburne Online
Type | Hours per week | Number of Weeks | Total |
Online Contact Activities | 15 | 10 | 150 |
TOTAL | 150 hours |
Assessment
Types | Individual/Group Role | Weighting | Unit Learning Outcomes (ULOs) |
Assignment 1 | Individual/Group | 25-35% | 1 |
Assignment 2 | Individual | 65-75% | 2,3,4,5 |
Content
- Introduction to Data Science
- Probability
- Statistics
- Predictive Analytics with Python
- Exploratory analysis with R
- Machine Learning with R
- Natural Language Processing and Text Mining
- Demonstrating business value
Study resources
- Reading materials.
Reading materials
A list of reading materials/and or text books will be made available in the Unit Outline.