Data Science with Python and R
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 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. Implement and justify an appropriate data model for specific requirements
2. Initiate statistical methods to test a hypothesis
3. Analyse data to identify trends, insights, and make recommendations
4. Articulate results to stakeholders through visualisations
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 Contact Directed Online Learning and Independent Learning (Asynchronous) | 1.5 | 8 | 12 |
Unspecified Learning Activities Independent Learning | 8.5 | 12 | 102 |
TOTAL | 150 hours |
Swinburne Online
Type | Hours per week | Number of Weeks | Total |
Online Contact Directed Online Learning and Independent Learning | 15 | 10 | 150 |
TOTAL | 150 hours |
Assessment
Types | Individual/Group Role | Weighting | Unit Learning Outcomes (ULOs) |
Assignment 1 | Individual/Group | 30-50% | 1,3 |
| Assignment 2 | Individual | 50-70% | 1,2,3,4 |
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 will be made available in the Unit Outline.