Data Science Principles
Duration
- One Semester or equivalent
Contact hours
- 48 hours face to face + Blended
On-campus unit delivery combines face-to-face and digital learning. For Online unit delivery, learning is conducted exclusively online.
2023 teaching periods
Swinburne Online Teaching Period 3 |
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Dates: Results: Last self enrolment: Census: Last withdraw without fail: |
Aims and objectives
Unit Learning Outcomes (ULO)
Students who successfully complete this unit will be able to:
1. Appreciate the roles of data science and Big Data analytics in organisational contexts.
2. Compare and analyse he key concepts, techniques and tools for discovering, analysing, visualising and presenting data.
3. Describe the processes within the Data Analytics Lifecycle.
4. Analyse organisational problems and formulate them into data science tasks.
5. Evaluate suitable techniques and tools for specific data science tasks.
6. Develop and execute an analytics plan for a given case study.
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Type | Hours per week | Number of Weeks | Total |
Live Online Lecture | 2 | 12 | 24 |
On Campus Class (Computer Lab) | 2 | 12 | 24 |
Online Directed Online Learning and Independent Learning | 1 | 12 | 12 |
Unspecified Activities Independent Learning | 7.5 | 12 | 90 |
TOTAL | 150 hours |
Swinburne Online
Type | Hours per week | Number of Weeks | Total |
Online Contact Directed Online Learning and Independent Learning | 12.5 | 12 | 150 |
TOTAL | 150 hours |
Assessment
Types | Individual or Group task | Weighting | Assesses attainment of these ULOs |
Assignment 1 | Individual/Group | 10-30% | 4,5,6 |
Assignment 2 | Individual/Group | 10-30% | 1,2,3 |
Online Test 1 | Individual | 5-15% | 4,5,6 |
Online Test 2 | Individual | 5-15% | 2,3,4,5,6 |
Online Test | Individual | 30-50% | 1,2,3,4,5,6 |
Content
• Roles of Data Science for Business
• Contemporary data structures and management
• Data Science tools, techniques and technologies
• The Data Analytics Lifecycle
• Analytical techniques and methods
• Analytics plan development
Study resources
- Reading materials.