Introduction to Data Science
Duration
- One Semester or Equivalent
Contact hours
- 48 hours face to face + blended
On-campus unit delivery combines face-to-face and digital learning.
2023 teaching periods
| Hawthorn Higher Ed. Semester 1 |
||
|---|---|---|
Dates: Results: Last self enrolment: Census: Last withdraw without fail: |
Prerequisites
Admission to MA-ITPC1 Master of Information Technology (Professional Computing)
Concurrent pre-requisites
Assumed knowledge
Basic understanding of Database concepts
Basic understanding of Database concepts
Aims and objectives
This unit aims to develop students’ conceptual and practical understanding of the field of data science in the contexts of real-world applications. The students will learn about basic concepts, key techniques and popular tools in various aspects of data science, following the lifecycle of a practical data science project which involves data collection, management, wrangling, analytics and visualisation. They 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 science relevant tasks.
Unit Learning Outcomes (ULO)
Students who successfully complete this unit will be able to:
1. Demonstrate knowledge of fundamental concepts, key techniques and popular tools in data science, and understanding of the lifecycle of a practical data science project
2. Demonstrate understanding of the lifecycle of a practical data science project and how to identify and define data science relevant tasks in practical scenarios
3. Critically analyse, evaluate and apply given techniques and tools to solve given data science relevant problems
Unit Learning Outcomes (ULO)
Students who successfully complete this unit will be able to:
1. Demonstrate knowledge of fundamental concepts, key techniques and popular tools in data science, and understanding of the lifecycle of a practical data science project
2. Demonstrate understanding of the lifecycle of a practical data science project and how to identify and define data science relevant tasks in practical scenarios
3. Critically analyse, evaluate and apply given techniques and tools to solve given data science relevant problems
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Hawthorn
Type | Hours per week | Number of Weeks | Total |
On Campus Lecture | 2 | 12 | 24 |
| On Campus Class in Computer Lab | 2 | 12 | 24 |
Unspecified Activities Independent Learning | 8.5 | 12 | 102 |
TOTAL | 150 hours |
Assessment
| Types | Individual or Group task | Weighting | Assesses attainment of these ULOs |
| Assignments | Individual | 30% - 40% | 1, 2 |
| In-Class Tests | Individual | 10% - 20% | 1, 2, 3 |
| Examination | Individual | 50% - 60% | 1, 2, 3 |
Minimum requirements to pass this unit
As the minimum requirements of assessment to pass a unit and meet all Unit Learning Outcomes to a minimum standard, a student must achieve:
• An aggregate mark of 50% or more, and
• At least 40% in the final exam.
Students who do not successfully achieve hurdle requirement (ii) will receive a maximum of 44% as the total mark for the unit and will not be eligible for a conceded pass.
As the minimum requirements of assessment to pass a unit and meet all Unit Learning Outcomes to a minimum standard, a student must achieve:
• An aggregate mark of 50% or more, and
• At least 40% in the final exam.
Students who do not successfully achieve hurdle requirement (ii) will receive a maximum of 44% as the total mark for the unit and will not be eligible for a conceded pass.
Content
• Foundations of data science
• Data science in real world
• Lifecycle of practical data science projects
• Data collection: basic concepts, techniques and tools
• Data management: basic concepts, techniques and tools
• Data wrangling: basic concepts, techniques and tools
• Data analytics: basic concepts, techniques and tools
• Data visualisation: basic concepts, techniques and tools
• Applications of given data science relevant techniques and tools to deal with specific data science relevant tasks
• Data science in real world
• Lifecycle of practical data science projects
• Data collection: basic concepts, techniques and tools
• Data management: basic concepts, techniques and tools
• Data wrangling: basic concepts, techniques and tools
• Data analytics: basic concepts, techniques and tools
• Data visualisation: basic concepts, techniques and tools
• Applications of given data science relevant techniques and tools to deal with specific data science relevant tasks
Study resources
- Reading materials.
Reading materials
A list of reading materials and/or required texts will be made available in the Unit Outline.