Fundamentals of Data Management
Duration
- One Semester or equivalent
Contact hours
- 24 Hours
On-campus unit delivery combines face-to-face and digital learning.
2023 teaching periods
Hawthorn Higher Ed. Semester 2 |
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Dates: Results: Last self enrolment: Census: Last withdraw without fail: |
Swinburne Online Teaching Period 2 |
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Dates: Results: Last self enrolment: Census: Last withdraw without fail: |
Aims and objectives
This unit introduces students to a range of skills, techniques, technologies and fundamental computer science concepts related to managing data within software systems. Students will learn how to organise data, efficiently search and sort information, as well as apply techniques to optimise these operations. Data management is a critical component in most software systems – knowledge and skills gained in this unit can be applied to a range of different solution domains from enterprise systems to smaller desktop and mobile applications.
Unit Learning Outcomes (ULO)
On successful completion of this module the learner will be able to:
1. Appreciate set theory, ternary logic, and algorithmic complexity in the context of data management
2. Select and apply appropriate techniques, tools and methods to sort, search and transform data stored in a variety of data formats
3. Explain the role of data types, data representation, indexing and schemas in managing data, and use methods to validate that data matches an expected schema
4. Select and apply appropriate methods to efficiently store, insert and retrieve data appreciating the underlying trade-offs between different strategies
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Activity Type | Activity | Total Hours | Number of Weeks | Hours Per Week | Optional - Activity Details |
---|---|---|---|---|---|
Face to Face Contact | Tutorials in Computer Labs | 24 | 12 weeks | 2 | No Description |
Online Contact | Online Learning Activities | 24 | 12 weeks | 2 | No Description |
Unspecified Learning Activities | Independent Learning | 100 | 12 weeks | 8.33 | No Description |
Total Hours: | 148 | Total Hours (per week): | 12.33 |
Assessment
Types | Individual/Group Role | Weighting | Unit Learning Outcomes (ULOs) |
Portfolio | Individual | 100% | 1,2,3,4 |
Test | Individual | 0% | 1,2,3,4 |
Hurdle
As the minimum requirements of assessment to pass the unit and meet all Unit Learning Outcomes to a minimum standard, a student must achieve:
(i) An aggregate mark of 50% or more, and
(ii) A pass grade for the non-reportable (pass/fail) test.
As the minimum requirements of assessment to pass the unit and meet all Unit Learning Outcomes to a minimum standard, a student must achieve:
(i) An aggregate mark of 50% or more, and
(ii) A pass grade for the non-reportable (pass/fail) test.
Students who do not successfully achieve hurdle requirements (ii) will receive a maximum of 45% as the total mark for the unit.
Content
Data Fundamentals
- Set theory
- Data types, schema and validation
- Ternary logic and dealing with null values
Data representation
- Tabular representation (Text, CSV)
- Hierarchical representation (XML and JSON)
- Relational representation and basics of normalisation
Data processing and retrieval
- Searching, sorting and algorithmic complexity
- Text processing tools and techniques
- Importing, exporting and transforming data
- Indexing, B+ -Trees, algorithms and trade-offs
- Querying with regular expressions, SQL and tree-matching expressions
- Transactions and concurrent data access
Study resources
- References.
References
A list of reading materials and/or required texts will be made available in the Unit Outline.