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We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.
Unit of study_
DATA2001: Data Science: Big Data and Data Diversity
This course focuses on methods and techniques to efficiently explore and analyse large data collections. Where are hot spots of pedestrian accidents across a city? What are the most popular travel locations according to user postings on a travel website? The ability to combine and analyse data from various sources and from databases is essential for informed decision making in both research and industry. Students will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects, such as relational, semi-structured, time series, geospatial, image, text. As well as reinforcing their programming skills through experience with relevant Python libraries, this course will also introduce students to the concept of declarative data processing with SQL, and to analyse data in relational databases. Students will be given data sets from, eg. , social media, transport, health and social sciences, and be taught basic explorative data analysis and mining techniques in the context of small use cases. The course will further give students an understanding of the challenges involved with analysing large data volumes, such as the idea to partition and distribute data and computation among multiple computers for processing of 'Big Data'.
| Code | DATA2001 |
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| Academic unit | Computer Science |
| Credit points | 6 |
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Prerequisites:
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DATA1002 OR DATA1902 OR INFO1110 OR INFO1910 OR INFO1903 OR INFO1103 |
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Corequisites:
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None |
| Prohibitions:
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DATA2901 |
At the completion of this unit, you should be able to:
- LO1. use appropriate Python libraries to automate data science activities on diverse kinds of data
- LO2. ingest, combine and summarise data from a variety of data models
- LO3. demonstrate experience with handling datasets of diverse kinds of data, including relational, semi-structured, time series, geo-location, image, text, including experience to combine data of different types
- LO4. understand and produce declarative queries to extract appropriate information from data sets, including competence in use of SQL
- LO5. understand the main challenges analysing 'big data': data volume, variety, velocity, veracity
- LO6. understand the impact of data volume on data processing, and have awareness of approaches to address this such as indexing, compression, data partitioning, and distributed processing frameworks (Hadoop).
- LO7. demonstrate awareness of privacy issues when working with data
Unit outlines
Unit outlines will be available 2 weeks before the first day of teaching for 1000-level and 5000-level units, or one week before the first day of teaching for all other units.
There are no unit outlines available online for previous years.
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