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Unit of study_

DATA2901: Big Data and Data Diversity (Advanced)

2025 unit information

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'. This unit is an alternative to DATA2001, providing coverage of some additional, more sophisticated topics, suited for students with high academic achievement.

Unit details and rules

Managing faculty or University school:

Engineering

Study level Undergraduate
Academic unit Computer Science
Credit points 6
Prerequisites:
? 
75% or above from (DATA1002 or DATA1902 or INFO1110 or INFO1910 or INFO1903 or INFO1103 or ENGG1810)
Corequisites:
? 
None
Prohibitions:
? 
DATA2001
Assumed knowledge:
? 
None

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 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
  • LO8. know and work with several sophisticated topics related to data scale and diversity.

Unit availability

This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.

The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.

Session MoA ?  Location Outline ? 
Semester 1 2024
Normal day Camperdown/Darlington, Sydney
Session MoA ?  Location Outline ? 
Semester 1 2025
Normal day Camperdown/Darlington, Sydney
Outline unavailable
Session MoA ?  Location Outline ? 
Semester 1 2020
Normal day Camperdown/Darlington, Sydney
Semester 1 2021
Normal day Remote
Semester 1 2022
Normal day Camperdown/Darlington, Sydney
Semester 1 2022
Normal day Remote
Semester 1 2023
Normal day Camperdown/Darlington, Sydney
Semester 1 2023
Normal day Remote

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Modes of attendance (MoA)

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