Useful links
Unit of study_
DATA2901: Big Data and Data Diversity (Advanced)
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.
| Code | DATA2901 |
|---|---|
| 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 |
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 outlines
Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.
- Key dates
Key dates through the academic year, including teaching periods, census, payment deadlines and exams.
- Student administration
Enrolment, course planning, fees, graduation, support services, student IT
- Expectations of student conduct
Code of Conduct for Students, Conditions of Enrollment, University Privacy Statement, Academic Integrity
- Academic appeals
Academic appeals process, special consideration, rules and guidelines, advice and support
- Learning and teaching policy
Policy register, policy search
- Financial support
Scholarships, interest free loans, bursaries, money management
- Study resources
Learning Centre, faculty and school programs, Library, online resources
- Health and support
Student Centre, counselling & psychological services, University Health Service, general health and wellbeing
