ETC5512: Wild Caught Data
Data can be obtained from many sources. It may be generated via experiments, collected from observational studies or surveys, obtained via sampling, or recorded using sensors.
Each type of data has its own characteristics that affect the analysis tools we use. Very large data sets come with their own challenges and require some database skills.
This unit will equip you with the tools to understand and use different sources of data. Open data sources will be emphasised.
Learning objectives
Understand the definitions, allowed usage, digital identification and licensing of open data
Know about common open data sources, how they are used and effectively search for new sources
Explain the differences between data collection methods and the limitations for data analysis
Work with the range of different data formats of open data, including APIs
Understand ethical constraints and privacy limits when working with open data
Recognise the components of effective curation needed for open data.
Teachers
- Kate Saunders - Lecturer and Chief Examiner
- Krisanat Anukarnsakulchularp - Tutor
- Maliny Po - Tutor
Weekly schedule
- Seminar: Monday 2 pm - 4 pm
- Drop in & practice session: Monday 10 - 11 pm (zoom)
- Refer to your timetable for your tutorial time
- Refer to Moodle for room locations and zoom links
Consults
- Kate: Monday 11 - 12 pm. (Room 64, Level 5, Building H, Caulfield Campus or see Moodle for zoom link)
- Kris: Tuesday 2:00 - 3:00 pm (Room 232A Building 6, 29 Ancora Imparo Way Clayton Campus)
- Maliny: Wednesday 3:30 - 4:30 pm
(Room 232A Building 6, 29 Ancora Imparo Way Clayton Campus) - New session Alternates Kris and Maliny: Thursday 3:30 - 4:30 pm (Room 232A Building 6, 29 Ancora Imparo Way Clayton Campus)
Schedule
| Week | Date | Topic | Tutorial | Solution |
|---|---|---|---|---|
| 1 | 03 Mar | Open data: definitions, sources and examples | ||
| 2 | 10 Mar | Introduction to data collection methods | ||
| 3 | 17 Mar | Case Study: US airline traffic | ||
| 4 | 24 Mar | Case Study: Australian census | ||
| 5 | 31 Mar | Case Study: Australian election data | ||
| 6 | 07 Apr | Case Study: Combining census and election data | ||
| 7 | 14 Apr | Case Study: PD model and credit risk | ||
| 21 Apr | Mid-semester break | |||
| 8 | 28 Apr | Case Study: Data ethics | ||
| 9 | 05 May | Case Study: Data ethics and privacy | ||
| 10 | 12 May | Case Study: Introduction to webscraping | ||
| 11 | 19 May | Case Study: Large Language Models for preparing data in R | ||
| 12 | 26 May | Revision: Proper care and feeding of wild caught data |