U.C. Data Science Course Ideation

1. Social & Ethical Issues

1.1. Ethical issue 1: We need to think about how data are collected. Data collected by passive means (e.g., through mobile phones, GPS, website 'hits' via large corporations providing data) can sometimes be done without informed consent of the people whose data is being used. Who owns the data and what right do individuals have to provide consent for their data to be used.

1.2. Maher, N.A., Senders, J.T., Hulsbergen, A.F., Lamba, N., Parker, M., Onnela, J.P., Bredenoord, A.L., Smith, T.R. and Broekman, M.L., 2019. Passive data collection and use in healthcare: A systematic review of ethical issues. International journal of medical informatics, 129, pp.242-247.

1.3. Ethical issue 2: We need to think about issues of 'disclosure' in big, digital data use. Confidentiality of information is an important ethical issue. 'Disclosure' is more than just identity of a research subject, but extends to: 1.Identity disclosure (i.e., where an individual subject is identifable throuh data); 2. Attribute disclosure (i.e., when information has been revealed about that person that was previously unknown); 3. Inferential disclosure (i.e., where information is inferred about an individual based on analytic results); and 4. Population disclosure (i.e., where information is disclosed about a population or group rather than an individual).

1.4. Wiltshire, D. and Alvanides, S., 2022. Ensuring the ethical use of big data: lessons from secure data access. Heliyon, 8(2).

1.5. Social opportunity: The advent of 'big data' offers researchers an amazing opportunity to generate answers to questions with extremely large data samples (allowing for greater 'power' in the analysis of data). However, ethical guidelines are vital to ensure digital data is used responsibly.

1.6. Clark, K., Duckham, M., Guillemin, M., Hunter, A., McVernon, J., O’Keefe, C., Pitkin, C., Prawer, S., Sinnott, R.O., Warr, D. and Waycott, J., 2015. Guidelines for the ethical use of digital data in human research.

2. Data Sources

2.1. Use Jobs and Skills Australia for career data - https://www.jobsandskills.gov.au/

2.2. Use Australian Bureau of Statistics for income data - https://www.abs.gov.au/statistics

3. Analysis Tools

3.1. AVERAGE function in Excel to calculate mean scores

3.2. SUM function in Excel to add data

3.3. IF function in Excel for converting string date to neumerical data

3.4. COUNT IF function in Excel to add converted string data

3.5. CONVERT text to table function in Excel for data cleaning

4. Visualisation Tools

4.1. Graphs in Excel

4.2. Tables in Excel

4.3. Headings in Powerpoint

4.4. Text in Powerpoint

5. Story Elements

5.1. Structure narrative flow based on pre-generated and self-genetrated questions

5.2. Main message 1: If you want a career in data science you need to know about labout market information

5.3. Main message 2: Be aware of salary information for data science jobs

5.4. Main message 3: Be aware of where data science jobs are available in Australia

5.5. Main message 4: Be aware of companies advertising data science jobs

5.6. Main message 5: Be aware of the fields in which data science jobs are available

6. Inspirations

6.1. Microsoft Data Storytelling - https://powerbi.microsoft.com/en-us/data-storytelling/

6.1.1. Use data storytelling to convey key messages in a way that lay people can understand

6.2. Storytelling with Data - https://www.storytellingwithdata.com/blog/category/Excel+Downloads

6.2.1. Keep the visual design elements uncluttered and clean

6.3. Excel: Data Storytelling for Beginners - https://careerhub.ufl.edu/classes/excel-data-storytelling-for-beginners/

6.3.1. Use visuals, such as graphs and tables to provide support for your key messages