SPARKMEET | Data Literacy: An Essential 21st Century Skill
Meetup: Data Literacy: An Essential 21st Century Skill
7th July 2022
Dr. Kimsy Tor is an Assistant Professor of Information and Communications Technology and Mathematics at the American University of Phnom Penh. Dr. Kimsy received her Ph.D. in Computational Statistics from Telecom Paris, France, before joining AUPP. She has a Master's degree in Statistics from Paris Sorbonne University. Dr. Kimsy received her bachelor's degree in Mathematics and a minor in Computer Science from Manhattan College in the United States. Mathematical Teaching, Data Science, and Machine Learning are among her interests (American University of Phnom Penh, 2019).
Data is something that we come across every day, but not all data can be used as some may contain errors and problems. Here is some advice Dr. Kimsy Tor told us to consider when collecting data.
1. Beware of Bias
Bias can quickly happen if you are not careful enough. Biased data can be found in many ways, including organizations reporting on themselves, data that is produced by interest groups, and data that is self-reported where there may be room for exaggeration.
2. Citing Your Data
Certainly, you are not going to rely solely on your own data. Some data visualization may be obtained from external sources. Ms. Kimsy stated that it is essential you give credit to its rightful owners after using such data for your work. You can do so by putting citations in your work with your organization’s documentation style. By doing this, you would be safe from plagiarism, and you also help readers quickly find the original source.
3. Data Privacy and Ethics
Even though data can be collected in many ways, you should be aware that some can be unethical and interfere participant’s privacy. There are three questions that Dr. Kimsy Tor suggested to ensure that the way you use data does not negatively impact anyone.
· What data is collected?
· How is the data stored?
· Who can access the data?
In the meetup, Dr. Kimsy Tor also talked about:
· Data journey and cycle
· The use of data in different fields
· How to become more data proficient.