DMA 803S: Data Literacy and Mining 3 CREDITS
This course is primarily premised on the fact that the structure of a dataset, broad patterns and trends and other ancillary characteristics provide an indication of the appropriate analytical tools that will be suitable. Data literacy and mining is incremental gaining prominence due to the realised need to replicate scientific findings.
Students should be able to:
- Gain insight into the challenges and limitations of different data mining techniques
- Apply data literacy and mining solutions using common data mining software tool (e.g. WEKA, SPSS, Data Miner, etc)
- Prepare data for different types of analyses
Among the issues to be considered are interpreting data at different stages of the analytical process, data warehousing, handling outliers and missing values and engaging with differences that emerge between initial descriptive outcomes and inferential analysis.
- • Corti, L., Van den Eynden, V., Bishop, L., & Woollard, M. (2014). Managing and Sharing Research Data: A Guide to Good Practice. Sage.
Suggested Reading List
- Ng, A. & Soo, K. (2017). Numsense! Data Science for the Layman. Kindle
- Prodromou, T. (2017). Data Visualization and Statistical Literacy for Open and Big Data (Advances in Data Mining and Database Management). IGI Global