Description
ETHICS
Determine the implications in the collection, mining and recombination of open- and digital data.
As we use online and specialist data source for analysis, risks of de-anonymization.
Examples: Netflix de-anonymization, NY Taxis, Genome recovery, fitness trackers.
CURATION
Employ methods for presenting data for synthesis and usage, and employing methods for data maintenance.
Knowledge management systems, APIs, data standards and approaches to archival.
Methods for moving, tracking, cleaning, ETF, history and ownership.
ANALYSIS
Perform techniques in randomness and probability to understand distribution and likelihood.
Randomness, probability, generating datasets, tree diagrams, sampling techniques.
From software random numbers, to people (ie. before we get to people) … (samples with / without replacement), law of large numbers / averages.
PRESENTATION
Apply histograms, line charts and scatter plots to illustrate probability.
Using charts from previous lessons.
CASE STUDY
False positives / negatives from a universal breast cancer screening program, including cost and individual anxiety. Also consider risk from universal datasets of this nature.