Data Dogs meets the first Monday of every month in the event hall of the Hawthorne Lucky Labrador (https://www.google.com/maps/place/Lucky+Labrador+Brew+Pubemail@example.com,-122.6563595,15z/data=%214m5%213m4%211s0x0:0xdb6152a73ae2c980%218m2%213d45.5124695%214d-122.6563595) from 6pm – 8pm.
The Goal: Data Dogs focuses on the human side of data as it has become more ubiquitous both inside organizations and within our personal lives. We seek to expand this conversation beyond the tribe of data professionals and welcome anyone who is tracking or making data driven decisions.
Our discussions will be targeted around four distinct areas of the data cycle.
Cycle 1: Data Origins – Sources, generation and ownership
C2: Data Growth – Assembly, storage and science
C3: Data Maturity – Appeal, wisdom and power
C4: Data Destruction – Burial, deletion and ceremony
While prior attendance is not required to participate, please watch or read a link of interest below:
Cycle 1 – Data Origin (links listed in order of importance)
1. The Point of Collection
2. The Illusion of Agency (https://petervan.wordpress.com/2016/09/17/the-illusion-of-agency/)
3. Origin problems at Google (http://gking.harvard.edu/files/gking/files/0314policyforumff.pdf)
4. Stories VS Statistics (https://www.youtube.com/watch?v=XVMYTplQ158)
5. Random or Systematic Error? (http://www.socialresearchmethods.net/kb/measerr.php)
Cycle 1.5 - Measurement (This thing between the origin of the data and the empirical record of the phenomena.)
1. Software Engineering Metrics (http://testingeducation.org/a/metrics2004.pdf)
2. Level of Measurement Theory (https://en.wikipedia.org/wiki/Level_of_measurement)
Cycle 2 - Data Growth
1. Tidy Data (http://vita.had.co.nz/papers/tidy-data.pdf) (skim and understand the nomenclature)
2. Missing Data (http://www.stat.columbia.edu/~gelman/arm/missing.pdf)
2. Algorithms and Ethics (https://medium.com/pandemonio/algorithms-and-ethics-with-susan-etlinger-f9ae7941f083#.rza6zdqlu)
3. Data Wrangling (https://www.youtube.com/watch?v=4MfUCX_KpdE) (Data structures explained with Legos)
4. Defining Reproducibility and Replicability
Cycle 3 - Data Maturity
1. How to Convince Someone When Fact Fail (https://www.scientificamerican.com/article/how-to-convince-someone-when-facts-fail/)
2. The Power of Big Data and Psychographics (https://www.youtube.com/watch?v=n8Dd5aVXLCc)
3. Causal Impact (http://dataskeptic.com/blog/episodes/2016/causal-impact) (Appealing to apriori)
We look forward to your participation in the discussion and as a member of this community we expect you to be Open, Considerate, and Respectful.