Posted on Fri 06 October 2017 in TDDA • Tagged with tdda, constraints, verification, bad data
We have a new White Paper available:
Abstract
Correctness is a key problem at every stage of data science projects: completing an entire analysis without a serious error at some stage is surprisingly hard. Even errors that reverse or completely invalidate the analysis can be …
Continue readingPosted on Thu 21 September 2017 in TDDA • Tagged with tdda, constraints, discovery, verification, suggestion, cartoon, bad data
Bad data is widespread and pervasive.1
Only datasets and analytical processes that have been subject to rigorous and sustained quality assurance processes are typically capable of achieving low or zero error rates. "Badness" can take many forms and have various aspects, including incorrect values, missing values, duplicated entries, misencoded …
Continue readingPosted on Thu 14 September 2017 in TDDA • Tagged with tdda, python
This post is a standing post that we plan to try to keep up to date, describing options for obtaining the open-source Python TDDA library that we maintain.
Assuming you have a working pip setup, you should be able to install the tdda library by typing …
Continue readingLast night I went to The Protectors of Data Scotland Meetup on the subject of Marketing and GDPR. If you're not familiar with Europe's fast-approaching General Data Protection Regulation, and you keep or process any personal data about humans,1, you probably ought to learn about it. A good place …
Continue readingA quick-reference guide ("cheat sheet") is now available for the Python TDDA library. This is linked in the sidebar and available here.
We will try to keep it up-to-date as the library evolves.
See you all at PyData London 2017 this weekend (5-6 May 2017), where we'll be running a …
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