This archives some of the old material (you could also still find in git but want it here for convenience for now).
Also moving stuff to data/archive/
Note on the term 'pattern'
The term pattern has developed a very specific meaning in software engineering. While we use the term in this sense, the tricks presented are not defined as a pattern using any of the formal templates that have developed for software design patterns.
Target audience
This site is for anyone interested in working with data. It assumes you have a basic working knowledge of UNIX shell commands, the Python programming language, networking, SQL and common file formats like HTML, CSV or XML. If you are not familiar with these technologies, start by working through a tutorial on Python, such as Zed Shaw's Learn Python the Hard Way and then pick up the others as you need them.
We hope that some of the content will be especially useful to the many emerging types of data users, such as data journalists, civic hackers, coding wonks etc.
While most techniques apply to any kind of machine-readable information, some of the material may refer to a specific class of data that we care a lot about: open government data. Government information is a good example of data both for the interesting things that we can learn from it, but also because virtually any imaginable data problem applies to i
t: incompleteness, corruption, strange formats or sheer size.
This archives some of the old material (you could also still find in git but want it here for convenience for now).
Also moving stuff to
data/archive/Note on the term 'pattern'
The term pattern has developed a very specific meaning in software engineering. While we use the term in this sense, the tricks presented are not defined as a pattern using any of the formal templates that have developed for software design patterns.
Target audience
This site is for anyone interested in working with data. It assumes you have a basic working knowledge of UNIX shell commands, the Python programming language, networking, SQL and common file formats like HTML, CSV or XML. If you are not familiar with these technologies, start by working through a tutorial on Python, such as Zed Shaw's
Learn Python the Hard Wayand then pick up the others as you need them.We hope that some of the content will be especially useful to the many emerging types of data users, such as data journalists, civic hackers, coding wonks etc.
While most techniques apply to any kind of machine-readable information, some of the material may refer to a specific class of data that we care a lot about: open government data. Government information is a good example of data both for the interesting things that we can learn from it, but also because virtually any imaginable data problem applies to i t: incompleteness, corruption, strange formats or sheer size.