To learn about conferences and other DH-related events, view or subscribe to the Digital Library Federation community calendar.
DH, sponsored by the Alliance of Digital Humanities Organizations, is the main international conference, but the Association for Computing and the Humanities (the North American DH organization) has also begun holding a bi-annual conference. You might also look at Digital Frontiers, HASTAC, and the Global DH Symposium.
I have benefited from a number of local meetups and training events, even if they’re not specific to digital humanities. Some organizations to keep your eye on:
- PyLadies of Los Angeles
- R-Ladies Los Angeles
- Data Science LA
- DataVis LA
- Women in Big Data
- Blacks in Tech-LA
- Black Women in Technology LA
CrashSpace is a “hackerspace” in West LA that holds a variety of events (everything from robotics to Dungeons & Dragons), often for free.
If you’re willing and able to travel for training, two DH institutes in North America have excellent reputations:
- Digital Humanities Summer Institute (Victoria, B.C.)
- Humanities Intensive Learning and Teaching (location varies)
Both institutes offer scholarships.
There are two main journals of digital humanities: DHQ and Digital Scholarship in the Humanities. However, a number of journals specialize in particular methods, such as Cultural Analytics (text analysis) and [in]Transition (videographic film criticism).
In general, these are my favorite sources of tutorials, although this tends to be very tool-specific:
Getting started with programming
Many DH-related tasks are actually easier if you can do some simple programming. A lot of people favor Python as a first language, and it’s particularly useful for manipulating documents and data. The best resource for starting out with Python (and, increasingly, other languages, too) is probably The Programming Historian, although I also like Automate the Boring Stuff with Python. Ruby is also a popular choice. People seem to like Learn Ruby the Hard Way, although I haven’t used it.
R is designed for statistical analysis and visualization, and there are several good resources for learning it. (I actually think R might be a little easier for beginners than Python, but this is Highly Controversial.)