Editing Designing Internet Research (Spring 2022) (section)

== Some Reflections on Technical Skills ==

This course will focus on teaching conceptual skills related to Internet research. These skills involve the "softer" skills of understanding, designing, and critiquing research plans. These are harder to teach, evaluate, and learn than more "hard" technical skills like programming, web scraping, and so on. But they are ultimately what will make a research project interesting, useful, or valid.

That said, I also believe that any skilled Internet researcher must be comfortable writing code to collect a dataset from the web or, at the very least, should have enough experience doing so that they know what is involved and what is possible and impossible. This is essential even if your only goals is to manage somebody else writing code and gathering data or work productively with a collaborator who is doing so. 

Because students are going to come to the class with different technical skillsets, I will not be devoting time in this class to developing technical skills. That said, I strongly believe that a well rounded Internet researcher will have these skills as well. ''Although being successful in this class will '''not''' also require technical skills, being a successful Internet researcher will.''

For example, I think most Internet researchers should have at least:

# '''Basic skills in a general purpose high-level programming language used for Internet-based data collection and analysis.''' I strongly recommend the Python programming language although other programming languages like Ruby and Perl are also good choices. Generally speaking, statistical programming languages like R, Stata, Matlab are not well suited for this. However, if you happen to known a statistical programming language, learning a language like Python will be much easier!
# '''Familiarity with the technologies of web APIs.''' In particular, students should understand what APIs are, how they work, and should be able to read, interpret, and process data in JSON.
# '''Knowledge of how to process and move data from a website or API into a format that they will be able to use for analysis.''' The final format will depend on the nature of the result but this might be a statistical programming environment like R, Stata, SAS, SPSS, etc or a qualitative data analysis tools like ATLAS.ti, NVivo, Dedoose, Taguette, or similar.

If you are already comfortable doing these things, great. If you are not, I'd love to work with you to help you make a plan for building these skills. To be clear: It's not part of the class and it's not part of how you will be evaluated. But it's something that I want to help you all to have.

Here are some options for building these technical skills:

* I can help point you to find some online resources like MOOCs, online tutorials, and so on that are useful for building these skills. The details will probably vary based on what you know already.
* I have plans to teach a class (likely in the Spring quarter of 2023) that will be a sort of companion class to this one and which will introduce Python and the skills above. I'd love to have any of you join me!
* I also regularly have organized free workshops called the [[Community Data Science Workshops]] that teach exactly these skills. Although I've historically tried to time these workshops with this class, the ongoing pandemic has meant that it isn't in the cards this quarter. I do hope to teach them again at some point in the near future, though and happy to put you all on the announcement list.