Editing Internet Research Methods (Spring 2016) (section)

== Technical Skills ==

Nearly all of our structured in-person meetings and all of our readings 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 but are ultimately what will make a research project interesting, useful, or valid. When the course has been taught in the past by other faculty, it has been entirely focused on these types of conceptual skills.

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. As a result, being successful in this class will also require technical skills.

Because students are going to come to the class with different technical skillsets, we well be devoting a relatively small chunk of class time to developing technical skills. Instead, I'm requiring that students build these skills outside of our meetings together if they do not have them already.

In particular, I want every student to have the following three things:

# '''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.
# '''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 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 or Dedoose.

If you are already comfortable doing these things, great.

If you are not yet comfortable, I am going to be organizing three free workshops called the [[Community Data Science Workshops]] on Saturdays in April and May and I extremely strongly recommend that you attend them. The workshops will teach exactly the skills I'm expecting you to have and attending the full series of workshops will be enough to fulfill this requirement.

The workshops will meet four times so please block these out on your calendar now:

# Friday 4/8 6-9pm
# Saturday 4/9 9:45am-4pm
# Saturday 4/23 9:45am-4pm
# Saturday 5/7 9:45am-4pm

I've offered this workshops four times previously and they have always been oversubscribed. As a result, '''you should register for these workshops immediately.''' You can find the registration link [[CDSW Spring 2016|on this page]]. Please mention that you are in this class when you register so that we make sure that you accept your application.

I have taught these workshops four times before in 2014 and 2015. If you have taken them in the past, you do not need to take them again. If you took them before but are feeling unsure about your skills, you will be welcome to come back to review and brush up on the material.

If you do not have the technical skills required above and you will not attend the workshops, you're going to be responsible for learning this material on your own. Although this is totally fine, I suspect it present a major challenge to success in this class. If you will be in this situation, contact me before the quarter starts.