I'm going to give you my completely biased librarian point of view here, but I also hold open data helpdesk hours and facilitate a weekly Python user's group. So I have fielded a lot of questions about R and Python. Some come in knowing the names and "just want to get started" while others have a particular project (or sometimes just a buzzword) in mind. Pay attention to how people ask the questions because it can tell you a lot of what they're after.
Not all resources have the same value for everyone. There is no one true resource. As they say, you need two points to make a line. You need at least two of three pieces of information to direct people to the right area for resources. a) What is your background? b) What are you trying to do with this? c) where do you want to go with this? Imagine these people: a) humanities, b) text mining, c) on this pile of PDFs a) social science, b) IDK but free, c) twitter behavior around a hashtag a) bioinformatics, b) R, c) epidemic modeling You may want to stress Python for the text mining and R for the survey, but remind each person that each language has advantages and places in their workflows. Background is also important to recognize, because not everyone wants to work through a book made up entirely of physics or survey research examples. Having something that 'speaks our own language' is incredibly valuable. Ask yourself this: would you give a book on survey analysis to a teenager who wants to learn how to code games? As far as identifying potential resources to recommend ask around to people you know who have been able to take it up and make it work for their research. Pick their brains. Then check to see if your library has copies of any books mentioned so you can refer people to either a physical copy or an ebook. Consider making up a formal list of what you know works well to prep people for your tools and keeping that handy for consultations. For your question about how much do you need to know before you can teach, I believe it's less than you'd think. I do a lot of work in R and still don't feel as cozy within it as I do in Python, so I don't like to bill myself as an R tutor. But many of the questions I get in for R are pretty much covered in the basic SWC lessons. Understanding how to properly read and work through examples or even visually parse a Stack Overflow answer is what a researcher needs. Being able to answer every single question is great, but don't let that hold you back from providing instruction that is needed. One of the best ways to learn more about a subject is to start helping people with it. tl;dr: ask people what worked, make a list, formalize that list for consultations, start a mini-library, try to pry research questions out of people who bring you buzzwords, and if you know enough to do anything in something you know more than someone who knows nothing. Go teach things! Elizabeth On Wed, Mar 30, 2016 at 1:09 AM, Jason Bell <[email protected]> wrote: > G’day Software Carpentry Instructors > > > > This being my first post to this list, as I recently become a software > carpentry instructor (as of last week) and I hope this is the appropriate > channel to ask a few questions in regards to learning, and then teaching, R > and python to my local research colleagues. > > > > I am in the unusual position of providing eResearch support to all of the > researchers at my University – distributed throughout 20 campuses. I look > after a number of systems, including our dedicated research storage > infrastructure (https://my.cqu.edu.au/web/eresearch/data-tools) and also > our High Performance Computing facility (www.cqu.edu.au/hpc), amongst > many things. Recently I have been getting a number of researchers who have > been approaching me requesting help in getting their research data > completed more quickly. I have been surprised how many different research > domains are now using R, in which the need for scientific computing skill > is starting to explode. As an example, I have assisted researchers to run > their code on our HPC System, in which the results would have taken them > months to complete on their local machine, to having a full set of data > results in just a few hours by running many programs on our HPC system at > once. > > > > One of the reasons why I am keen to learn and teach R and python, is so I > can help even more of my colleagues to produce their research data more > effectively and efficiently. Unfortunately at my local institution their > isn’t any local training that my colleagues can attend – this I hope > software carpentry can help to fill this large gap in scientific computing > training. > > > > Over the years I have learnt many programming languages (I have been quite > interested in reading some of the recent emails to this list about > programming languages), which stated with “BASIC” at high school, to Pascal > as the first language I learnt at University, to C/C++, ADA, Java, Visual > Basic, Lego robotics programming, Perl, Bash scripts, Matlab, PHP and HTML > (did someone mention TeX), using middleware libraries such MPI, P4 and even > did some python training quite a few years ago and contributed to the open > source software project “Access Grid” Software. I believe I have an > acceptable understanding of programming principles in general and therefore > would like to ask the following questions > > > > · What is the best (the quickest) way to get up to speed in R > (and python a little further down the track). As you can appreciate my > time is extremely limited (like most of us these days) and thus am chasing > the most efficient method for learning R and python, so I can begin > providing lessons in the very near future. > > · Do you think “instructors” should know more than just the > teaching material for the “subjects” they plan on teaching. For example, I > recently ran a local “UNIX Shell” locally and given I have been using bash > for over 15 years, I was extremely comfortable with the teaching material > (even though I did pick up a few tips and tricks), there were no unexpected > questions that I could not answer. I doubt this would be the case with R > or python, as I don’t use it regularly enough to feel competent to answer > left field questions. Now, I appreciate that you cannot know everything, > but having a greater knowledge than just the 3-4 hour lesson material would > like highly desirable – thus would welcome any suggestions in resources, > training material that could help me to get up to speed ASAP. > > > > · I see there are a few “R” lessons within software and data > carpentry, so I wonder if there are any recommended lessons that are > designed as an overview and not so much research domain specific? > > > > · I am also be interested in some visualisation aspects of R as > well, as a lot of my users are still trying to use “excel” to graph data. > > > > o I have taught myself how to pass command line arguments in R, as this > allows you to write a script to submit hundreds or thousands of separate > jobs to solve on a HPC system. Is this sort of thing covered anywhere? > > > > Some other “general” questions in regards to what our research colleagues > should be learning > > > > · Is there still a place for researchers to learn programming > languages such as C/C++ - from a program “execution” speed, C is pretty > hard to compete again, especially when looking to HPC types of programs. > > · A colleague has suggested that the “go” programming language ( > https://golang.org/) is becoming quite popular these days, is anyone else > seeing this? > > Anyway – I hope all of these questions are acceptable to ask here and > would appreciate any advice and comments you might have. > > > > Many thanks for your time, > > Jason. > > > > [image: cid:[email protected]] <https://www.cqu.edu.au/> > > *Jason Bell* > > Senior Research Technologies Officer | Information and Technology > Directorate > > CQUniversity eResearch Analyst | Queensland Cyber Infrastructure > Foundation (QCIF) > CQUniversity Australia, Building 19 Room 1.07, Bruce Highway, Rockhampton > QLD 4702 > *P* +61 7 4930 9229 *| X* 59229 *| M* 0409 630 897 |* E *[email protected] > > [image: cid:[email protected]] > <https://www.cqu.edu.au/social-media> > > This communication may contain privileged or confidential information. If > you have received this in error, > > please return to sender and delete. CRICOS: 00219C | RTO Code 40939 > > > > > > > > _______________________________________________ > Discuss mailing list > [email protected] > > http://lists.software-carpentry.org/mailman/listinfo/discuss_lists.software-carpentry.org >
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