Wow - this is a very active community and I just wanted to thank everyone for their replies, as it have provided me with a lot of tips and resources to getting up to speed with R and Python.
Thanks again all and will endeavour to keep you all posted with how I go. Cheers, Jason. P.S. The suggestion on attending a R and/or Python software carpentry is quite a valid one, but it difficult for me to travel to where these workshops are normally run and hence why I would like to provide some local courses myself. From: Discuss [mailto:[email protected]] On Behalf Of Jason Bell Sent: Wednesday, 30 March 2016 4:10 PM To: [email protected] Subject: [Discuss] Any tips for learning R and Python? 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<http://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. [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]<mailto:[email protected]> [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
