Software Carpentry: lessons learned

Since its start in 1998, Software Carpentry has evolved from a week-long training course at the US national laboratories into a worldwide volunteer effort to improve researchers' computing skills. This paper explains what we have learned along the way, the challenges we now face, and our plans for the future.


Introduction
In January 2012, John Cook posted this to his widelyread blog [1]: In a review of linear programming solvers from 1987 to 2002, Bob Bixby says that solvers benefited as much from algorithm improvements as from Moore's law: "Three orders of magnitude in machine speed and three orders of magnitude in algorithmic speed add up to six orders of magnitude in solving power.A model that might have taken a year to solve 10 years ago can now solve in less than 30 seconds." A million-fold speedup is impressive, but hardware and algorithms are only two sides of the iron triangle of programming.The third is programming itself, and while improvements to languages, tools, and practices have undoubtedly made software developers more productive since 1987, the speedup is percentages rather than orders of magnitude.Setting aside the minority who do high-performance computing (HPC), the time it takes the "desktop majority" of scientists to produce a new computational result is increasingly dominated by how long it takes to write, test, debug, install, and maintain software.The problem is, most scientists are never taught how to do this.While their undergraduate programs may include a generic introduction to programming or a statistics or numerical methods course (in which they're often expected to pick up programming on their own), they are almost never told that version control exists, and rarely if ever shown how to design a maintainable program in a systematic way, or how to turn the last twenty commands they typed into a re-usable script.As a result, they routinely spend hours doing things that could be done in minutes, or don't do things at all because they don't know where to start [2,3].This is where Software Carpentry comes in.We ran 91 workshops for over 4300 scientists in 2013.In them, more than 100 volunteer instructors helped attendees learn about program design, task automation, version control, testing, and other unglamorous but time-tested skills [4].Two independent assessments in 2012 showed that attendees are actually learning and applying at least some of what we taught; quoting [5]: The program increases participants' computational understanding, as measured by more than a two-fold (130%) improvement in test scores after the workshop.The program also enhances their habits and routines, and leads them to adopt tools and techniques that are considered standard practice in the software industry.As a result, participants express extremely high levels of satisfaction with their involvement in Software Carpentry (85% learned what they hoped to learn; 95% would recommend the workshop to others).Despite these generally positive results, many researchers still find it hard to apply what we teach to their own work, and several of our experiments-most notably our attempts to teach online-have been failures.

From Red to Green
Some historical context will help explain where and why we have succeeded and failed.

Version 1: Red Light
In 1995-96, the author organized a series of articles in IEEE Computational Science & Engineering titled, "What Should Computer Scientists Teach to Physical Scientists and Engineers?"[6].The articles grew out of the frustration he had working with scientists who wanted to run before they could walk-i.e., to parallelize complex programs that weren't broken down into self-contained functions, that didn't have any automated tests, and that weren't under version control [7].In response, John Reynders (then director of the Advanced Computing Laboratory at Los Alamos National Laboratory) invited the author and Brent Gorda (now at Intel) to teach a week-long course on these topics to LANL staff.The course ran for the first time in July 1998, and was repeated nine times over the next four years.It eventually wound down as the principals moved on to other projects, but two valuable lessons were learned: 1. Intensive week-long courses are easy to schedule (particularly if instructors are travelling) but by the last two days, attendees' brains are full and learning drops off significantly.
2. Textbook software engineering is not the right thing to teach most scientists.In particular, careful documentation of requirements and lots of up-front design aren't appropriate for people who (almost by definition) don't yet know what they're trying to do.Agile development methods, which rose to prominence during this period, are a less bad fit to researchers' needs, but even they are not well suited to the "solo grad student" model of working so common in science.

Versions 2 and 3: Another Red Light
The Software Carpentry course materials were updated and released in 2004-05 under a Creative Commons license thanks to support from the Python Software Foundation [8].They were used twice in a conventional termlong graduate course at the University of Toronto aimed at a mix of students from Computer Science and the physical and life sciences.The materials attracted 1000-2000 unique visitors a month, with occasional spikes correlated to courses and mentions in other sites.But while grad students (and the occasional faculty member) found the course at Toronto useful, it never found an institutional home.Most Computer Science faculty believe this basic material is too easy to deserve a graduate credit (even though a significant minority of their students, particularly those coming from non-CS backgrounds, have no more experience of practical software development than the average physicist).However, other departments believe that courses like this ought to be offered by Computer Science, in the same way that Mathematics and Statistics departments routinely offer service courses.In the absence of an institutional mechanism to offer credit courses at some interdepartmental level, this course, like many other interdisciplinary courses, fell between two stools.

It Works Too Well to be Interesting
We have also found that what we teach simply isn't interesting to most computer scientists.

It Adds Up
Saying, "We'll just add a little computing to every other course," is a cheat: five minutes per hour equals four entire courses in a four-year program, which is unlikely to ever be implemented.Pushing computing down to the high school level is also a non-starter, since that curriculum is also full.
The sweet spot for this kind of training is therefore the first two or three years of graduate school.At that point, students have time (at least, more time than they'll have once they're faculty) and real problems of their own that they want to solve.

Version 4: Orange Light
The author rebooted Software Carpentry in May 2010 with support from Indiana University, Michigan State University, Microsoft, MITACS, Queen Mary University of London, Scimatic, SciNet, SHARCNet, and the UK Met Office.More than 120 short video lessons were recorded during the subsequent 12 months, and six more weeklong classes were run for the backers.We also offered an online class three times (a MOOC avant la lettre).This was our most successful version to date, in part because the scientific landscape itself had changed.Open access publishing, crowd sourcing, and dozens of other innovations had convinced scientists that knowing how to program was now as important to doing science as knowing how to do statistics.Despite this, though, most still regarded it as a tax they had to pay in order to get their science done.Those of us who teach programming may find it interesting in its own right, but as one course participant said, "If I wanted to be a programmer instead of a chemist, I would have chosen computer science as my major instead of chemistry."Despite this round's overall success, there were several disappointments: 1. Once again, we discovered that five eight-hour days are more wearying than enlightening.
2. And once again, only a handful of other people contributed material, not least because creating videos is significantly more challenging than creating slides.
Editing or modifying them is harder still: while a typo in a slide can be fixed by opening PowerPoint, making the change, saving, and re-exporting the PDF, inserting new slides into a video and updating the soundtrack seems to take at least half an hour regardless of how small the change is.
3. Most importantly, the MOOC format didn't work: only 5-10% of those who started with us finished, and the majority were people who already knew most of the material.Both figures are in line with completion rates and learner demographics for other MOOCs [9], but are no less disappointing because of that.
The biggest take-away from this round was the need come up with a scalable, sustainable model.One instructor simply can't reach enough people, and cobbling together funding from half a dozen different sources every twelve to eighteen months is a high-risk approach.

Figure 2. Cumulative Enrolment was two-day intensive workshops like those pioneered by
The Hacker Within, a grassroots group of grad students helping grad students at the University of Wisconsin -Madison.Shortening the workshops made it possible for more people to attend, and increased the proportion of material they retained.It also forced us to think much harder about what skills scientists really needed.Out went object-oriented programming, XML, Make, GUI construction, design patterns, and software development lifecycles.Instead, we focused on a handful of tools (discussed in the next section) that let us introduce higher-level concepts without learners really noticing.
Reaching more people also allowed us to recruit more instructors from workshop participants, which was essential for scaling.Switching to a "host site covers costs" model was equally important: we still need funding for the coordinator positions (the author and two part-time administrative assistants at Mozilla, and part of one staff member's time at the Software Sustainability Institute in the UK), but our other costs now take care of themselves.Our two-day workshops have been an unqualified success.Both the number of workshops, and the number of people attending, have grown steadily: More importantly, feedback from participants is strongly positive.While there are continuing problems with software setup and the speed of instruction (discussed below), 80-90% of attendees typically report that they were glad they attended and would recommend the workshops to colleagues.

What We Do
So what does a typical workshop look like?
• Day 1 a.m.: The Unix shell.We only show participants a dozen basic commands; the real aim is to introduce them to the idea of combining singlepurpose tools (via pipes and filters) to achieve desired effects, and to getting the computer to repeat things (via command completion, history, and loops) so that people don't have to.
• Day 1 p.m.: Programming in Python (or sometimes R).The real goal is to show them when, why, and how to grow programs step-by-step as a set of comprehensible, reusable, and testable functions.
• Day 2 a.m.: Version control.We begin by emphasizing how it's a better way to back up files than creating directories with names like "final", "really_final", "really_final_revised", and so on, then show them that it's also a better way to collaborate than FTP or Dropbox.
• Day 2 p.m.: Using databases and SQL.The real goal is to show them what structured data actually is-in particular, why atomic values and keys are important-so that they will understand why it's important to store information this way.
As the comments on the bullets above suggest, our real aim isn't to teach Python, Git, or any other specific tool: it's to teach computational competence.We can't do this in the abstract: people won't show up for a hand-waving talk, and even if they do, they won't understand.If we show them how to solve a specific problem with a specific tool, though, we can then lead into a larger discussion of how scientists ought to develop, use, and curate software.We also try to show people how the pieces fit together: how to write a Python script that fits into a Unix pipeline, how to automate unit tests, etc. Doing this gives us a chance to reinforce ideas, and also increases the odds of them being able to apply what they've learned once the workshop is over.Of course, there are a lot of local variations around the template outlined above.Some instructors still use the command-line Python interpreter, but a growing number have adopted the IPython Notebook, which has proven to be an excellent teaching and learning environment.
We have also now run several workshops using R instead of Python, and expect this number to grow.While some people feel that using R instead of Python is like using feet and pounds instead of the metric system, it is the lingua franca of statistical computing, particularly in the life sciences.A handful of workshops also cover tools such as LaTeX, or domain-specific topics such as audio file processing.We hope to do more of the latter going forward now that we have enough instructors to specialize.We aim for no more than 40 people per room at a workshop, so that every learner can receive personal attention when needed.Where possible, we now run two or more rooms side by side, and use a pre-assessment questionnaire as a sorting hat to stream learners by prior experience, which simplifies teaching and improves their experience.We do not to shuffle people from one room to another between the first and second day: with the best inter-instructor coordination in the world, it still results in duplication, missed topics, and jokes that make no sense.Our workshops were initially free, but we now often have a small registration fee (typically $20-40), primarily because it reduces the no-show rate from a third to roughly 5%.When we do this, we must be very careful not to trip over institutional rules about commercial use of their space: some universities will charge us hundreds or thousands of dollars per day for using their classrooms if any money changes hands at any point.We have also experimented with refundable deposits, but the administrative overheads were unsustainable.

Commercial Offerings
Our material is all covered by the Creative Commons -Attribution license, so anyone who wants to use it for corporate training can do so without explicit permission from us.We encourage this: it would be great if graduate students could help pay their bills by sharing what they know, in the way that many programmers earn part or all of their living from working on open source software.
What does require permission is use of our name and logo, both of which are trademarked.We're happy to give that permission if we've certified the instructor and have a chance to doublecheck the content, but we do want a chance to check: we have had instances of people calling something "Software Carpentry" when it had nothing to do with what we usually teach.We've worked hard to create material that actually helps scientists, and to build some name recognition around it, and we'd like to make sure our name continues to mean something.
As well as instructors, we rely local helpers to wander the room and answer questions during practicals.These helpers may be participants in previous workshops who are interested in becoming instructors, grad students who've picked up some or all of this on their own, or members of the local open source community; where possible, we aim to have at least one helper for every eight learners.
We find workshops go a lot better if people come in groups (e.g., 4-5 people from one lab) or have other pre-existing ties (e.g., the same disciplinary background).They are less inhibited about asking questions, and can support each other (morally and technically) when the time comes to put what they've learned into practice after the workshop is over.Group signups also yield much higher turnout from groups that are otherwise often under-represented, such as women and minority students, since they know in advance that they will be in a supportive environment.

Small Things Add Up
As in chess, success in teaching often comes from the accumulation of seemingly small advantages.Here are a few of the less significant things we do that we believe have contributed to our success.

Live Coding
We use live coding rather than slides: it's more convincing, it enables instructors to be more responsive to "what if?" questions, and it facilitates lateral knowledge transfer (i.e., people learn more than we realized we were teaching them by watching us work).This does put more of a burden on instructors than a pre-packaged slide deck, but most find it more fun.

Open Everything
Our grant proposals, mailing lists, feedback from workshops, and everything else that isn't personally sensitive is out in the open.While we can't prove it, we believe that the fact that people can see us actively succeeding, failing, and learning buys us some credibility and respect.

Open Lessons
This is an important special case of the previous point.Anyone who wants to use our lessons can take what we have, make changes, and offer those back by sending us a pull request on GitHub.As mentioned earlier, this workflow is still foreign to most educators, but it is allowing us to scale and adapt more quickly and more cheaply than the centralized approaches being taken by many highprofile online education ventures.

Use What We Teach
We also make a point of eating our own cooking, e.g., we use GitHub for our web site and to plan workshops.Again, this buys us credibility, and gives instructors a chance to do some hands-on practice with the things they're going to teach.The (considerable) downside is that it can be quite difficult for newcomers to contribute material; we are therefore working to streamline that process.

Meet the Learners on Their Own Ground
Learners tell us that it's important to them to leave the workshop with their own working environment set up.
We therefore continue to teach on all three major platforms (Linux, Mac OS X, and Windows), even though it would be simpler to require learners to use just one.
We have experimented with virtual machines on learners' computers to reduce installation problems, but those introduce problems of their own: older or smaller machines simply aren't fast enough.We have also tried using VMs in the cloud, but this makes us dependent on universityquality WiFi. . .

Collaborative Note-Taking
We often use Etherpad for collaborative note-taking and to share snippets of code and small data files with learners.(If nothing else, it saves us from having to ask students to copy long URLs from the presenter's screen to their computers.)It is almost always mentioned positively in post-workshop feedback, and several workshop participants have started using it in their own teaching.We are still trying to come up with an equally good way to share larger files dynamically as lessons progress.Version control does not work, both because our learners are new to it (and therefore likely to make mistakes that affect classmates) and because classroom WiFi frequently can't handle a flurry of multi-megabyte downloads.

Sticky Notes and Minute Cards
Giving each learner two sticky notes of different colors allows instructors to do quick true/false questions as they're teaching.It also allows real-time feedback during hands-on work: learners can put a green sticky on their laptop when they have something done, or a red sticky when they need help.We also use them as minute cards: before each break, learners take a minute to write one thing they've learned on the green sticky, and one thing they found confusing (or too fast or too slow) on the red sticky.It only takes a couple of minutes to collate these, and allows instructors to adjust to learners' interests and speed.

Pair Programming
Pairing is a good practice in real life, and an even better way to teach: partners can not only help each other out during the practical, but clarify each other's misconceptions when the solution is presented, and discuss common research interests during breaks.To facilitate it, we strongly prefer flat seating to banked (theater-style) seating; this also makes it easier for helpers to reach learners who need assistance.

Keep Experimenting
We are constantly trying out new ideas (though not always on purpose).Among our current experiments are:

Partner and Adapt
We have built a very fruitful partnership with the Software Sustainability Institute (SSI), who now manage our activities in the UK, and are adapting our general approach to meet particular local needs.

A Driver's License for HPC
As another example of this collaboration, we are developing a "driver's license" for researchers who wish to use the DiRAC HPC facility.During several rounds of beta testing, we have refined an hour-long exam to assess people's proficiency with the Unix shell, testing, Makefiles, and other skills.This exam was deployed in the fall of 2013, and we hope to be able to report on it by mid-2014.
New Channels On June 24-25, 2013, we ran our first workshop for women in science, engineering, and medicine.This event attracted 120 learners, 9 instructors, a dozen helpers, and direct sponsorship from several companies, universities, and non-profit organizations.Our second such workshop will run in March 2014, and we are exploring ways to reach other groups that are underrepresented in computing.
Smuggling It Into the Curriculum Many of our instructors also teach regular university courses, and several of them are now using part or all of our material as the first few lectures in them.We strongly encourage this, and would welcome a chance to work with anyone who wishes to explore this themselves.

Instructor Training
To help people teach, we now run an online training course for would-be instructors.It takes 2-4 hours/week of their time for 12-14 weeks (depending on scheduling interruptions), and introduces them to the basics of educational psychology, instructional design, and how these things apply to teaching programming.It's necessarily very shallow, but most participants report that they find the material interesting as well as useful.

Why do people volunteer as instructors?
To make the world a better place.The two things we need to get through the next hundred years are more science and more courage; by helping scientists do more in less time, we are helping with the former.
To make their own lives better.Our instructors are often asked by their colleagues to help with computing problems.The more those colleagues know, the more interesting those requests are.
To build a reputation.Showing up to run a workshop is a great way for people to introduce themselves to colleagues, and to make contact with potential collaborators.This is probably the most important reason from Software Carpentry's point of view, since it's what makes our model sustainable.
To practice teaching.This is also important to people contemplating academic careers.
To help diversify the pipeline.Computing is 12-15% female, and that figure has been dropping since the 1980s.While figures on female participation in computational science are hard to come by, a simple head count shows the same gender skew.Some of our instructors are involved in part because they want to help break that cycle by participating in activities like our workshop for women in science and engineering in Boston in June 2013.

To learn new things, or learn old things in more detail.
Working alongside an instructor with more experience is a great way to learn more about the tools, as well as about teaching.
It's fun.Our instructors get to work with smart people who actually want to be in the room, and don't have to mark anything afterward.It's a refreshing change from teaching undergraduate calculus. . .

TODO
We've learned a lot, and we're doing a much better job of reaching and teaching people than we did eighteen months ago, but there are still many things we need to improve.

Too Slow and Too Fast
The biggest challenge we face is the diversity of our learners' backgrounds and skill levels.
No matter what we teach, and how fast or how slow we go, 20% or more of the room will be lost, and there's a good chance that a different 20% will be bored.The obvious solution is to split people by level, but if we ask them how much they know about particular things, they regularly under-or over-estimate their knowledge.We have therefore developed a short preassessment questionnaire (listed in the appendix) that asks them whether they could accomplish specific tasks.While far from perfect, it seems to work well enough for our purposes.

Finances
Our second-biggest problem is financial sustainability.
The "host site covers costs" model allows us to offer more workshops, but does not cover the 2 full-time coordinating positions at the center of it all.We do ask host sites to donate toward these costs, but are still looking for a long-term solution.

Long-Term Assessment
Third, while we believe we're helping scientists, we have not yet done the long-term follow-up needed to prove this.This is partly because of a lack of resources, but it is also a genuinely hard problem: no one knows how to measure the productivity of programmers, or the productivity of scientists, and putting the two together doesn't make the unknowns cancel out.What we've done so far is collect verbal feedback at the end of every workshop (mostly by asking attendees what went well and what didn't) and administer surveys immediately before and afterwards.Neither has been done systematically, though, which limits the insight we can actually glean.We are taking steps to address that, but the larger question of what impact we're having on scientists' productivity still needs to be addressed.

Meeting Our Own Standards
One of the reasons we need to do long-term follow-up is to find out for our own benefit whether we're teaching the right things the right way.As just one example, some of us believe that Subversion is significantly easier for novices to understand than Git because there are fewer places data can reside and fewer steps in its normal workflow.Others believe just as strongly that there is no difference, or that Git is actually easier to learn.While learnability isn't the only concern-the large social network centered around GitHub is a factor as well-we would obviously be able to make better decisions if we had more quantitative data to base them on.

"Is It Supposed to Hurt This Much?"
Fourth, getting software installed is often harder than using it.This is a hard enough problem for experienced users, but almost by definition our audience is inexperienced, and our learners don't (yet) know about system paths, environment variables, the half-dozen places configuration files can lurk on a modern system, and so on.
Combine that with two version of Mac OS X, three of Windows, and two oddball Linux installations, and it's almost inevitable that every time we introduce a new tool, it won't work as expected (or at all) for at least one person in the room.Detailed documentation has not proven effective: some learners won't read it (despite repeated prompting), and no matter how detailed it is, it will be incomprehensible to some, and lacking for others.

Edit This
And while it may seem like a trivial thing, editing text is always harder than we expect.We don't want to encourage people to use naive editors like Notepad, and the two most popular legacy editors on Unix (Vi and Emacs) are both usability nightmares.We now recommend a collection of open and almost-open GUI editors, but it remains a stumbling block.

Teaching on the Web
Challenge #5 is to move more of our teaching and follow-up online.We have tried several approaches, from MOOC-style online-only offerings to webcast tutorials and one-to-one online office hours via VoIP and desktop sharing.In all cases, turnout has been mediocre at the start and dropped off rapidly.The fact that this is true of most high-profile MOOCs as well is little comfort. . .

What vs. How
Sixth on our list is the tension between teaching the "what" and the "how" of programming.When we teach a scripting language like Python, we have to spend time up front on syntax, which leaves us only limited time for the development practices that we really want to focus on, but which are hard to grasp in the abstract

Standardization vs. Customization
What we actually teach varies more widely than the content of most university courses with prescribed curricula.We think this is a strength-one of the reasons we recruit instructors from among scientists is so that they can customize content and delivery for local needs-but we need to be more systematic about varying on purpose rather than by accident.

Watching vs. Doing
Finally, we try to make our teaching as interactive as possible, but we still don't give learners hands-on exercises as frequently as we should.We also don't give them as diverse a range of exercises as we should, and those that we do give are often at the wrong level.This is partly due to a lack of time, but disorganization is also a factor.
There is also a constant tension between having students do realistic exercises from actual scientific workflows, and giving them tasks that are small and decoupled, so that failures are less likely and don't have knockon effects when they occur.This is exacerbated by the diversity of learners in the typical workshop, though we hope that will diminish as we organize and recruit along disciplinary lines instead of geographically.

Better Teaching Practices
Computing education researchers have learned a lot in the past two decades about why people find it hard to learn how to program, and how to teach them more effectively [10,11,12,13,14].We do our best to cover these ideas in our instructor training program, but are less good about actually applying them in our workshops.

Conclusions
To paraphrase William Gibson, the future is already here-it's just that the skills needed to implement it aren't evenly distributed.A small number of scientists can easily build an application that scours the web for recentlypublished data, launch a cloud computing node to compare it to home-grown data sets, and push the result to a GitHub account; others are still struggling to free their data from Excel and figure out which of the nine backup versions of their paper is the one they sent for publication.
The fact is, it's hard for scientists to do the cool things their colleagues are excited about without basic computing skills, and impossible for them to know what other new things are possible.Our ambition is to change that: not just to make scientists more productive today, but to allow them to be part of the changes that are transforming science in front of our eyes.If you would like to help, we'd like to hear from you.
• Richard "Tommy" Guy (Microsoft) • Edmund Hart (University of British Columbia) • Neil Chue Hong (Software Sustainability Institute) • Katy Huff (University of Wisconsin) • Michael Jackson (Edinburgh Parallel Computing Centre) • W. Trevor King (Drexel University) • Justin Kitzes (University of California, Berkeley) • Stephen McGough (University of Newcastle) • Lex Nederbragt (University of Oslo) • Tracy Teal (Michigan State University) • Ben Waugh (University College London) • Lynne J. Williams (Rotman Research Institute) • Ethan White (Utah State University) A Pre-Assessment Questionnaire • What best describes the complexity of your programming?(Choose all that apply.)-I have never programmed.
-I write scripts to analyze data.
-I write tools to use and that others can use.
-I am part of a team which develops software.
• A tab-delimited file has two columns showing the date and the highest temperature on that day.Write a program to produce a graph showing the average highest temperature for each month.
-Could not complete.
-Could complete with documentation or search engine help.-Could complete with little or no documentation or search engine help.
• How familiar are you with Git version control?
-Not familiar with Git.
-Only familiar with the name.
-Familiar with Git but have never used it.
-Familiar with Git because I have used or am using it.
• Consider this task: given the URL for a project on GitHub, check out a working copy of that project, add a file called notes.txt, and commit the change.
-Could not complete.
-Could complete with documentation or search engine help.-Could complete with little or no documentation or search engine help.
• How familiar are you with unit testing and code coverage?
-Not familiar with unit testing or code coverage.
-Only familiar with the terms.
-Familiar with unit testing or code coverage but have never used it.-Familiar with unit testing or code coverage because I have used or am using them.
• Consider this task: given a 200-line function to test, write half a dozen tests using a unit testing framework and use code coverage to check that they exercise every line of the function.
-Could not complete.
-Could complete with documentation or search engine help.-Could complete with little or no documentation or search engine help.
• How familiar are you with SQL?
-Not familiar with SQL.
-Only familiar with the name.
-Familiar with SQL but have never used it.
-Familiar with SQL because I have used or am using them.
• Consider this task: a database has two tables: Scientist and Lab.Scientist's columns are the scientist's user ID, name, and email address; Lab's columns are lab IDs, lab names, and scientist IDs.Write an SQL statement that outputs the number of scientists in each lab.
-Could not complete.
-Could complete with documentation or search engine help.-Could complete with little or no documentation or search engine help.
• How familiar do you think you are with the command line?
-Not familiar with the command line.
-Only familiar with the term.
-Familiar with the command line but have never used it.-Familiar with the command line because I have or am using it.
• How would you solve this problem: A directory contains 1000 text files.Create a list of all files that contain the word "Drosophila" and save the result to a file called results.txt.
-Could not create this list.
-Would create this list using "Find in Files" and "copy and paste".-Would create this list using basic command line programs.-Would create this list using a pipeline of command line programs.

Figure 1
Figure 1.Cumulative Number of Workshops . By comparison, version control and databases are straightforward: what you see is what you do is what you get.We also don't as good a job as we would like teaching testing.The mechanics of unit testing with an xUnit-style framework are straightforward, and it's easy to come up with representative test cases for things like reformatting data files, but what should we tell scientists about testing the numerical parts of their applications?Once we've covered floating-point roundoff and the need to use "almost equal" instead of "exactly equal", our learners quite reasonably ask, "What should I use as a tolerance for my computation?"for which nobody has a good answer.