When you come to this craft -- or sullen art -- without a background except as a user, how do you get started writing code?
It's not easy. Indeed, developing software may be one the hardest things there is. Really, really hard.
Why? Consider the orders of magnitude involved. From sub-microsecond clock speeds to software that's supposed to continue running for 8,763 hours a year without interruption. That's 31,547,269 seconds. Isn't that about 15 orders of magnitude?
Or consider scope of storage. We wrangle over bytes in a dataset that spans terabytes. That's 12 orders of magnitude.
When engineers build a 13,000' long bridge, are they looking at it from scales of 10±5? Do they even care what's 21 miles away? They might care about things at the scale of 10-5, since that's about an inch. But 10-7? 100th of an inch? I could be wrong, but I have doubts.
I won't go so far as to say bridge building is particularly easy. It's safety critical work. People die when things go wrong. Consequently, it's regulated by civil engineering standards. Bridge designs are limited to proven patterns. You can't spring something new on the world and expect anyone to pay money for it or trust their life to it.
If you're with me so far, you see my point: software is different. And that makes it particularly hard. People do learn elements of it. How does this happen?
Two Paths DivergeI see two separate paths:
- More formal, and
- Less formal.
The more formal path includes the kind of curriculum you find at big CS schools. Formal treatment of algorithms and data structures. Logic and Computable Functions. The essentials of Turing Completeness.
The less formal path starts with -- essentially -- random hacking around, trying to get stuff to work. Some folks argue that a curriculum of structured exercises isn't "random" hacking around. I suggest that a curriculum of structured exercises can be the formal path concealed under a patina of hackeriness. On the other hand, a set of exercises can be successful at training programmers; if it doesn't follow a formalized structure, it's merely a small step from random.
[Random doesn't mean "bad;" it means "informal" and "unstructured."]
Some folks learn well in a formal, structured approach. They like axiomatic definitions of computability, and they can get a grip on how to map the abstractions of computing to specific languages and problem domains. They read content at http://www.algorist.com and see applications of principles.
Other folks can be shown the formal background that makes their random hacking fit into a larger pattern. When shown how some things fit a larger pattern, they're often happy work in a new context with an expanded repertoire of data structures and algorithms. They read content at http://www.algorist.com and look for solutions to problems; the formal patterns will emerge eventually.
Not all folks respond well to having their informal notions challenged. Some folks have ingrained bad habits and prefer to fight to the death to avoid change. A sad state of affairs, but remarkably common. They didn't understand linked lists at some point and steadfastly refuse to use the java.util.LinkedList class. This is what software religious wars are about. Some trolls truly and deeply love an uniformed religious war.
Chickens and Eggs
Is this a chicken-and-egg problem?
- You can't really appreciate the formal foundations until you have some hands-on coding experience.
- You shouldn't dirty your hands with implementation details until you have the proper theoretical foundations.
That seems potentially reductionist and uninformative. Or. Perhaps there is a nugget of truth in this. Perhaps one is actually foundational.
Eggs, to be specific, show the fresh mutations. The egg comes first from a chicken-like precursor that's not properly a chicken.
What's that precursor to programming in Python? CS Fundamentals? Hacking around? I suggest that the way we acquire languages is important here.
Software languages are a small step from natural languages. As with learning natural languages, formal grammar may not be as helpful as engaging in conversations. Indeed, for natural languages, formal grammars are an afterthought. They're something we discover about a corpus. We impose the discovered grammar rules on ourselves (and others) to be understood in a context of other writing (and speaking.)
Natural language grammar isn't timeless and immutable. People throw their hands up in despair at the erosion of grammar and language. They're -- of course -- just being reactionary. Language evolves. The loudest complainers are the ones who didn't pay attention for a long time and suddenly (somehow) realized the don't know what "WTF" means. LOL.
With an artificial language, the grammar is formalized. It has a first-class existence in compilers, interpreters and other tools.
However, I think the bits of our brain that assimilate grammar work best from concrete examples. A formal grammar definition -- while helpful -- isn't the way to start. I think that a less formal, "try this" suite of exercises is perhaps the best way to learn to program.
As an author, I'm beholden to my publisher's notions of what sells. Examples sell. See almost everything from Packt. Working examples are solid gold.
These are not necessarily problems for the reader to tackle and solve. They're examples to study.
The conundrum with attempting to solve problems is the attempting part. It's hard to set out a list of "solve these problems and master programming" problems and hope folks get through them. What if they fail? Clearly, you'd provide answers. In that case, you'd be back at examples to study. Hmm.
I have intermittent interest in my older Building Skills in Python book. Partly because it's got extensive exercises in each chapter. I get donations. I get inquiries. The exercises seem to resonate in a small way.
I've done about 22 levels of the Python Challenge (I'll write about that separately.) It's not a great way to learn from scratch. You need to know a lot. And you need a lot of hints.
I've done almost 70 levels of Project Euler. It might be a better way to learn programming because the easy problems are really easy. No guesswork. No riddles. No steganography. The answers are totally cut-and-dried, unambiguous, and absolute. However, there's no easy guidance for learners. Either you have an answer, and want help on improving it, or ... well ... you're stuck and frustrated.
Structured Sequence of Exercises
What strikes me as a possibility here is a structured series of exercises that lay out the foundations of computer science as realized in a specific programming language.
Puzzle-style. With extensive hints. Background readings, too. But with absolutely right answers. And a score-keeping system to show where you stand.
No tricky riddles. No quizzes to proceed. You could go on to advanced material without mastering the foundations, if you wanted.
I've got a bunch of exercises and examples in my Building Skills books. Plus some of the examples in my Packt books can be modified and repurposed. Plus. Projects like HamCalc contain a wealth of simple applications that can be adjusted to show CS fundamentals.
Perhaps relevant is this: https://www.google.com/edu/programs/exploring-computational-thinking/. I'm not sure precisely how it fits, since it seems to be more aimed at providing a general background, rather than teaching programming language skills. They decompose the skills into four specific techniques. Here are specific techniques.
- Decomposition: Breaking a task or problem into steps or parts.
- Pattern Recognition: Make predictions and models to test.
- Pattern Generalization and Abstraction: Discover the laws, or principles that cause these patterns.
- Algorithm Design: Develop the instructions to solve similar problems and repeat the process.
Perhaps this is relevant: http://interactivepython.org/courselib/static/pythonds/index.html. I haven't read this carefully, but it seems to be expository rather than exploratory. It's really thorough. It has quizzes and self-checks.
I think there's a big space for publishing lots simple recreational programming exercises as teaching tools.