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I can't say I'm weak in programming but I can't come up with the logic faster. I can analyse others code and get to know the logic. But I can't do it on my own. How can I improve my programming skills?
Quite a broad question but from my own, 30yr experience I can tell you there is no way around starting to
analyze existing code,
modify some things (play with it until it feels like it's your own code)
see what the changes do
develop your own ideas on how to do things faster/better/more beautiful
implement your ideas
see if it works
go on to more complex tasks
read books (very important, because many things can't just be discovered by trial'n'error)
be very passionate and determined about what you want to become reality
if you want to learn faster, then write more code
One very important item. You should have fun with what you do is always the best guarantee for success
If you fail at these items then I'm afraid you will never succeed with programming. But then maybe it's like any other field of knowledge.
I experience the same difficulties during my learning journey, too. When I complete challenge tasks I create more complex tasks for myself to see what I can do. It takes me to the next levels of solving problems.
Practice, Practice, Practice!
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I'm a computer engineering student but i've never programmed in my life (i've only studied physics, chemistry, control systems etc). Since I know a lot more about math than programming languages, and I'm studying logic right now on my own (i read it can be useful for artificial intelligence), i was thinking about learning Prolog as a first programming language. I tried to find some information about it on the internet but i couldn't really find much, all i discovered is that it's not really useful for landing a job but it can give you a different "mindset". Do you think it would be worth to learn it, or it would just be better learning something like C, Python etc?
Thank you!
Prolog is indeed a wonderful language, and it makes you think is a very differnt mode from other languages. As for making it a first, I think that's a bold move, and I suspect it'll make learning more languages a bit of a challenge. If your intent is to learn sofware engineering, I'm very sure you'll eventually learn another one.
I'd start with Python but, since you're curious already, learn it on the side.
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Lets say that my definition of 'rudimentary programming' refers to the fundamental tools employed for a computer to perform a task.
Considering programming rudiments, the learning spectrum usually looks something like this:
Variables, data types and variable memory
Arrays/Lists and their manipulation
Looping and conditionals
Functions
Classes
Multi threading/processing
Streams (hard-disk and web)
My question is, have I missed any of the major rudiments? Is there a 'next' to the spectrum that still eludes me?
I think you missed the most important one: algorithms. Understanding the complexity, know the situation to use them, why use them and more important, how to implement them.
I'm pretty sure that you already know a lot about algorithms but if you think that your tool-knowledge (aka the programming languages) are good enough, you should start focus, more, on the algorithms.
A great book to start is: Introduction to Algorithms, from Thomas H. Cormen
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As the title stated, what are the pros and cons of Ruby meta-programming?
So far I know the advantages of Ruby meta-programming give us quite flexible ways to write our code using code that writes code. But what are the disadvantages? Maintainability issues?
I feel that meta-programming helps to scale an app also.
Can anyone can give more details about this?
Almost everything I know about metaprogramming in Ruby comes from "Metaprogramming Ruby: Program Like the Ruby Pros". It's a great read, and I suggest it to everyone who want to understand metaprogramming.
I'd say the main advantage is to have incredibly flexible code, which can adapt swiftly to any changes one could imagine. Things like send, respond_to? or define_method really give you the possibility of writing beautiful code.
On the other hand, I'd say that the main disadvantage is to make code harder to read and harder to debug, since most of the times the code isn't there yet! It will be generated at runtime, so you can't actually read it when you debug it.
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I have applied to couple of startups and most of them are asking to solve programming challenge before they start on the interviewing candidate.
I have submitted couple of the solution and all the time getting rejected in the initial screening.
Now what i think is, they will see my coding style, algorithm and OOD concepts that i have used to solve the problem. Can you guys input more on it as what other details are taken into consideration and how can i improve my coding for getting selected.
By the way, i did all my coding in either Java/Perl.
Nice question, I am a new grad too... One thing I notice: When you do the exercise home, they expect you to use the best algorithm out there. In my opinion code modularity, even on a small function is key. Put lot effort into the code because they are not just judging you, but comparing you against other candidate. The one which seems to have put the more effort wins.
ps: Ask this question on programmers.stackexchange, you will obtain some good inputs there.
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I came across the declaration in a software best practices guide that algorithm and code shouldn't get mixed up. I'm not sure what is meant by this? As far as I understand, code is the implementation of the algorithm, isn't is? So, what exactly is meant by this statement? and why it is considered as a good practice?
Thank You!
The context in which the author mentioned would be clearer if you had pasted the surrounding lines.
Though what it would mean to me is, an algorithm is just a clear step-by-step logic that you would use to implement. You would leave out the finer implementation details like selection of the right data structure and other implementation details while you write/design the algorithm.
A good explanation can be found here
An algorithm is a series of steps for solving a problem, completing a task or performing a calculation. Algorithms are usually executed by computer programs but the term can also apply to steps in domains such as mathematics for human problem solving.
Code is a series of steps that machines can execute. In many cases, code is composed in a high level language that is then automatically translated into instructions that machines understand.