The iconic software company that is based in King County Washington has been getting almost universally slammed from it's recent Los Angeles press announcement about its entry into the hardware business with the convertible laptop/tablet known as Surface.

Certainly I can see the point that it is now competing with its hardware vendors/partners. Intel has done a good job in the arena creating 'reference designs' without competing with its partners.

There is another viewpoint which seems to be ignored. The cold facts are Microsoft is a public company. This puts Microsoft in a legal position of doing the most it can to return value to its shareholders. Failure to do so means somebody is going to jail.

Microsoft has a vision, which at the end of the day is, a certain way to get enough people to see enough value to hand over their money, to fulfill their fiduciary duty.

Google is one of the most popular websites in the entire world that gets millions of views each day. Therefore, it should come as no surprise that it needs a strong and reliable programming language that it can rely on to run its searches and many of the apps that Google has created. Because of this, Google uses Python to ensure that every time a user uses one of their products, it will work smoothly and flawlessly. That being said, Google uses Python in a variety of different ways, outlined below.

Code.Google.Com
Since its creation, Google has always used Python as part of its core for programming language. This can still be seen today considering the strong relationship the two have with one another. Google supports and sponsors various Python events, and Python works to better itself so that Google remains on top of cutting edge material. One way that they do this is by working with code.google.com. This is the place where Google developers go to code, learn to code and test programs. And with it being built on Python, users can experience exactly what it is that they should expect once they start using the real site.

Google AdWords
Google AdWords is a great way for people to get their websites out there, through the use of advertising. Each time a person types in a certain string of keywords, or if they have history in their cookies, then they’ll come across these AdWords. The way that these AdWords are broadcasted to online web surfers is built on the foundation from Python. Python also helps clients access their AdWord accounts, so that they can tailor where they want their advertisements to go.

Beets
If you have loads of music, but some of it is uncategorized or sitting in a music player without a name or title, Beets is for you. This Google project uses Python and a music database to help arrange and organize music. The best part about Beets is that even if it doesn’t run exactly the way that you want, you can use a bit of Python knowledge to tailor it to be more specific to your desires.

Android-Scripting
Not only does Google run off Python, but Android also has its own value for the language. Whether you are someone who is just creating your own app for your phone or if you are someone who is looking to create the next app that gets downloaded multiple millions of times, you can use Python and Android-Scripting to create an app that does exactly what you want it to do.

YouTube
YouTube one just started as a video viewer on its own, but is now a billion-dollar company that is owned by Google. YouTube uses Python to let users view and upload video, share links, embed video and much more. Much like Google itself, YouTube relies heavily on Python to run seamlessly for the amount of traffic it gets daily.

Python is not your average coding language. Instead, it is a valuable and integral part of some of the biggest websites in the world, one of which is Google. And the resources listed here are just a fraction of what Google uses Python for in total.

Related:

What Are The 10 Most Famous Software Programs Written in Python?

The Future of Java and Python

Ranking Programming Languages: Which are Gaining Popularity?

Top 10 Software Skills for 2014 and Beyond

Working With Strings In Python

Working With Lists In Python

Conditional Programming In Python

Before we go to the list do you know what makes software skills most sought-after and hence more valuable than most of the other job skills? It is simply because unlike any other skill, software skills are global and are not at all location specific! With the evolution and penetration of internet technologies, the physical distance between the client and service provider hardly matters. So, with more advancement in technology, it is indeed going to rain opportunities on the right skilled developer. I’ll take the liberty to reproduce the following quotes here to prove my claims statistically:

Demand for “cloud-ready” IT workers will grow by 26 percent annually through 2015, with as many as 7 million cloud-related jobs available worldwide.

---IDC White Paper (November 2012).

In the United States, the IT sector is experiencing modest growth of IT jobs in general, with the average growth in IT employment between 1.1 and 2.7 percent per year through 2020.

---U.S. Bureau of Labor Statistics.

The original article was posted by Michael Veksler on Quora

A very well known fact is that code is written once, but it is read many times. This means that a good developer, in any language, writes understandable code. Writing understandable code is not always easy, and takes practice. The difficult part, is that you read what you have just written and it makes perfect sense to you, but a year later you curse the idiot who wrote that code, without realizing it was you.

The best way to learn how to write readable code, is to collaborate with others. Other people will spot badly written code, faster than the author. There are plenty of open source projects, which you can start working on and learn from more experienced programmers.

Readability is a tricky thing, and involves several aspects:

1. Never surprise the reader of your code, even if it will be you a year from now. For example, don’t call a function max() when sometimes it returns the minimum().
2. Be consistent, and use the same conventions throughout your code. Not only the same naming conventions, and the same indentation, but also the same semantics. If, for example, most of your functions return a negative value for failure and a positive for success, then avoid writing functions that return false on failure.
3. Write short functions, so that they fit your screen. I hate strict rules, since there are always exceptions, but from my experience you can almost always write functions short enough to fit your screen. Throughout my carrier I had only a few cases when writing short function was either impossible, or resulted in much worse code.
4. Use descriptive names, unless this is one of those standard names, such as i or it in a loop. Don’t make the name too long, on one hand, but don’t make it cryptic on the other.
5. Define function names by what they do, not by what they are used for or how they are implemented. If you name functions by what they do, then code will be much more readable, and much more reusable.
6. Avoid global state as much as you can. Global variables, and sometimes attributes in an object, are difficult to reason about. It is difficult to understand why such global state changes, when it does, and requires a lot of debugging.
7. As Donald Knuth wrote in one of his papers: “Early optimization is the root of all evil”. Meaning, write for readability first, optimize later.
8. The opposite of the previous rule: if you have an alternative which has similar readability, but lower complexity, use it. Also, if you have a polynomial alternative to your exponential algorithm (when N > 10), you should use that.

Use standard library whenever it makes your code shorter; don’t implement everything yourself. External libraries are more problematic, and are both good and bad. With external libraries, such as boost, you can save a lot of work. You should really learn boost, with the added benefit that the c++ standard gets more and more form boost. The negative with boost is that it changes over time, and code that works today may break tomorrow. Also, if you try to combine a third-party library, which uses a specific version of boost, it may break with your current version of boost. This does not happen often, but it may.

Don’t blindly use C++ standard library without understanding what it does - learn it. You look at std::vector::push_back() documentation at it tells you that its complexity is O(1), amortized. What does that mean? How does it work? What are benefits and what are the costs? Same with std::map, and with std::unordered_map. Knowing the difference between these two maps, you’d know when to use each one of them.

Never call new or delete directly, use std::make_unique and [cost c++]std::make_shared[/code] instead. Try to implement usique_ptr, shared_ptr, weak_ptr yourself, in order to understand what they actually do. People do dumb things with these types, since they don’t understand what these pointers are.

Every time you look at a new class or function, in boost or in std, ask yourself “why is it done this way and not another?”. It will help you understand trade-offs in software development, and will help you use the right tool for your job. Don’t be afraid to peek into the source of boost and the std, and try to understand how it works. It will not be easy, at first, but you will learn a lot.

Know what complexity is, and how to calculate it. Avoid exponential and cubic complexity, unless you know your N is very low, and will always stay low.

Learn data-structures and algorithms, and know them. Many people think that it is simply a wasted time, since all data-structures are implemented in standard libraries, but this is not as simple as that. By understanding data-structures, you’d find it easier to pick the right library. Also, believe it or now, after 25 years since I learned data-structures, I still use this knowledge. Half a year ago I had to implemented a hash table, since I needed fast serialization capability which the available libraries did not provide. Now I am writing some sort of interval-btree, since using std::map, for the same purpose, turned up to be very very slow, and the performance bottleneck of my code.

Notice that you can’t just find interval-btree on Wikipedia, or stack-overflow. The closest thing you can find is Interval tree, but it has some performance drawbacks. So how can you implement an interval-btree, unless you know what a btree is and what an interval-tree is? I strongly suggest, again, that you learn and remember data-structures.

These are the most important things, which will make you a better programmer. The other things will follow.