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Posts Tagged ‘Google

Artificial Intelligence, Machine Learning are new buzzwords that are actively discussed in the tech world. Do you remember how our future was described in the movies some time ago: Terminator, Skynet, AI rules the world? General AI machines have remained in the movies and science fiction novels however narrow AI technologies are gradually evolving from the science fiction era to the reality and are already around us. Google uses Machine Learning to filter out spam messages from Gmail. Facebook trained computers to identify specific human faces nearly as accurately as humans do. Deep Learning is used by Netflix and Amazon to decide what you want to watch or buy next.

AI, machine learning, and deep learning are not quite the same thing but these terms are often used haphazardly and interchangeably, and that sometimes leads to some confusion. So let`s see what is the difference between each type of technology.
 
Artificial Intelligence (AI)

Artificial intelligence, which has been around since the 1950s, has seen ebbs and flows in popularity over the last 60+ years. But today, with the recent explosion of big data, high-powered parallel processing, and advanced neural algorithms, we are seeing a renaissance in AI—and companies from Amazon to Facebook to Google are scrambling to take the lead.

AI is the broadest way to think about advanced, computer intelligence. It can refer to anything from a computer program playing a game of chess, to a voice-recognition system like Amazon’s Alexa interpreting and responding to speech. The technology can broadly be categorized into three groups: Narrow AI (that is focused on one narrow task), artificial general intelligence or AGI (a machine with the ability to apply intelligence to any problem, rather than just one specific problem), and superintelligent AI (when its equal to humans or even surpasses them).

Pardoe believes that “we’ve just entered the “Fourth Industrial Revolution”, and while the adoption of AI has just started, the next few years will transform many sectors.
 
Machine learning

Machine learning is one subfield of AI. Or let`s say it`s the field of AI which today is showing the most promise at providing tools that industry and society can use to drive change. The core principle here is that machines take data and “learn” for themselves. Unlike hand-coding a software program with specific instructions to complete a task, ML allows a system to learn to recognize patterns on its own and make predictions.
 

 
Here are some of the popular machine learning methods:

-supervised learning: the “trainer” will present the computer with certain rules that connect an input (an object’s feature, like “smooth,” for example) with an output (the object itself, like a marble), and the algorithm learns by comparing its actual output with correct outputs to find errors. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.

-unsupervised learning: the computer is given inputs and is left alone to discover patterns. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns.

-reinforcement learning: the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker, for instance, the driverless car), the environment (everything the agent interacts with, for instance the road) and actions (what the agent can do).
 
Deep Learning

Deep learning is a brunch of Machine Learning, let`s see it as the cutting-edge of the cutting-edge. It uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making.

Deep Learning involves feeding a computer system with a lot of data, which it can use to make decisions about other data. This data is fed through neural networks. These networks are logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received.

Text-based searches, fraud detection, spam detection, handwriting recognition, image search, speech recognition, Street View detection, and translation are all tasks that can be performed through deep learning. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future.

The machine revolution has certainly started and the AI revolution is sure to pave the way for some significant changes in our lives. Machines will gradually improve, slowly replacing jobs that require repetitious behavior. But what happens when one day the machines become smarter than us?

 

Anna Kozik

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altabel

Altabel Group

Professional Software Development

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www.altabel.com

 


 
Go was created in 2007 at Google as an experimental project. It was designed to be a fun language and at the same time it is productive, practical, expressive and powerful. Google Go can be considered the result of a rather conservative language evolution from languages such as C and C++.

Node.js is an increasingly popular platform that is built on fast, JavaScript-based runtime: V8. V8 is a JS virtual machine created by Google that is designed to build scalable, networked applications. It compiles JavaScript code to native machine code, using some complex optimizations. V8 also does the memory allocation and garbage collection of JS objects.

Recently, there have been criticisms about the value of using Node.js in a high-performance, networked application environment, and some developers have moved to the Go language. Not only developers but also some well-known companies, such as Google, DropBox, Docker, DigitalOcean and more have picked up Go for some of their projects.

Certainly, Node.js is still used by a much wider audience, has more modules, is easier to use and isn’t going anywhere anytime soon. However, Go is gaining more and more popularity. In this article we will take a look at the differences between Node.js and Go to reveal the strengths and weaknesses of each environment.
 
Performance

Due to JS’s dynamically-typed nature Node cannot reach the raw performance of CPU or memory bound tasks that Go can achieve in many tests. Go is about 3 times faster and more lightweight than Node.js in a production environment. Not to say that Node.js is slow, it’s fast enough for most use cases, but when you do hit a limit, Go can still go a long way.
 
Concurrency

One of the language’s peculiarities is the presence of goroutines, functions that can be executed concurrently with one another.

These can be launched simply by using a keyword. Go runtime contains a scheduler that coordinates the execution of an arbitrary number of goroutines on an arbitrary number of system threads (the M:N model). In this way it is possible to carry out rapid context switches in order to take advantage of all CPU cores. So, in a hypothetical web application written in Go a single process will be able to continue serving requests even if one of these is trying to execute a blocked operation.

Node suffers from JavaScript’s less than elegant concurrency support using the event-callback mechanism. However, for a lot of applications working with JS promises and the coming async generator support (also called “semi-coroutines”) will suffice. Something like the Koa framework is already supporting the async generator approach in Node.
 
Ease of use

Node.js is a much simpler platform to use, especially if you are already a JS developer. For Go you will need to learn some new programming concepts, such as: coroutines, channels, strict typing (with compilation), interfaces, structs, pointers, and some other differences.
 
Ecosystem

Both platforms have pretty active and growing ecosystems, but as Node.js has been around much longer and certainly it has a broader community of users and more tools that make certain software projects a lot easier and/or cheaper to implement.

Go is a younger language, however, it dynamically develops: the number of standard Go packages is growing steadily, currently at over 100, and the Go community packages can be searched easily.

In conclusion, I’d like to say that there is no ideal language/framework/tool that could be used by everyone otherwise there would be only one programming language and there wouldn’t be such debates as Go vs Node.js:) Every language is tailored to be used for specific use cases. But we need to admit that there are some things Go performs better in, at the same time it lacks some characteristics that Node.js provides.

And what advantages and disadvantages of using Node.js and Go have you come across?

 

Anna Kozik

Business Development Manager

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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

 

“Computer programming is an art, because it applies accumulated knowledge to the world, because it requires skill and ingenuity, and especially because it produces objects of beauty.”
Donald Knuth, 1974

 

It’s better to start your journey into the career of programming by answering the question “Do you really need programming?” This question does not apply to those, who majored in computer programming or was close to it. If at school you were good at math, if you like to spend a lot of time sitting in front of the computer, if you want to learn something new, then programming is for you. What is more, this area is now in demand and highly paid in the world, job vacancies for the post of programmers are always open. Isn’t it the best time to be a programmer? 🙂

Everyone knows that the future programmer should be able to think broadly and to present the project from different perspectives before its implementation and realization. Unfortunately, the machine does not understand a human language. Of course, I’m not talking about Siri and other voice recognition — I’m talking about the creation of new software. To create the calculator, the computer needs to be given the task in the same way as the foreman explains to workers how to lay bricks. That’s why you can’t do anything without understanding the programming languages. Well, first you need to decide what kind of programming languages we should start with.

And here everyone chooses a language which will be useful for him. It depends on the kind of products you are going to develop. Most of us studied Turbo Pascal at school, and it’s no news that this language is practically not used anymore. So, if you want to join the team of programmers in the nearest future, the choice of language should be made sensibly.

Among the most popular programming languages in 2016 are Java, followed by C languages, then Python, JavaScript, PHP, Ruby, etc. It should come as no surprise that the more popular language is, the more chances you have to find work in the future. So, you’d better start with Java or C#, as these are the best paid and relatively simple learning languages of writing code. If you can’t cope with them, then you should try to learn Python. This language suits for quick and effective programming.

But if you have no programming experience at all you can start with something more simple for understanding. Good examples can be the basics of HTML and CSS.

Why? These two languages are essential for creating static web pages. HTML (Hypertext Markup Language) structures all the text, links, and other content you see on a website. CSS is the language that makes a web page look the way it does—color, layout, and other visuals we call style. Well, if you are interested in making websites, you should definitely start with HTML and CSS.

Let’s move to JavaScript. It is the first full programming language for many people. Why? It is the next logical step after learning HTML and CSS. JavaScript provides the behavior portion of a website. For example, when you see that a form field indicates an error, that’s probably JavaScript at work.

JavaScript has become increasingly popular, and it now lives outside web browsers as well. Learning JavaScript will put you in a good place as it becomes a more general-purpose language.

Some people also suggest choosing Python as the first programming language because Python’s program code is readable, first of all. You don’t even need to be a programmer to understand what is happening in the program. Due to the simple syntax of Python you will need less time for writing programs than in Java, for example. A huge base of libraries will save you a lot of strength, nerves and time. Large technology companies are working with Python: Yandex, Google, Facebook and YouTube. It is used for web applications, game development, software for servers.

Java can also be a good choice for a beginner. This language is more popular than Python, but a bit more complicated. At the same time, the development tools are much better designed. Java is one of the most popular languages for the backend development of modern enterprise web applications. It is used in Amazon, eBay, LinkedIn and Yahoo! With Java and the frameworks based on it, developers can create scaling web apps for a wide range of users. Java is also the primary language used for developing Android applications for smart phones and tablets. Moreover, after Java you will be able to work with low level programming languages.

PHP is one more popular language. The PHP language, along with databases (e.g. MySQL) is an important tool for creating modern web applications. Most of the sites developed on PHP are focused on a large amount of data. It is also a fundamental technology of powerful content management systems like WordPress. There are no normal imports in PHP, there are many solutions to one and the same problem. And it makes training more complicated.

 

 
The languages C and C# are a bit complicated for a beginner. But if you develop software for embedded systems, work with system kernels or just want to squeeze out every last drop from all available resources, C is what you need.

Ruby has begun to gain popularity since 2003, when the framework Rails appeared. Used widely among web startups and big companies alike, Ruby and Rails jobs are pretty easy to come by. Ruby and Rails make it easy to transform an idea into a working application, and they have been used to bring us Twitter, GitHub, and Treehouse.

Choosing a programming language may still seem challenging. It shouldn’t. You can’t go wrong. As long as you choose a language that is regularly used in technology today, you’re winning. When you are starting out, the goal is to become solid in the basics, and the basics are pretty similar across almost all modern programming languages.

Part of learning to code is learning a language’s syntax (its grammatical or structural rules). A much bigger part of learning to code, the part that takes longer and gives you more headaches, is learning to solve problems like a programmer. You can learn the grammatical structure of the English language pretty quickly; however, you won’t truly understand the language until you put that grammatical structure to use in a conversation. The same is true in programming. You want to learn the core concepts in order to solve problems. Doing this in one language is similar to doing it in another. Because the core concepts are similar from language to language, I recommend sticking with whichever language you choose until your understanding of the core concepts is solid. If you have a clear idea of your reasons for learning to program, and know exactly what you want to accomplish with your new coding skills, then you’ll be able to make the right choice.

How did you guys get into programming? What are the best programming languages for first-time learners?

Please, share with us your experience and opinion here below 🙂

 

Kate Kviatkovskaya

Kate Kviatkovskaya

Business Development Manager

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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

jsframework

Whether you’re building apps for the browser, mobile or desktop, Aurelia can enable you to not only create amazing UI, but do it in a way that is maintainable, testable and extensible.

Retrospective and today

Aurelia is a project of Rob Eisenberg, the author of a very popular MV * – framework for Caliburn.Micro XAML-platforms, Durandal. Understanding all the disadvantages of Durandal, Eisenberg engaged in the development of so-called NextGen framework. In 2014 he began to work in Angular team on the second version of the framework. However, several months later, Rob decided to leave the Angular team since the direction of Angular 2, in his opinion, had changed a lot. He gathered a large team and returned to work on the framework of his dreams. And Aurelia is the result of that work.

JavaScript of tomorrow?

By using modern tooling Aurelia was written from the ground up in ECMAScript 2016. This means you have native modules, classes, decorators and more at disposal.
Aurelia is written in modern and future JavaScript, it takes a nowadays approach to architecture. It’s built as a series of collaborating libraries, which form a powerful and robust framework for building Single Page Apps (SPAs). However, Aurelia’s libraries can often be used individually, in traditional web sites or even on the server-side through technologies like NodeJS.
Aurelia’s code is open sourced under the MIT License, a very permissive license used by many popular web projects today. The starter kits are available under the Creative Commons 0 license. There is also a Contributor for those who wish to join the team in working on Aurelia. Ultimately, this means that you can use Aurelia without fear of legal repercussions and it can be build in the same confidence.

Benefits of Aurelia

Clean and Unobtrusive – Aurelia is the only framework that lets you build components with plain JavaScript. It stays out of your way so your code remains clean and easy to evolve over time.

Convention over Configuration – Simple conventions help developers follow solid patterns and reduce the amount of code they have to write and maintain. It also means less fiddling with framework APIs and more focus on their app.

Simple, But Not Simplistic – Because of the simple design developers are able to learn a very small set of patterns and APIs that unlock limitless possibilities.

Promotes the “-ilities” – Testability, maintainability, extensibility, learnability, etc.- Aurelia’s design helps developers to naturally write code that exhibits these desirable characteristics.

Amazingly Extensible – Aurelia is highly modular and designed to be customized easily, so developers will never hit a roadblock or have to “hack” the framework to succeed.

Web Standards Focused – Focused on next generation JavaScript and Web Components, and avoiding unnecessary abstractions that obscure the underlying web, Aurelia is the cleanest and most standards-compliant framework today.

Integrates Well with Others – Easily integrated with any 3rd party library or framework: for instance, with jQuery, React, Polymer, Bootstrap, MaterializeCSS and much more.

TypeScript Support – Each Aurelia library is released with its own d.ts files. There are also official TypeScript beginner kits and production quality starter kits.

An Official Product with Commercial Support – Being an official product of Durandal Inc., it has commercial and enterprise support available, so you can use Aurelia for building core technology for your business.

Thriving Community and Ecosystem – Having one of the largest developer gitter channels in the JavaScript world, oodles of contributors and a huge core team, Aurelia has been used to build just about every type of application and is used by large, well-known multi-national companies and enterprises.
 
Aurelia, Angular and React.js – what’s common and what’s different?
 

Aurelia vs. Angular

Similarities between Aurelia and Angular 2:

  • Aurelia offers ES6-support out of the box and supports all forms of alternative abstraction syntax such as TypeScript and CoffeeScript. Migration documentation about migrating from Angular 1 and 2 have been put on the roadmap.
  • The basis of both Angular 2 and Aurelia application comprise components associated with the corresponding template.
  • Differences in vision details and options range:

  • The syntax is much simpler and more explicit (i.e. self-explanatory) than Angular 2 and looks a lot like standard JS syntax. ES6 and JSPM are used by default and a gulp file with a custom-built system to transpile ES6 to ES5 using the Babel transpiler is included in the standard package.
  • Aurelia also uses conventions instead of its own syntax and boilerplate code. No special characters like the ones in Angular 2 (*, (), [] en #) here.
  • Aurelia is built in a modular way making it very pluggable. You can plug in internationalization, routing, virtualization, animation, … Besides that, third party plugins are available as well such as the aurelia-flux plugin adding the Flux dispatcher to Aurelia.
  • The presence of a root-component is necessary; it represents an application (app). The metadata may / should be attached to components by using decorators. Component initialization is performed by using dependency injection. In addition, each component has a declared lifecycle, which can be built by using the lifecycle hooks. The components may be formulated into a hierarchical structure.
  • Communication between the component and the template is performed by using data binding. The process of template rendering to the final HTML can be integrated by using pipes (Angular) or value converters + binding behaviours (Aurelia).
  • The main advantage of Aurelia in comparison to Angular is an advanced composition mechanism and template parts. Aurelia is designed with an emphasis on unobtrusive, the number of framework structures in the final code is minimal. Aurelia is more compact, while Angular sometimes simply forces to produce copy-paste.
  • Aurelia is new to the market while Angular has a big user base because it’s already been around for 6 years. On the other hand, Aurelia has great documentation available, it’s an official product of Durandal Inc, and the company has a long term vision for the product, something the Angular team doesn’t seem to have and is blamed for a lot.

Aurelia vs. React.js

  • While on the surface it might not seem fair to compare Aurelia to React.js, they’re both being used for the same things. Despite the fact that React.js is a fully-fledged and functionally released product without the early preview alpha tag and Aurelia is not, at current stage they are both pretty at the same level. You can achieve the same tasks within both, just in different ways.
  • As for React components and Aurelia’s ViewModel’s, they are both quite similar in that you’re essentially using a class to define properties and methods bound to a particular view. The primary difference between them is React doesn’t separate the logic from the view, meaning in React the View and ViewModel are both within the same file.
  • However, that’s not to say that Aurelia doesn’t allow you to achieve the same thing by rendering the View from within the ViewModel as well and forgoing a traditional View.
  • The original intent behind React.js was not to be a competitor to the likes of Angular or Aurelia, but rather be the library that everyone uses with their SPA framework like Angular to improve performance.
  • Therefore, this means you can easily use React.js within Aurelia. Aurelia and React.js can be used together and in doing so, it provides you with a level of power other frameworks cannot without subsequent complexity and strict convention like EmberJS.

Aurelia vs. Angular and React

  • Two-way binding is provided out of the box and the framework does so very precisely. By default, 1-way databinding is used except for form controls, a clear plus when compared to React. Do keep in mind that two-way data binding can only be done through explicit syntax, as is the case in Angular 2.
  • The learning curve for Aurelia is comparable to that of Angular 2 and thus a lot steeper than React’s. Luckily, the extensive documentation makes up for that a great deal.
  • Angular 2 and Aurelia Architecture is very similar. Aurelia looks a lot like Angular 2 in the sense that it’s a complete framework that relies on the web standards. It’s as pluggable as React is and as Angular 2 will be.
  • While Angular was created by Google and React by Facebook, they don’t provide commercial or enterprise support, something that Aurelia will do.

 
Conclusion

It goes without saying why these three frameworks are so popular. They all have a lot of strong advantages. Eventually, I’m favoring Aurelia: there’s solid documentation available and the overall philosophy is the same with Angular 2, but Aurelia is a better choice from the syntax and execution point of view. The architecture and syntax vision of Aurelia team seems to be more clear than the vision of the Angular team. The company and enterprise support of Aurelia is also a big pro.
What is your personal experience with these frameworks? Which one would you choose for your projects and why? What’s your prediction “who” will win the crown in the nearest future? Please feel free to share your thoughts with us.

Thank you in advance!

 

Darya Bertosh

Darya Bertosh

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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

The Go Programming Language (Go) is an open-source programming language sponsored by Google and created by Robert Griesemer, Rob Pike, and Ken Thompson that makes it easy to build simple, reliable, and efficient software.

Go has gained popularity since it was first announced in 2009, and it’s now being used by many companies worldwide and for a variety of applications; Dropbox, Google, SoundCloud,  CloudFlare,  Docker and Cloud Foundry are some of the Go programming users.

go

Like any technology, though, it has its adherents and critics. Here are some key benefits and perceived drawbacks of the language as told by experts familiar with it.

Pros:

  • It is fast. And not only fast in the sense that programs written in it run fast when compared to other common languages; but also fast in the sense that its compiler can compile projects in the blink of an eye. You can even edit and run Go programs directly on the Web.
  • It is a garbage-collected language. This puts less pressure on the developer to do memory management, as the language itself takes care of most of the grunt work needed.
  • It has built-in concurrency, which allows parallelism in an easier way than is possible in other languages. Go has the concept of goroutines to start concurrent work and the concept of channels to permit both communication and synchronization.
  • Go has documentation as a standard feature. That makes it easier for developers to document their code and generate human-readable data out of source code comments.
  • Go has a rich standard library which covers a lot of areas. In fact, Go is probably the only language that can claim to have a fully working Web server as part of its standard library.
  • Go’s built-in build system is both elegant and simple. No need to mess with build configurations or makefiles.

Cons:

  • Go is still a very young language and has a very young ecosystem. This means there aren’t many libraries for it yet, leaving developers to write libraries themselves. There is also a shortage of books and online courses on the language.
  • Go is simple to the point of being superficial. Go’s simplicity is mostly superficial, and in its effort to find simplicity, it threw away decades of valuable programming language progress.
  • Although Go is a high-level language, it still has low-level features such as pointer-arithmetic which does not rule out the chance of doing systems and OS programming.
  • Go’s tooling is really weird, on the surface it has some really nice tools, but a lot of them, when you start using them, quickly show their limitations.
  • It is still not so easy to learn Go and it’s difficult to handle errors in it.

What is your attitude to Go? Is it worth learning? What do you think are Go’s advantages and disadvantages? Can you tell us about a real use you have given to this programming language? Please, feel free to share your thoughts here below.

 

Kate Kviatkovskaya

Kate Kviatkovskaya

Business Development Manager

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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

At one time, universities and colleges were institutes of higher learning for those who were passionate about acquiring knowledge. Today, education discussions tend to to center around how much individuals can make with their degree. Thanks to the Internet there are still places that offer open learning initiatives designed to help a new generation of technologists succeed.

Let’s start reviewing the free online courses to grow your tech skills.

1. Data visualization

Those who can take different types of data and visualize it in a way that helps provide clarity and insight are in demand. The big data machine is picking up steam. In fact, according to recent data from Dice, big data skills rank highly among those most in demand by employers.

This four-week free course offered by the University of Illinois through Coursera provides a foundation in elementary graphics programming, human perception and cognition, basic visualization, visualizing relationships and information. The course is offered from July 20th – August 15th, 2015 and requires roughly 4-6 hours per week of study time.

2. Programming for everybody (Python)

This beginner’s course is a gentle introduction into the world of Python programming. It’s meant to be a first programming course, and as a result the focus is more so on understanding the concepts. There are no prerequisites and no advanced math skills are required so if you’ve ever wanted to learn how to code or are just interested in the simple intuitive programming syntax that Python offers now is the time to take advantage of this opportunity to learn.This 10-week free course is offered from October 5th to December14th 2015. It requires 2-4 hours a week of study time. All textbooks and resources are free.

3. Begin programming: build your first mobile game

A crash course in programming, this offering from Future Learn aims to teach the beginnings of Java, taking students through the basics of programming by modifying a small Java game (code provided) that can run on your desktop or your Android devices.

Over the seven-week course has started on June 1 , 2015 students will be introduced to the basic constructs of Java that are similar to many programming languages. The course consists of video introductions, on-screen examples, Java game code, downloadable guides, articles and group discussions. It requires roughly four hours of study per week.

You could join the course in progress or register to a new one (date TBA).

4. Introduction to Linux

Have you always wanted to learn more about Linux but never had the extra cash to inveset. This course offered through edX might be just what you’re looking for. The Linux Foundation partnered with edX to offer this free Introduction to the Linux that covers familiarity with the graphical interface and command line as well as a look at all the major Linux distributions. The course gives an over view of the day to day working environment of a Linux administrator and covers pertinent tools and skills.

The designers of the course estimate that a total of 40- 60 hours of study are required to accurately cover the material. Students have the option of auditing the course, at no cost. You get access to course materials, tests, assignments and activities. Those who audit and complete the course will receive a certificate of achievement, but for those wanting to add it as a bullet point on their resume there is a verified certificate available for a fee.

5. Google Analytics Academy

With the proliferation of the Web, online shopping and social media marketing, competition for organic search traffic has gone through the roof. Understanding your website’s analytics can help you make better data-based decisions while at the same time improving the customer experience. Google knows this and would love for you to use its product, Google Analytics. So much so that they’ve created this online learning center that offers courses to help you better use the hidden data located within your site.

The courses highlighted here are all free and are good examples of how the digital revolution is bringing education to the masses around the globe.

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Romanas Lauks
Romanas.Lauks@altabel.com
Skype ID: romanas_lauks
Marketing Manager (LI page)
Altabel Group – Professional Software Development

The big languages are popular for a reason: They offer a huge foundation of open source code, libraries, and frameworks that make finishing the job easier. Sometimes the vast resources of the popular, mainstream programming languages aren’t enough to solve your particular problem. Sometimes you have to look beyond the obvious to find the right language, where the right structure makes the difference while offering that extra feature to help your code run significantly faster without endless tweaking and optimizing. This language produces vastly more stable and accurate code because it prevents you from programming sloppy or wrong code.

The world is filled with thousands of clever languages that aren’t C#, Java, or JavaScript. Some are treasured by only a few, but many have flourishing communities connected by a common love for the language’s facility in solving certain problems. There may not be tens of millions of programmers, who know the syntax, but sometimes there is value in doing things a little different, as experimenting with any new language can pay significant dividends on future projects.

The following six languages should be on every programmer’s radar. They may not be the best for every job — many are aimed at specialized tasks. But they all offer upsides that are worth investigating and investing in. There may be a day when one of these languages proves to be exactly what your project — or boss — needs.

Erlang: Functional programming for real-time systems

Erlang’s secret is the functional paradigm. Most of the code is forced to operate in its own little world where it can’t corrupt the rest of the system through side effects. The functions do all their work internally, running in little “processes” that act like sandboxes and only talk to each other through mail messages. You can’t merely grab a pointer and make a quick change to the state anywhere in the stack. You have to stay inside the call hierarchy. It may require a bit more thought, but mistakes are less likely to propagate.

The model also makes it simpler for runtime code to determine what can run at the same time. With concurrency so easy to detect, the runtime scheduler can take advantage of the very low overhead in setting up and ripping down a process. Erlang fans like to flourish about running 20 million “processes” at the same time on a Web server.

If you’re building a real-time system with no room for dropped data, such as a billing system for a mobile phone switch, then check out Erlang.

Go: Simple and dynamic

Google wasn’t the first organization to survey the collection of languages, only to find them cluttered, complex, and often slow. In 2009, the company released its solution: a statically typed language that looks like C but includes background intelligence to save programmers from having to specify types and juggle malloc calls. With Go, programmers can have the terseness and structure of compiled C, along with the ease of using a dynamic script language.

While Sun and Apple followed a similar path in creating Java and Swift, respectively, Google made one significantly different decision with Go: The language’s creators wanted to keep Go “simple enough to hold in one programmer’s head.Thus, there are few zippy extras like generics, type inheritance, or assertions, only clean, simple blocks of if-then-else code manipulating strings, arrays, and hash tables.

The language is reportedly well-established inside of Google’s vast empire and is gaining acceptance in other places where dynamic-language lovers of Python and Ruby can be coaxed into accepting some of the rigor that comes from a compiled language.

If you’re a startup trying to catch Google’s eye and need to build some server-side business logic, Go is a great place to start.

Groovy: Scripting goodness for Java

The Java world is surprisingly flexible. Say what you will about its belts-and-suspenders approach, like specifying the type for every variable, ending every line with a semicolon, and writing access methods for classes that simply return the value. But it looked at the dynamic languages gaining traction and built its own version that’s tightly integrated with Java.

Groovy offers programmers the ability to toss aside all the humdrum conventions of brackets and semicolons, to write simpler programs that can leverage all that existing Java code. Everything runs on the JVM. Not only that, everything links tightly to Java JARs, so you can enjoy your existing code. The Groovy code runs like a dynamically typed scripting language with full access to the data in statically typed Java objects. Groovy programmers think they have the best of both worlds. There’s all of the immense power of the Java code base with all of the fun of using closures, operator overloading, and polymorphic iteration.

Finally, all of the Java programmers who’ve envied the simplicity of dynamic languages can join the party without leaving the realm of Java.

CoffeeScript: JavaScript made clean and simple

Technically, CoffeeScript isn’t a language. It’s a preprocessor that converts what you write into JavaScript. But it looks different because it’s missing plenty of the punctuation. You might think it is Ruby or Python, though the guts behave like JavaScript.

CoffeeScript began when semicolon haters were forced to program in JavaScript because that was what Web browsers spoke. Changing the way the Web works would have been an overwhelming task, so they wrote their own preprocessor instead. The result? Programmers can write cleaner code and let CoffeeScript turn it back into the punctuation-heavy JavaScript Web browsers demand.

Missing semicolons are only the beginning. With CoffeeScript, you can define a function without typing function or wrapping it in curly brackets. In fact, curly brackets are pretty much nonexistent in CoffeeScript. The code is so much more concise that it looks like a modernist building compared to a Gothic cathedral. This is why many of the newest JavaScript frameworks are often written in CoffeeScript and compiled.

Haskell: Functional programming, pure and simple

For more than 20 years, the academics working on functional programming have been actively developing Haskell, a language designed to encapsulate their ideas about the evils of side effects. It is one of the purer expressions of the functional programming ideal, with a careful mechanism for handling I/O channels and other unavoidable side effects. The rest of the code, though, should be perfectly functional.

The community is very active, with more than a dozen variants of Haskell waiting for you to explore. Some are stand-alone, and others are integrated with more mainstream efforts like Java (Jaskell, Frege) or Python (Scotch). Most of the names seem to be references to Scotland, a hotbed of Haskell research, or philosopher/logicians who form the intellectual provenance for many of the ideas expressed in Haskell. If you believe that your data structures will be complex and full of many types, Haskell will help you keep them straight.

Julia: Bringing speed to Python land

The world of scientific programming is filled with Python lovers who enjoy the simple syntax and the freedom to avoid thinking of gnarly details like pointers and bytes. For all its strengths, however, Python is often maddeningly slow, which can be a problem if you’re crunching large data sets as is common in the world of scientific computing. To speed up matters, many scientists turn to writing the most important routines at the core in C, which is much faster. But that saddles them with software written in two languages and is thus much harder to revise, fix, or extend.

Julia is a solution to this complexity. Its creators took the clean syntax adored by Python programmers and tweaked it so that the code can be compiled in the background. That way, you can set up a notebook or an interactive session like with Python, but any code you create will be compiled immediately.

The guts of Julia are fascinating. They provide a powerful type inference engine that can help ensure faster code. If you enjoy metaprogramming, the language is flexible enough to be extended. The most valuable additions, however, may be Julia’s simple mechanisms for distributing parallel algorithms across a cluster. A number of serious libraries already tackle many of the most common numerical algorithms for data analysis.

The best news, though, may be the high speeds. Many basic benchmarks run 30 times faster than Python and often run a bit faster than C code. If you have too much data but enjoy Python’s syntax, Julia is the next language to learn.

Polina Mikhan

Polina Mikhan
Polina.Mikhan@altabel.com 
Skype ID: poly1020
Business Development Manager (LI page)
Altabel Group – Professional Software Development

 


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