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10 Best Programming Languages to Learn in 2019 (for Job & Future)

The most important skill to learn in today’s world is to know how to write a computer program.

Be it the autopilot in an aircraft or digital speedometer in your bike, computers in various forms surround us.

Various new programming languages are coming up that are suited for different categories of developers (beginners, intermediate, and experts) as well as for different use cases (web application, mobile applications, game development, distributed system, etc).

Let us take a look at best Programming Languages to learn in 2019 for a job and for future prospects:

Python is fast, easy-to-use, and easy-to-deploy programming language that is being widely used to develop scalable web applications.

A lot of startups are using Python as their primary backend stack and so, this opens up a huge opportunity for full-stack Python developers.

If you are looking for a development based job at a large organization, Java is the language that you should learn.

This opens up a huge opportunity for Java developers given the fact that Google has created an excellent Java-based Android development framework –

C++ is also widely used by competitive programmers owing to the fact that it is extremely fast and stable.

STL is a pool of ready-to-use libraries for various data structures, arithmetic operations, and algorithms.

The library support and speed of the language make it a popular choice in the High-frequency trading community as well.

These days, many organizations, particularly startups, are using NodeJS which is a JavaScript-based run-time environment. Node.js lets developers use JavaScript for server-side scripting—running scripts server-side to produce dynamic web page content before the page is sent to the user’s web browser.

If you are looking for that cool tech job at your favorite startup, you should seriously consider learning JavaScript.

Go provides excellent support for multithreading and so, it is being used by a lot of companies that rely heavily on distributed systems.

Those who wish to join a reasonably well old organization as a backend developer should aim to learn PHP programming.

C# is widely used for backend programming, building games (using Unity), building Window mobile phone apps and lots of other use cases.

Top 10 Machine Learning Tools You Need to Know About

The era of Machine Learning is here and it’s making a lot of progress in the Technological field and according to a Gartner Report, Machine Learning and AI is going to create 2.3 million Jobs by 2020 and this massive growth has led to the evolution of various Machine Learning Tools that we will discuss in this article.

Machine learning is a type of Artificial Intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention.

KNIME (Konstanz Information Miner), is a free and open-source data analytics, reporting, and integration platform built for powerful analytics on a GUI based workflow.

TensorFlow provides an accessible and readable syntax which is essential for making these programming resources easier to use and being a low-level library provides more flexibility and with the new v2.0, it’s just going to be on the top for any Machine Learning or Deep Learning purpose.

The support for CUDA ensures that the code can run on the GPU, thereby decreasing the time needed to run the code and increasing the overall performance of the system.

RapidMiner is a data science platform for teams that unites data prep, machine learning, and predictive model deployment.

It has a powerful and robust graphical user interface that enables users to create, deliver, and maintain predictive analytics.

With RapidMiner, uncluttered, disorganized, and seemingly useless data becomes very valuable as it simplifies data access andlets you structure them in a way that it is easy for you and your team to comprehend.

Google’s human labeling service can put a team of people to work annotating or cleaning your labels to make sure your models are being trained on high-quality data.

It is a complete framework for building production-grade computer vision, computer audition, signal processing, and statistics applications.

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