1. Python

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. It was created by Guido Van Rossum from 1985 - 1990. Python source code is also available under the GNU General Public License.

Pros:

• East to maintain
• Very easy to learn and use
• Extensive Support Libraries
• Focuses on code readability
• Supports multiple systems and platforms
• Helps to improve Programmer's Productivity
• Open-source with an ever-growing community support
• Ideal for building prototypes and testing out ideas faster
• Allows you to scale even the most complex applications with ease
• Creating and using classes and objects is easy thanks to OOP characteristics

Cons:

• Not proper for the mobile environment
• The database access layer is a bit primitive
• Problems with threading because of Global Interpreter Lock
• Speed limitations because of being an interpreted programming language

Usage:

• Web development, scientific and analysis applications, desktop application, Business applications.
• It is broadly used in Artificial Intelligence and Machine Learning

Learning Difficulty: Very Easy

Official Site: python.org

Latest Version: Python 3.8.5

2. Java

Java is a programming language. Java is a high level, object-oriented, secure, and robust programming language.

Java was developed in the year 1995 by Sun Microsystems. Before Java, its name was Oak. Since Oak was already a registered company, so James Gosling and his team changed the Oak name to Java. James Gosling is known as the father of Java.

Java is another popular choice in large organizations and it has remained so for decades. Java is widely used for building enterprise-scale web applications. Java is known to be extremely stable and so, many large enterprises have adopted it. If you are looking for a development based job at a large organization, Java is the language that you should learn. Java is also widely used in Android App Development. Almost any business today needs an Android Application owing to the fact that there are billions of Android users today. This opens up a huge opportunity for Java developers given the fact that Google has created an excellent Java-based Android development framework - Android Studio.

Pros:

• Secure
• Multithreaded
• Platform-Independent
• Distributed computing
• Object-Oriented language
• A large number of open-source libraries
• Java offers APIs for different features like Database connection, networking, XML parsing, utilities, etc.

Cons:

• Single-Paradigm Language
• slow and has a poor performance
• Memory management in Java is quite expensive
• The absence of templates can limit you to create high-quality data structures.

Usage:

• Java broadly used for developing mobile apps, web apps, and large amounts of data.

Learning Difficulty: Easy

Official Site: Java

Latest Version: Java 14

3. R

R was designed by Robert Gentleman and Ross Ihaka at the University of Auckland. This project was conceived in 1992, but it's the initial version released in 1995 and a stable beta version in 2000.

R programming language is one of the most commonly used programming languages for Data Analysis and Machine Learning. R provides an excellent framework and built-in libraries to develop powerful ML algorithms. R is also used for general statistical computing as well as graphics. R has been well adopted by enterprises. Those who wish to join the “Analytics” team of a large organization should definitely learn R.

Pros:

• Highly extensible
• Powerful package ecosystem
• Active, mushrooming community
• Comprehensive statistical analysis language
• R is good for GNU/Linux and Microsoft Windows.
• Ability to run seamlessly on various operations systems
• R is open-source software. Therefore, anyone can use and change it
• As a statistical language which is considered to be very easy to code
• R is cross-platform which means it can seamlessly run on different operating systems.

Cons:

• Lacks security features
• No strict programming guidelines
• Not have the best memory management
• Quality of some packages is not good

Usage:

• Data Science projects, Statistical computing, Machine learning

Learning Difficulty: Hard

Official Site: R

Latest Version: R 4.0.2