C# and C++ are two of the top programming languages of 2019. Both are easy to learn and based on object-oriented programming concepts. Before we dig into the differences, let us explore some features of each and how they are contributing to the programming world.
Java Cheat Sheet
Object-Oriented Programming Language: based on the concepts of “objects”.
Open Source: Readily available for development.
Platform-neutral: Java code is independent of any particular hardware or software. This is because Java code is compiled by the compiler and converted into byte code. Byte code is platform-independent and can run on multiple systems. The only requirement is Java needs a runtime environment i.e, JRE, which is a set of tools used for developing Java applications.
Memory Management: Garbage collected language, i.e. deallocation of memory.
Exception Handling: Catches a series of errors or abnormality, thus eliminating any risk of crashing the system.
The era of artificial intelligence
2019 has witnessed a major turnaround in artificial intelligence with a solidified belief of the customers. 41% of them firmly believing that it is going to improve their lives in some way or the other. The conscious boom created by customers in helping companies is pretty evident with the fact that over 33% think that they are already using applications and platforms that are artificially intelligent.
Our little planet is now becoming a Digital planet and by 2020 we will have 40 times more bytes than there are stars in the universe. Over 90 percent of the data sitting and floating in all possible devices and systems in the world today was simply generated in the last two years alone. These humongous volumes of data – now called Big Data – can mean a lot to businesses and can help gain insights and trends about their users and user behavior. The massive volume of data in both structured and unstructured formats is difficult to process through traditional database modeling and tools. Hence there is a need to use scientific methods, algorithms, and tools to analyze and to make sense out of Big Data and the need for Data Science and Data Analytics.