C, C++, & Java – Coding – 4.4

C, C++, & Java – Coding – 4.4


When you talk about coding in any field, one
of the languages or one of the groups of languages that come up most often are C, C++, and Java. These are extremely powerful applications
and very frequently used for professional, production level coding. In data science, the place where you will
see these languages most often is in the bedrock. The absolute fundamental layer that makes
the rest of data science possible. For instance, C and C++. C is from the ‘60s, C++ is from the ‘80s,
and they have extraordinary wide usage, and their major advantage is that they’re really
really fast. In fact, C is usually used as the benchmark
for how fast is a language. They are also very, very stable, which makes
them really well suited to production-level code and, for instance, server use. What’s really neat is that in certain situations,
if time is really important, if speeds important, then you can actually use C code in R or other
statistical languages. Next is Java. Java is based on C++, it’s major contribution
was the WORA or the Write Once Run Anywhere. The idea that you were going to be able to
develop code that is portable to different machines and different environments. Because of that, Java is the most popular
computer programming language overall against all tech situations. The place you would use these in data science,
like I said, when time is of the essence, when something has to be fast, it has to get
the job accomplished quickly, and it has to not break. Then these are the ones you’re probably
going to use. The people who are going to use it are primarily
going to be engineers. The engineers and the software developers
who deal with the inner workings of the algorithms in data science or the back end of data science. The servers and the mainframes and the entire
structure that makes analysis possible. In terms of analysts, people who are actually
analyzing the data, typically don’t do hands-on work with the foundational elements. They don’t usually touch C or C++, more
of the work is on the front end or closer to the high-level languages like R or Python. In sum: C, C++ and Java form a foundational
bedrock in the back end of data and data science. They do this because they are very fast and
they are very reliable. On the other hand, given their nature that
work is typically reserved for the engineers who are working with the equipment that runs
in the back that makes the rest of the analysis possible.

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