 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. Now these are extremely powerful applications and very frequently used for sort of professional production level coding. In data science, the place where you're going to see these languages most often is in the bedrock, the absolute fundamental layer that makes the rest of data science possible. So for instance, C and C++ sees from the 60s, C++ is from the 80s, and they have extraordinarily 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're 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 of speeds important, then you can actually use C code in R or other statistical languages. Next is Java. Java is based on C++. Its major contribution was the war or right once run anywhere. The idea that you're going to be able to develop code that is portable to different machined and different environments. And because of that, Java is actually the most popular computer programming language overall against all tech situations. And the place where you would use these in data science is 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 that you're probably going to use the people who are going to use it are primarily going to be engineers. So the engineers and the developers, 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 the work is on the front end or closer to the high level languages, like our or Python. In some C C++ and Java form a foundational bedrock and the back end of data and data science. And they do this because they're 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.