Data science has been known as the sexiest job of the 21st century a couple of times now. It’s been a booming field in the IT industry for quite a long time now. If you take a good look around, it won’t be hard to see hundreds of aspiring data scientists in the market.
However, data science is always closely associated with the high-level and declarative world of programming languages – the best example of which is Python. While there are other languages people use, such as R and Scala, there’s one thing common in them – the quick learning curve. You can learn more about R and Python by clicking here.
Other languages such as C/C++ seem to be lagging a lot, and there are hardly any aspirants that seem to be interested in using them. So, today, we will take a detailed look into the matter and find out why exactly this is the case; and why don’t people like using languages such as C++ in data science.
What is Data Science?
Let’s take a comprehensive look at what data science really is since many interpretations are going around in the market nowadays. This will help you get a better idea of what we’re going to talk about.
Data science is an interdisciplinary field that involves using customer data in any form available to a business to help make better decision-making and make their products more customer-centric. It uses advanced tools and techniques to extract valuable insights from a lot of raw data.
So, anything you’re doing with customer data with the ultimate goal of extracting actionable insights from it for the betterment of business is called data science.
Why is C++ not used for Data Science?
First off, let me clarify that just because C++ isn’t used for data science at a large scale doesn’t mean that it cannot be used for data science. It’s actually very beneficial to use C++ for data science, and it could achieve what Python could never, as we will see further in the article.
C++ is not used widely for data science because most data scientists don’t have a Computer Science background. Hence, complex languages that require a fundamental knowledge of programming aren’t their strongest suit. However, a lot of data scientists still prefer using C++ for data science over any other language.
So, arguing that C++ isn’t used for data science isn’t true at all. It’s just that people that belong to mathematics or other backgrounds prefer using declarative languages such as Python so they can focus on the part they’re good at instead of putting hours in complex concepts of programming such as pointers.
Benefits of using C++ for Data Science
While using C++ isn’t a necessity to become a data scientist, it’s a huge plus. Let’s take a look at some crucial gains you’ll be getting if you use C++ in data science.
First off, C++ is extremely fast when you compare it to languages like Python. Everything happening in a Python interpreter is processed in the form of C on the backend. Yes, as shocking as it might come to you, this is the truth! Python is but a wrapper on top of C.
So, if you’re playing directly on the C++ field, expect things to process a lot faster. Since the processes of data science are already long, as they involve tons of data, being able to work with a faster language could be very important to your use case. While the difference in speed might not be noticeable in small projects, it’s certainly not negligible when it comes to the big ones. In the ideal scenario, you might be able to process something in days which would have taken months otherwise.
As a matter of fact, C++ is the only language that can process data over a gigabyte within a second!
Given its processing capabilities, another great use of C++ in the data science world is developing new libraries using it. The libraries built on C++ can be used with other languages as well. This, bundled up with speed, is a perfect combination for new libraries that data scientists want to build.
Moreover, C++ is closer to the machine and lets you play with the hardware on a low level. That way, you can tweak the underlying hardware directly according to your needs – to a certain extent. On the other hand, you can’t do the same in Python. When you’re building high-end products, tweaking your software while keeping your hardware in mind is the key to being efficient.
From small and medium-sized businesses to Fortune 500 conglomerates, the success of a modern business is now increasingly tied to how the company implements its data infrastructure and data-based decision-making. According
Any form of the systematic decision-making process is better enhanced with data. But making sense of big data or even small data analysis when venturing into a decision-making process might
Data is important in decision making process, and that is the new golden rule in the business world. Businesses are always trying to find the balance of cutting costs while
Should you learn C++?
Now that you know C++ is used for data science and, more so, how beneficial it is, is it a wise choice to learn C++ only for your data science career? Is it worth putting in all those hours when you can do quite well with declarative languages such as Python? Well, it’s certainly not an easy question.
The first question to ask yourself here is what background you are coming from. If you belong to mathematics or maybe physics background, trust me, it will take a lot of sweat to get comfortable with a language such as C++. However, once you’re comfortable with it, you will be able to code more intuitively since you will start to understand what goes on underneath the programming language; learning other languages will also be a piece of cake for you.
But the effort needed can be better spent elsewhere. Data science is a vast field and already covers many disciplines such as statistics, visualizations, data cleaning, and processing. So, picking a task as tedious as this might backfire on your data science career.
However, there are areas of data science that are very closely associated with data or ML engineering and involve building data pipelines or machine learning models. In such tasks, speed is the key, and being able to configure your models to machine-level is also very important. If you belong to these areas, I’d say learning C++ wouldn’t be a time-waste at all, and the time you spend learning it will give you an amazing return on your career.
Hence, unless your background is not close to programming or you’re most interested in the data analysis part of data science, not learning C++ will be a good option for you. There are plenty of tools out there that will fill the void for you, and Python/R would be more than enough for you as the programming language(s) you need.
A lot of data scientists have been spoilt by declarative programming languages such as Python. While it’s a great thing that anybody with a non-programming background could jump into data science, we shouldn’t forget that just because people don’t use low-level languages for data science, they are incapable of being used.
Throughout the article, we found out that not only is C++ is a suitable language for data science, but it could provide data scientists features that Python can’t. Moreover, we discussed some important aspects of using C++ with data science and if it’s worth learning if you’re coming from a non-CS background.