Programming languages are basically the foundation of Data Science, and thanks to contemporary technological advancements, we now have a plethora of languages at our disposal. However, the challenge is to choose the most suited programming language for a data scientist. Learn Data Science with Python with this online training program.
Now let’s take a look at the factors you should consider before choosing a programming language for Data Science.
What is your project specification?
The first thing to think about is the aim you’re attempting to achieve. Varying jobs will need various degrees of expertise, and several languages may be better suitable for the job at hand.
How can Data Science assist you in completing your task?
Most processes you might currently be doing can be automated or streamlined using Data Science. You’ll save quite a lot of time and resources if you do it this way.
What level of expertise do you have in programming languages and Data Science?
Determine your abilities and then decide whether you are capable of advancing your expertise towards the next level. If you are a beginner, then you have to choose a programming language that is easy to learn and understand.
How much free time do you have to learn a programming language?
This should also be considered while choosing a language. If you have knowledge of programming languages that is great. But if you do not have any prior knowledge then you need to figure out how much time you have in your hands. So that you can prepare accordingly.
How extensive is your company’s usage of Data Science?
The last thing you’ll need to figure out is how extensive your company is employing Data Science. This will assist you in determining which languages to study as well as how to learn and utilize them.
These are the five questions that you need to ask yourself before picking the right programming language. However, I assume that most of the answers lead to Python and R. But why? What is there in these programming languages that beats others? Let’s discuss that now without further ado.
Python is popular for its comprehensible and easy-to-understand syntax. You may create whole research environments using a particular language like Python without having to bother with compatibility or interface difficulties.
Python code is less expensive to maintain. Python can perform virtually anything, from data handling to web scraping, and Machine Learning model deployments, thanks to integrated support. Check out this comprehensive blog, if you have any doubts about What is Python.
Python can also help with critical activities like the collection of data, analysis, modeling, and visualizations, all of which are vital aspects of working with big data. Python’s strong Data Science libraries make these jobs much easier. Keras, Scikit-Learn, Matplotlib, TensorFlow, and other sophisticated Data Science libraries are supported.
The very first element of an array in Python is addressed with a 0, such as string  for the first letter in a string in Python. It facilitates acceptance by broader users with well-established programming behaviors. Python has a quicker startup time than others, which puts it ahead of them.
When you use Python, you will never be left off. You will have huge support all the time. This language has a wide support ecosystem, which is another possible explanation for why it is one of the most important Data Science tools.
If you’re starting to program for the very first time, want to have something scalable, and/or would like to keep your career opportunities open, Python is an excellent starting point. Python is well suited for automating tasks. With Data Science, automating activities is highly beneficial since it saves time and provides important data.
While Python is a general language, R is mainly focused on statistical analysis and insightful visuals. R is one of the most popular Data Science programming languages and for a valid reason.
R is a highly flexible and simple-to-learn programming language that promotes a statistical computing and graphical ecosystem. It is capable of a wide range of tasks, including data processing, statistical modeling, and graphics. And also the perfect place for Data Science, big data, and Machine Learning.
R is a scripting language with a lot of capability. That is why it is capable of handling big and complicated large datasets. This, along with the fact that it has a rapidly developing ecosystem, makes it a top choice for data scientists. This also helps developers to quickly develop innovative applications.
Another huge advantage of the R programming language is that, unlike Python, we don’t need to learn all the libraries for the Data Science tasks, RStudio can help us with this.
For suppose, if you have an immediate task you need to finish. But you do not have knowledge of any programming language. Then, R may be a better alternative for you to get started because it requires little or no programming expertise. Using existing libraries, you can create statistical models with just a few lines of code. Python needs time to learn all the libraries however R does not. In this case, Rstudio takes care of everything.
Through RStudio, R is designed to handle large amounts of data and sophisticated computations. Its metrics syntax is straightforward for scientists with a foundation in stats, and excellent visuals allow scientists to communicate their findings more effectively.
R is a programming language that data analysts with certain programming expertise or new data scientists seeking to create a name for themselves in the research area must learn. If you’ve worked as a statistician before, you’ll be familiar with R’s structure.
In this IT industry, the struggle between programming languages has long been a controversial issue. And, considering how quickly technology evolves, a new programming language or infrastructure emerges so often.
Taking into account all of the above discussion, I would like to conclude that deciding the best programming language is based on various factors. We cannot work with only one programming language for every project.
However, Python and R have stood as the most preferred languages for most Data Science projects. R has unrivaled libraries for data analysis and research, making it more equipped for statistical inference. Python is a superior choice for Machine Learning and massive projects, particularly data processing in web apps. We hope this article is useful!