If you are a data scientist, you will agree that the need for better tools to handle big data is growing at a rapid pace. Even small businesses are moving away from using basic data analysis tools like Excel and Access. When data experts create these powerful tools, they have to use the best programming software so that they can include useful features.
Most data experts are aware of programming tools like Python and R that have been around since the early ‘90s. What many do not know is which one is better between the two. After detailed research, this article will guide you through the two so that you can understand the uses of each. These insights will guide will help you in your future projects that are related to data analysis.
What is the Python Programming Language?
This is a programming language that can be used on absolutely anything. When using a programming language as a beginner, you will quickly recognize that it is easy and straightforward. Many experts have also praised the language for its versatility in making different software and apps that are useful in the handling of data. As of now, programmers are optimistic that Python coding is going to become even more satisfying than it is currently.
Did you know that you can use Python to create data solutions? Most people, including data experts, leave this to R programming. But Python is a flexible environment for all types of programming. However, it might have some limitations when it comes to coding for data analysis.
Owing to the fact that it is simple to use, beginners in data analysis prefer to set their coding foundation with Python before they move on to R, which is more specific to statistics. But when coding with the program, you will realize that some codes are ambiguous and can be misinterpreted. Their meaning comes clear when the scope of work is well-defined. All in all, this general programming software is very versatile and cost-effective according to Active Wizards experts. Thus, companies with a relatively low amount of data can seek solutions form beginners in data analysis who can create a model using Python.
What is the R Programming Language?
Now, the R programming environment is more specific to what it offers. If you are in the statistics field, this is the right environment to code your software and apps. The libraries are designed to support all coding for statistical purposes. Since its inception, it has helped experts to achieve their goals in many ways. When coding data solutions with R, there are various steps to use. Whether you are a beginner or not, you will have an easy time if you follow them. They include the following;
- Programming – Being the first stage, the experts can design and come up with the software of their choice. They are guided by the needs of the company or client they are working for. Hence, they should first understand what type of data that is coming through and the intended results.
- Transforming – Since R has numerous libraries that are loaded with the right tools, the data scientists can explore many options. Luckily, R programming has been simplified to accommodate both starters and experienced programmers who can help each other in the transformation of data.
- Discovering – Now, this is the most useful part of R according to the experts. This stage lets programmers analyze data, which is the main purpose of going this direction. It is a step towards getting the ultimate solution for the company you are working for. Thus, it is crucial to take enough time here.
- Modeling – Now that you have investigated and analyzed the data, the next step is to come up with a suitable model for the entire project of data analysis. The environment has a plethora of tools that will help you to achieve this.
- Communicating – An important phase of the creation of your data solution is the communication phase. It is the stage where managers and other stakeholders get to understand the results of the data analysis. It is primarily done through reports and visual presentations like graphs.
So, Which Is Better, R, or Python?
From the insights that are highlighted above, it is clear that each programming environment has its pros and cons. The preference of which one to use is dictated by a variety of factors. But it is clear that R is more specific to statistics, and big data is all about this. Thus, data experts in your company are better off if they understand R. It might look complicated, but the results are incredible.
The takeaway is that both programming environments are good. Clients will pick the option that is scalable to them. Reputable and experienced data experts will always recommend the best environment to use for your data solution needs.