When I first started out in systems engineering in the early 2000s, “data science” was a term that was only known to a select few academics. A few of my lecturers mentioned it as if it were a fleeting trend. How wrong they were; it is now a widely recognized and valued discipline.
Over the last two decades, data science has seen an immense growth and is now considered one of the most rapidly advancing disciplines within computer science. It is remarkable to note that California University’s employment rate has increased by a staggering 650% since 2023.
If you have been involved in the field of data science for some time, you may notice a division in opinion. On one side, there are those who have been utilizing R for statistical analysis since the start of the 21st century. On the other hand, there are those who advocate for the use of Python, believing it is our only path forward.
An alternative approach involves utilizing data analysis programmes such as SPSS, Stata, or MatLab. Ultimately, however, even the most introverted data scientist will need to devise their own strategies in order to effectively process unstructured data, which are not available through pre-existing programmes.
Certainly, acquiring knowledge of both is the obvious solution, however, this is only a viable option if one has sufficient time to explore both. For the sake of discussion, let us assume that our hypothetical inexperienced data scientist must select only one.
The resolution of the issue is not always simple. To begin with, both languages are highly beneficial for data science due to their proficiency in the core aspects of this field, such as data manipulation, ad hoc analysis, and exploration.
Therefore, rather of wasting time on the fundamentals, let’s examine the differences between the two.
Financial success and fame
If you are joining a new data science team, it is advisable to focus on learning the technologies they use. However, if you are new to the field and looking to make a decision based on job market demand, Python is the clear winner. According to data from GitHub, Python is the third most popular programming language in 2021, with R not even making the top 20.
When it comes to future employment prospects, Python appears to be the most advantageous language to know. Recent statistics show that the need for Python knowledge in job descriptions has risen by 50%. Furthermore, 10% of data scientists have already switched to Python, showing the loyalty of this language. Statistics also demonstrate that R is rapidly losing its popularity. However, more than half of data scientists still use both languages. What could be the reasons for this?
The R ecosystem is really potent.
I have never had a particular affinity for numbers. When I look at R and its packages, I feel as though I am starting afresh with a college-level statistics course, even though I am more than qualified to carry out data analysis. R has been employed by academics and statisticians for many years and the sheer number of opportunities available is quite remarkable. At present, around 12,000 packages are being maintained on R’s main repository (CRAN).
Are you considering undertaking the Lavaan assessment? This is an excellent choice. Information regarding the analysis of factors can be located in the Psychology manual. Please bear in mind that although a variety of features may be found in Python, R remains the programme of choice for more complex tasks.
Many R developers are employed in academia, so their packages often focus on common issues encountered in the educational setting. For example, the Psych package is specifically designed for psychologists who carry out psychometric studies.
If you need things like these, then R is the package for you:
- Data-specific functions that clean, organise, and format data for further study
- Prepared analysis and interpretation functions
- Operations that provide individualised visuals for such assessments
- Everything is supported by documentation pulled from scholarly texts,
The flexibility of Python cannot be matched.
Python has been designed with readability in mind from the beginning, providing an accessible entry point for less experienced developers. Its syntax has been created to be highly readable, meaning that even novice programmers can understand the code written by a seasoned developer. This makes Python a desirable choice compared to other programming languages, such as R, which have been around for longer.
Python’s versatility as a general-purpose language makes it a more suitable option for cross-platform development than R. For instance, one of my colleagues is currently working on a Python game that will capture players’ decisions, transmit this data to a server for assessment, and then provide the resulting information to the scientific community via a website.
Until recently, R was the go-to choose for data scientists due to its powerful machine learning capabilities. However, this is no longer the case. Nowadays, Python is just as capable, and often even more so, than R when it comes to AI programming.
Python’s rapid expansion can be attributed to its ability to bring together experts in both programming and science, providing a shared space for both disciplines in the realm of data science.
For those new to Python, its expansive ecosystem may appear daunting. However, as a data scientist, I believe it is best to become familiar with a few of Python’s features before gradually exploring the wider range of capabilities. The Numpy, Scikit-learn, Pandas, Scipy and Seaborn libraries are all essential tools for aspiring data scientists.
You should use the greatest resource available to you
A programmer who is looking to get into data science should start off with Python, and use R as and when necessary, while a researcher may find it more comfortable to begin with R, and switch to Python as the work they are undertaking increases.
Most individuals tend to opt for one or the other language depending on their preference and the resources available in each language. As such, it is possible to import R functions into Python, and vice versa.
It is not a case of predicting which language will be victorious, but rather a question of which language you will invest the most effort into mastering. Python is expanding quickly, so it may not be the most suitable language to start with. However, experienced data scientists should be able to use both languages proficiently.