During my early days as a systems engineer in the early 2000s, only a narrow group of scholars were familiar with the term “data science”. As far as my lecturers were concerned, it was nothing more than a passing trend. Oh, how inaccurate their assessments were! Nowadays, it is a highly respectable and widely recognised field of study.
In the past twenty years or so, data science has experienced tremendous expansion and development, establishing itself as one of the swiftest emerging departments in computer science. It is impressive to acknowledge that since 2023, California University’s hiring rate has skyrocketed by an outstanding 650% due to this growth in data science.
If you have been an active participant in the realm of data science for a while, you might have observed a divergence of opinions. One faction is comprised of folks who have been conducting statistical analyses using R since the turn of the millennium. On the other hand, there are advocates for the use of Python, claiming that it is the sole path forward.
One alternative approach involves the usage of specialized data analysis software, such as SPSS, Stata, or MatLab. However, even the most introverted data scientist will eventually need to formulate their own strategies to effectively handle unstructured data, which cannot be processed through pre-existing software.
Without a doubt, becoming proficient with both is the most evident resolution. However, it is only a realistic possibility if one has enough time to learn both. For the sake of argument, let us suppose that our hypothetical novice data scientist is compelled to opt for only one.
The answer to this conundrum is not always straightforward. Firstly, both programming languages offer significant advantages to data science with their competence in key areas of this realm, such as data manipulation, impromptu analysis, and experimentation.
With that in mind, instead of dwelling on the basics, let us focus on the dissimilarities between the two.
Monetary Success and Recognition
If you are embarking on a new data science team, it is recommended to concentrate on acquiring knowledge of the tools and technologies they utilise. Nevertheless, if you are a newcomer to this field seeking to make a selection based on job market demand, Python is the undisputed victor. As per statistics from GitHub, Python surpasses R by a significant margin, ranking as the third most widespread programming language in 2021, whereas R does not even feature in the top 20.
Python appears to be the programming language with the most favourable future employment opportunities. Current data indicates a 50% spike in the demand for Python expertise in job requirements. Additionally, 10% of data scientists have already made the switch to Python, revealing the loyalty and devotion to this language. On the other hand, R’s reputation is declining in a hurry. Nevertheless, over fifty percent of data scientists still employ both languages. What might be the reasons for this?
The R Framework has Exceptional Potency.
I have never had a special inclination towards numbers. Whenever I come across R and its various packages, I feel as though I am embarking on a statistics course at a college level, despite being more than qualified to perform data analysis. R has been used by scholars and statisticians for an extended period, and the vast variety of possibilities it presents is quite impressive. Currently, approximately 12,000 packages are maintained on R’s principal repository (CRAN).
Are you mulling over the possibility of attempting the Lavaan assessment? You have made a wise decision. You can find guidance on factor analysis in the Psychology handbook. However, keep in mind that even though Python offers a multitude of functionalities, R is still the software of preference when it comes to more intricate assignments.
Numerous R developers are hired in academia. Therefore, their packages typically address prevalent problems encountered in educational environments. To illustrate, the Psych package is customised for psychologists who conduct psychometric analyses.
If you require such elements, R is the ideal software for you:
- Functions customised for data that clean, organise, and format information for additional analysis
- Pre-programmed functions for analysis and interpretation
- Processes that generate tailored graphics for these evaluations
- Comprehensive documentation obtained from academic literature supports everything,
No programming language can rival the flexibility of Python.
Python has been conceptualised with emphasis on readability, resulting in an accessible entryway for less proficient developers. Its syntax is designed to be easy to read, meaning that even beginners can comprehend the code produced by a seasoned developer. In comparison to other programming languages like R, which have been in use for a longer period, Python is a more desirable option undoubtedly.
Python’s adaptability as a multipurpose language renders it a more appropriate alternative for cross-platform development than R. For instance, one of my coworkers is presently creating a Python game that will record players’ choices, transmit the data to a server for evaluation, and then make the resulting information available to the academic community via a website.
Until recently, R was the preferred option for data scientists due to its dominant machine learning capacities. However, this is not the scenario anymore. Presently, Python is equally proficient and frequently better than R for AI programming.
The swift growth of Python can be credited to its capability to bring together professionals in programming and science, creating a common area for these disciplines in the field of data science.
Python’s extensive system may seem overwhelming to novices. Nonetheless, as a data scientist myself, I believe it is better to get to know some of Python’s functionalities first before delving into its wider range of capabilities. The Numpy, Scikit-learn, Pandas, Scipy, and Seaborn libraries are all indispensable tools for beginner data scientists.
Make the most of the best resource at your disposal
A developer interested in pursuing data science should initiate with Python, and only resort to R when required. On the other hand, a researcher may prefer to begin with R and change to Python as their work expands in scope.
Generally, most people favour one language over the other based on their inclination and the resources available in each. Consequently, it is plausible to transfer R functions to Python, and vice versa.
It is not about foreseeing which language will triumph, but instead, it is about determining which language you are willing to devote the most time and energy towards mastering. Python is growing at a rapid rate; hence it may not be the most fitting language for initial adoption. Nonetheless, experienced data scientists should be adept at using both languages effectively.
