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Data scientists and analysts are required to know programming languages to become proficient in their respective fields.
According to the 2018 Kaggle Machine Learning and Data Science Survey, R and Python are both among the top database query languages used regularly. They also both appear in the IEEE Spectrum’s list of top ten coding languages for developers to know in general.
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Released in 1995 by Canada’s Ross Ihaka and New Zealand’s Robert Gentleman, R is a low-level programming language for statistical computing and graphics. It’s a GNU project that’s essentially a variant of the S language. The community of developers contributing to R is highly active, and it is known for its powerful object-oriented facilities and support for rendering data visualizations.
Python, on the other hand, is a high-level, interpreted, interactive, and object-oriented scripting language. Since it uses English keywords, it’s incredibly readable and, generally speaking, has fewer syntactic constructions than other programming languages. There are many valuable resources available to developers and data professionals seeking to learn Python.
In this article, we’ll compare and contrast R vs Python for data science with a focus on their security implications.
Natural language processing
Natural language processing deals with the interaction between human languages and data science and has wide-ranging impacts across various industries including finance, healthcare, and media. In simple terms, natural language processing focuses on teaching computers to process natural human languages such as text or speech data and perform analysis.
One of the key benefits of data science programming in R is that it’s great for data visualizations such as graphs and charts. R packages make it easy for data scientists to quickly generate graphs to visualize patterns and trends in large datasets and identify outliers and anomalies.
Being a versatile programming language, Python seamlessly integrates with a wide variety of project environments. This makes it easy to use in its own way for natural language processing projects.
It’s also worth mentioning that while R has over 10,000 libraries for data analysis, manipulation, and visualization, Python only has 200 standard libraries. Popular R libraries for natural language processing include wordcloud, tidytext, and text2vec. NLTK, scikit-learn, and SpaCy are some of the most widely-used Python libraries for natural language processing.
However, the security implications of R in natural language processing stem from its heavy use of third-party algorithms that can potentially lead to inconsistencies. R requires data scientists to use a new algorithm each time for development and come up with new ways to model data and make predictions. Considering that R’s documentation is rather limited, it becomes difficult for data scientists to learn every new package. In terms of consistency, Python is much easier to use since it has a much larger developer community.
That said, Python’s main security vulnerabilities relating to natural language processing are SQL injections. To protect against these, you’ll need to take steps to make sure your code fetches user input and database input in a secure way, for example, by using parameterized queries and stored procedures.
Still, the best language for data science and natural language processing depends on your specific project needs and preferences.
Data exploration
Data exploration is the first step in data analysis in which data scientists explore large, unstructured datasets to uncover patterns and characteristics. This helps them create a broad picture of trends rather than dig deeper into finer details. To this end, it’s important to use a programming language that focuses on data visualizations (like graphs and charts) and reports for data exploration projects.
Generally, data scientists use R more often than Python when it comes to data exploration. This is because of R’s massive toolbox of data visualization libraries and interactive style. In addition, R has a large community for data exploration. You should earn Data Science Online Course or Data Science Certification.
However, some data scientists prefer to use Python when their project requires better performance or structured code. Python’s scikit-learn, pandas, and numpy libraries are popular for data exploration.
In terms of security for data exploration use cases, both R and Python are in the same boat. The more libraries and packages you use, the more security vulnerabilities you’ll need to protect against. One major threat, for example, is man-in-the-middle attacks.
R packages include executable code which means you need to make sure you’re following best security practices. This means that you should download them from a secure server, verify the MD5 checksums, and configure R for secure file downloads. R packages are typically installed via CRAN, GitHub, and Bioconductor.
On the flip side, Python packages are installed via Pip. Best practices indicate that you should double-check the package’s name before installing it and make sure it’s updated.
Deep learning
Deep learning is a sub-field of machine learning and artificial intelligence that imitates the ways humans typically gain knowledge. It helps data scientists collect, analyze, and interpret large amounts of data. Deep learning is an important part of data science, and it paves the way for automating predictive analytics.
Python’s Keras and TensorFlow packages have made it significantly easier for data scientists to adopt deep learning workflows. Most data scientists use either TensorFlow or PyTorch in their deep learning projects.
R has only recently added support for Keras and TensorFlow packages. In fact, the Keras package in R can be thought of as interfaces to Python’s original Keras package. It’s easy to see why more deep learning work is being done in Python than in R.
The way R was originally designed poses problems when working with large datasets. Security capabilities weren’t built into the R language from the outset. In addition, R can’t be embedded in a web browser, which means data scientists can’t use it for web-like app projects.
Moreover, it’s practically impossible to use R as a back-end server for calculations due to its lack of security over the web. These security vulnerabilities, though unresolved, can be lessened with the use of virtual containers on the cloud.
Conclusion
Both R and Python are open-source programming languages for data science with large user bases. In fact, many data scientists use R and Python interchangeably in their projects.
If your data science projects require a flexible and versatile programming language that’s extendable with machine learning packages then Python is the best choice for you. It performs better in data manipulation and repetitive tasks.
However, if your project is statistics-heavy or requires lots of data visualization then R is the better choice. R is also preferable for data exploration projects as long as you’re downloading R packages securely.
It’s important to keep in mind that R has some serious shortcomings when it comes to security over the web which can only be lessened by using virtual containers.