Python and R have long been the standard for Data Science. The essence of their opposition is that both languages are great for working with statistics. While Python has clear syntax and a large number of libraries, the R language was developed specifically for the statistician, and therefore is equipped with high-quality data visualization. SQL stands out — because if the data is already in tables, then it’s more luck than a reason for frustration — and Scala — mainly due to the fact that the most popular distributed data processing framework Spark is written in it.
Introduction Into Apache Spark
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To conduct primary data analysis and decide on the future of a feature, SQL and the command line alone are enough because data science is, first of all, not about libraries with catchy names but an approach. Nevertheless, such minimalism has its limits (and a beginner may generally be scared off), and at some point, you will still have to turn to more advanced research tools.
Python’s thirtieth anniversary has crept quite unnoticed. Over its long history, Python has been reborn several times, losing backward compatibility, but has always remained popular both among developers in general and, in particular, among data scientists. There are several reasons for this.
Benefits of Python in Data Science
- Simple yet expressive syntax. Knowledge of English at the first grade school level is already a victory because Python's basics can be considered mastered. Further, it will not be much more difficult. If you are already familiar with Java, for example, you will be pleasantly surprised at how easy it is to say hello to the world.
- A rich selection of libraries. And it’s not only about libraries of machine learning algorithms — cloud storage, streaming services, and even games are developed in Python (although sometimes they have to beat the brakes as a feature, not a bug).
- High documentation culture. Python itself is well-documented, and usually, its libraries continue this tradition. For instance, Python exclusively supports Type Annotations, which greatly facilitate overall code readability and debugging of your functions.
Python’s Type Annotations — Why You Always Should Use It
Beauty and power of type annotations in Python or why I always use it
However, for all its glory, Python is not without its drawbacks. Python is often (and sometimes deservedly) called slow; it still lacks usable, so-called, Object-Relational Mapping (ORM) tools, and writing a real big project in it is quite a lot of hard work and requires good discipline. But as with any tool, it’s important to know how to use it. Speaking of instruments.
Python tools for the data scientist
As mentioned earlier, Python is notable for its extensive set of libraries and tools. When talking about data science, the following should be mentioned first:
- Pandas is a powerful data manipulation library. Allows you to conduct research on new data, test hypotheses quickly, and get a report—one of Python's main benefits.
- Scikit-learn is a large library of machine learning and data processing algorithms. A considerable part of the competition on Kaggle was won using only it in tandem with Pandas.
- Keras and PyTorch are libraries used to train deep neural networks. Suitable for tasks related to images, audio, and video files.
- IPython Notebook — when talking about Python, one cannot forget to mention it. The standard development environment is not quite suitable for the data scientist in the data mining process. There is a need for a format that would allow, for example, to run a costly algorithm, and when it is completed, play a little with the results, research them and build graphs. This is where the laptop format comes in. This is a graphical interface that opens in a regular browser and is basically a sequence of cells where you can write and execute code using shared memory to store data.
🚀R Language 🚀
In 2020, the R language remains one of the most popular languages for Data Science and Statistics, consistently gaining an increasing share of views in the relevant sections of StackOverflow. At the same time, academic questions lead with a significant preponderance: first of all, R is a language with a rich set of libraries for machine learning and statistics, which is especially important for research purposes.
Benefits of R in Data Science
- Rich ML ecosystem, a huge number of libraries of statistical methods. As noted earlier, R is especially popular in the academic environment, which leads to the fact that often new methods are first implemented on it.
- A fairly convenient proprietary development environment, RStudio or VS Code, will be easy to understand, especially if you have prior experience in MATLAB.
- Unusual syntax tailored to the needs of statistics. An experienced programmer with knowledge of another language may experience difficulties in acclimatization, but users with a mathematical background will easily perceive the logic of the language.
- Native support for vector computing. A cool bonus means that you can program reasonably fast implementations of mathematical methods in R using vector and matrix calculations.
R tools for the data scientist
Let’s talk about the R library riches mentioned. Here are some of the basic but powerful libraries that can be armed with extensive research or good spots in Kaggle:
- Dplyr is a “data manipulation grammar” library with functionality similar to Pandas.
- Ggplot2 and Esquisse are powerful graphing libraries.
- Shiny is the most useful library for creating web applications with interactive visualizations of research.
- Caret, randomForest, Mlr, etc. — dozens of libraries with machine learning methods. One of them will definitely work.
▶ Python vs. R in Data Science: Which is Better? ◀
Both languages have their own advantages and disadvantages. Any of them can be suitable; it all depends on your tasks. Here are some points that can help you with your choice:
- Have you programmed in other languages already? If so, it may take you some time to get used to R. Python is much more familiar, except for some nuances.
- Are you planning to work in a scientific field, or are you inclined to be closer to practice? Python is more close to production and is more often used in commercial projects. At the same time, R. is more popular in academic circles.
- Do you want to improve your horizons in machine learning methods? Or will it be enough for you to familiarize yourself with several of the most popular methods and devote more time, for example, to big data processing algorithms? In the first case, you definitely need R; in the second, you will find more Python features.
- Do you want to be engaged in implementing your developments and program anything other than predictors? If so, Python is better for you, but you will most likely need something else (like Java, Scala, or C++).
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