How To Choose a Programming Language to Do Data Science

Today it is really hard to imagine any scientific activity not being backed by a high-end software. Starting with a variety of studies on micro-organisms and ending with space science, scientists always use special computing programs. And in view of the fact that most of these applications are highly specialized, new software is usually developed for each task. In this article, we will discuss programming languages that are best suited for developing projects in the field of Data Science.

The Most Popular Programming Languages for Data Science

First, let us look at the statistics provided by the Tiobe service, which displays the market share for the most popular 20 Programming languages.

Each of them was created for some specific needs and purposes, and it would be misleading to state that all of them are one-size-fits-all solutions. But what about data science? Which of them are ideal for working with projects in this particular industry? If we rely on the main requirements for the programming language, which in this context can be considered ideal, then it should:

• Be adapted to operating the big data (in particular, to provide advanced tools for working with huge arrays of memory);
• Be concise (in view of the great need to create multi-level code structures with complex mathematical calculations);
• Have a lot of built-in functions for working with mathematical models.

To some extent, these requirements are best met by the languages R, Python, Java, and Scala. Further on we will concentrate on discussing them.

Java, Python, R, and Scala: What’s the Difference?

R

Created in the distant 1997, R was developed as a more functional analog to the S language with the aim to simplify the creation of stochastic software and drawing graphics (to date it has become the standard in the development of such applications). Initially, this language can be quite misleading for many developers – the numbering here starts from one, rather than usual zero and it also uses an unusual format of assignment operators).

But all these nuances pay off in full due to the fact that R supports a huge number of methods and libraries for working with numerical and statistical data. By the year 2017, R has been credited with almost 11,800 mathematical packages. This language, despite its rather low performance, is a worthy alternative to MATLAB and is widely used in Silicon Valley, including such famous companies as Facebook and Google.

Python

Python is the most time-tested language in our comparison. It was first presented publicly in 1991. To date, due to its minimalism and low entry threshold, this language is very often used in the development of complex “trend” projects based on the blockchain, machine learning, and artificial intelligence. In fact, it is a very advanced tool for working with applications that operate on middle volumes of mathematical data. In particular, Python is used by Bank of America for statistical calculations. Its major shortcoming is the relatively low efficiency though.

Java

Java is one of the most common object-oriented high-level languages, which is not, though, quite good in statistical modeling, but is ideal for working with structures and prototypes. It has a huge number of additional libraries, and the presence of a JVM virtual machine allows running Java-based apps on devices with almost any architecture. Unlike previous participants, Java is considered to be one of the “fastest” languages, primarily due to the use of JIT technology, platform-oriented code, and support for a number of processor technology optimizations that enhance the procedure for translation into the intermediary bytecode.

Scala

Scala is a Java-like safe-type programming language that is ideal for working with big data and is therefore often chosen to create Machine Learning solutions. It contains generics and existential types, as well as advanced data abstraction methods. Separately, we should note that the code written in Scala perfectly integrates with the applications in Java. For reference, such famous frameworks as Lift and Play were written in Scala.

And now let us move on to short reviews on the libraries that you will find useful in working with big data.

Pro Tips: The Most Prominent Libraries for Big Data

Of course, no scientist wants to waste their precious time creating packages and functions that could fully implement complex mathematical calculations and all kinds of operations with large amounts of data, from scratch. This is why the creators of the above-mentioned development languages have released a number of libraries for big data processing.

In particular, if we talk about the development of language R, then you should pay attention to ‘stringr’, ‘dplyr’, and ‘quantmod’ libraries. The first library from this list, designed for data collection, provides an impressive set of functions for string manipulation. The second, ‘dplyr’, is used for the comprehensive analysis of data frames. In turn, ‘quantmod’ is designed to import and visualize data, as well as build analytical models that find application in the field of economics.

Among the most popular Python libraries are ‘NumPy’, ‘SciPy’, and ‘Matplotlib’. ‘NumPy’ allows operating arrays, matrices, and vectors, carrying out various calculations and transformations on them. ‘SciPy’ allows using various algorithms from linear algebra and mathematical analysis spheres, as well as matrix methods, without thinking about the way they are implemented. In fact, developers get such an advanced calculator that performs complex computations with different types of data. And, finally, a few words about ‘Matplotlib’. This is a library designed to visualize digital data. It works upon the principles similar to those well-known MATLAB uses.

In the Java language, you can use libraries such as Java Statistical Analysis Tool (‘JSAT’), Java Machine Learning Library (‘Java-ML’), and Retina Library. ‘JSAT’ is an indispensable tool for creating solutions based on machine learning technologies. ‘Java-ML’ is a full-fledged API that implements all sorts of data mining and data analysis algorithms. In turn, the use of Retina Library will be useful in those cases when there is a need to process large amounts of data presented in a string format.

As for Scala, you definitely need to pay attention to such libraries as Breeze, Vegas, and Epic. Breeze is a library that has borrowed all the best from Python’s ‘NumPy’ and everything that MATLAB has to offer. As a result, you can process matrices, vectors, and even digital signals (here you will be helped by tools from mathematical analysis and linear algebra). Vegas is a very good solution for visualization and data analytics. And, you will need Epic for prediction (which is an integral part of projects based on machine learning).

Conclusion

Choosing the best programming language to use for developing the software in the field of data science, keep in mind the previous experience of your team (for example, if there is the need to learn the language as quickly as possible, and begin to develop soon, Python would be the best choice), and further prospects for the development of the project (in particular, if it is necessary to implement the processes of collecting and presenting large amounts of statistical data, R would be indispensable). Anyway, answering unequivocally which of the above languages is better for data science is hard. Each has its own pros and cons, and there are quite a number of factors to account for.

If you have an idea for implementing data science software, we will gladly join the project or aid in choosing the right languages and tools. Just contact us today to discuss the nuances of the cooperation.