Data science enables companies to extract valuable insights from the data they collect and use them profitably. Thus, the field is often referred to as Key to digital transformation seen. For example, there are fields of application such as predictive maintenance or fraud detection. In this respect, the demand for experts and programmers for data science is also increasing.
In our article, we present the most important programming languages. These can be useful for typical data science work areas such as statistical analysis, data manipulation, visual processing or data access.
Python: simple syntax, large library variety
This multi-paradigmatic, dynamic programming language is used in teaching for web application frameworks and partly for games. Google and YouTube, for example, are partly based on Python. The data science language developed in 1991 Offers helpful mathematical libraries that assist with data analysis.
One advantage is Python's large worldwide user community, where people help each other with specific problems. Roughly speaking, there are two Ways to use Python as a Data Scientist to use. On the one hand, scripts can be written and executed. On the other hand, it is possible to use a shell like REPL to check Python commands quickly and easily. REPL stands for:
- Read: Record the user's input
- Eval: Evaluation of the input
- Print: Provide output
- Loop: Repeat
R: specialised in statistics and data science
R is a language that appeared in 1993 and was initially aimed primarily at statisticians. Therefore it offers many useful functions to read in data or calculate statistics and regressions or plot. However, processing large amounts of data is faster with Python than with R. Nevertheless, R is sometimes used as the Develop machine learning models used.
There is less choice of software for R if you want to use an integrated development environment for programming. However, many users are very satisfied with the existing tools, especially since the programming language R has been validated by the Food and Drug Administration for medical purposes. This means it can be used for clinical studies. As an open source language, R can also be adapted for individual purposes.
C++ in the field of Data Science and Machine Learning
Increasingly, developers are discovering old Programming languages like C++ (from 1979) or C (published in 1972) for data science applications. The fact that the syntax of C is the basis of later languages helps many younger developers to learn it. The C++ programming language was used, for example, to develop MongoDB, MapReduce and many other data science applications. Deep Learning Libraries implemented.
The programming language is considered an efficient tool for creating rapidly scalable Data Science and Big Data Libraries. The reason for this is the good memory management and other performance features of C++, such as the very high speed of data compilation.
SQL: the most important data science language for database use
In order to be able to analyse data, one often has to extract it from databases. One programming language used for this purpose is SQL. It first came onto the market as early as 1979. It is a database language for defining data structures. As Data Scientist you should be proficient in SQL, as almost all common database systems use this programming language.
SQL is considered the standard language for relational databases and is a frequently used interface for Big Data platforms. It is used to create, extract and manipulate data from systems such as MySQL, Oracle, SQL Server or Postgre. Compared to other programming languages, the syntax of SQL is relatively simple, as it is semantically based on English colloquial language.
Java: a bonus in the portfolio of a Data Scientist
As One of the most important programming languages in general is Java, which was developed in 1991 and is used today for Android apps, web server applications, Hadoop and enterprise desktop applications. As a data science language, some developers use Java complementary to R or Python, for example, to write special programming.
Java has potential as a programming language - depending on the concrete development environment and overall structure of the software project - for the following areas:
- Data visualisation,
- Textual analyses,
- Deep Learning,
- Data cleansing,
- Statistical analyses,
- Data import and export
- and Machine Learning.
It is also relevant that many companies already use infrastructure based on Java. For this reason, it sometimes makes sense to create a prototype in R or Python, which is then rewritten in Java.
Other programming languages in the field of data science
In addition to widely used data science languages such as Python, some other programming languages are popular among data analysts, especially regionally:
Scala, for example, is popular in Japan, among other countries. This programming language, developed in 2003, was initially intended to help with certain problems with Java. Today, it is also used in the areas of Big Data and Machine Learning.
A data science language that is used for fast numerical analyses and dealing with matrices is Julia. It is considered a suitable language for mathematical concepts in the field of data science. In addition, the interface can be easily embedded in other programmes.
For advanced data analysis and complex statistical operations, some large companies with corresponding budgets use SAS. This language with its associated development environment is considered very reliable in the field of enterprise analytics, but also difficult to learn.
If intensive mathematical operations are necessary, MATLAB can also become a data science language. This is one of the programming languages that offers, among other things, graphics for data visualisation and tools for creating individual plots. Similar to the programming language Octave, which is also popular with some data scientists, MATLAB has a large number of libraries for linear algebra, statistics and Fourier analyses.
A programming language that has a lot in common with Python, but is currently less used, is Perl. This versatile scripting language is used primarily in bioinformatics, finance and statistical analysis. Modern Perl versions can handle large amounts of data better than older versions. This is why Boeing and Siemens, for example, used Perl for parts of their data science tasks.
Another data science programming language is Haskell. It is supposed to be fast and safe when it comes to mathematical concepts such as abstraction, which are necessary for some finance-oriented fields. However, the number of developers using Haskell for machine learning or in combination with other data science programming languages is quite small. This is because the language is difficult to learn.
Use of programming languages worldwide
There are regional differences in the use of the respective data science language. For example, experience with Python in 76 per cent of data science job ads on LinkedIn as a necessary qualification for the USA. Accordingly, the majority of participants in a Survey on working with Python in the USA, namely 16 percent. Indians follow with 11 per cent and German programmers with seven per cent.
If we take a closer look at the top data science languages, we learn, for example, that Java by 53 per cent of South Korean developers and 47 per cent of Chinese developers.but is only used by 33 per cent of German software experts. This may also be related to the fact that German programmers feel less influence in their position when using Java than with other programming languages.
Few people see the programming language R as their main language. Most use it in parallel with Python and in combination with databases such as PostgreSQL, MongoDB and SQLite. Probably due to its age, C++ is currently less popular. After all, still 23 per cent of Indian developers see it as their main language. C++ is also most often used in combination with Python.
Among all the programming languages that for Data Science, Python is currently the most important. Depending on the region, corporate philosophy and personal preferences, Java, R, SQL, C++ or lesser-known languages are also used. Particularly when it comes to complex mathematical tasks, special programming languages are also used that have advanced statistics or algebra functions.
Due to this diversity and dynamism, it becomes clear that it is of great importance in the AI field to always stay up to date. It is not enough to learn a programming language and apply it to all future projects. Data science is an extensive field that is exciting and varied due to its fast pace of development. This is also the attraction for experts and programmers in this field.