News / Blog: Knowledge Base

Which programming languages ​​are relevant for data scientists?

Several programming languages are relevant for data scientists, enabling them to analyze data, build models, and extract insights. The choice of programming language often depends on project-specific requirements and the preferences of the data scientist. Here are some of the relevant programming languages for data science:

Python:

Python is highly popular in the data science community. Extensive libraries such as NumPy, pandas, Matplotlib, Seaborn, Scikit-learn, and TensorFlow make Python powerful for data analysis, machine learning, and deep learning.

R:

R is specifically designed for statistical analysis and data visualization. It offers a wide range of packages used by statisticians and data scientists, including ggplot2, dplyr, and tidyr.

SQL:

SQL (Structured Query Language) is used for database queries to extract, transform, and analyze data. Fundamental for working with relational databases and large datasets.

Java:

Java is typically used for big data processing and analysis. Hadoop, a key big data technology, is written in Java.

Scala:

Scala is a functional programming language often used with Apache Spark. Spark, a powerful big data processing platform, provides APIs in Scala, Java, Python, and R.

Julia:

Julia is gaining popularity due to its speed and user-friendliness. Particularly suitable for numerical and scientific computations.

MATLAB:

MATLAB is widely used in engineering and scientific circles. It offers powerful features for numerical computations and data visualization.

The choice of programming language heavily depends on individual project requirements and the data scientist's preferences. In practice, many data scientists combine multiple languages to achieve the best results for their specific tasks.