Top 5 programming languages for data experts

4 min readMay 30, 2022

So you want to be or you already are a data scientist or engineer. (There are significant between the two fields despite the overlaps). These fields rely heavily on processing and manipulating data to make sense of them and inform optimal decisions that a team (or the company as a whole) must make. We have compiled (no pun intended) a list of five programming languages that will serve you well in your destination into the realm of big data. But first, we must first mention Excel, the software from Microsoft, which is a great starting point for any form of data analysis. That being mentioned, let’s delve into the rest:

  1. Python

Python is a popular language not only in the data world but among programmers in general. is an open-source, high-level, general-purpose programming language. It includes various programming paradigms such as structured, object-oriented, and functional programming. Its extensive libraries make it possible to perform process data robustly. Some of its libraries include “pandas”, for data analysis; “Matplotlib”, for data visualization; “scikit-learn”, for machine learning algorithms; TensorFlow, for machine and deep learning algorithms; and Keras, for neural networks.

2. R

R is highly known for statistical analysis and data visualization, commonly used in academe. As part of its libraries, it has “Tidyverse”which is a collection of R packages widely used by data analysts. “dplyr” and “ggplot2” are featured in the Tidyverse collection. They are used for data manipulation and data visualization respectively. R also allows the integration of third-party interfaces thereby expanding upon its functionality.

3. Structured Query Language (SQL)

SQL is a domain-specific language that enables users to manipulate data from a large database by entering, changing, and separating information. Processing data in such manners explains why SQL is a necessary language to pick up as a data expert. In addition, it is known for being relatively easy to learn as opposed to the languages aforementioned.

4. Java

Java is a high-level, class-based, object-oriented, and one of the most popular programming languages known. It is reputable for its efficiency and usage in various software, applications, and websites. Nonetheless, it can also be used in data analysis. The virtual machine known as “Java Virtual Machines” (JVM) encompasses tools such as “Hadoop”, “Spark”, and “Scala” which are powerful tools to manipulate big data. Another reason why Java proves itself to be helpful in the data world is due to its big storage capacity of data and complex processing abilities both of which can come in handy when dealing with machine learning algorithms.

5. Julia

Julia is another language that has been growing in popularity among data analysts. It is a high-level, high-performance, dynamic language, heavily used for numerical analysis and computational studies.

Other programming languages can also be useful depending on the firm you work for and the popular trend during the time you are in. If one works in the field of web development and application, for instance, picking up JavaScript would be necessary as it will help with visualization among other things. If you are in the world of academia and research, you may find yourself using MATLAB especially if the institution endorses it. Another propriety-based programming language aside from MATLAB is SAS which is used as another statistical analysis tool that helps retrieve and report from large data. C and C++ can also be used for scalable projects and in building tools for data and statistical analysis.




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