Python is the most popular programming language for data analysis due to its simplicity and a vast ecosystem of libraries and tools.
SQL (Structured Query Language) is a programming language used to query and manage relational databases. To perform analysis, data analysts frequently need to extract data from databases using SQL.
Julia is a high-performance language designed for scientific computation and data science. It is known for its speed and is gaining prominence in the community of data analysts.
R is specifically designed for statistical analysis and data visualization. It has a rich set of packages like ggplot2, dplyr, and tidyr for data manipulation and visualization. RStudio is a popular integrated development environment (IDE) for R
Scala is frequently used with Apache Spark, a framework for large data processing. If you deal with large-scale data analysis or distributed computation, it can be beneficial to learn Scala alongside Spark.
Java is occasionally used in data engineering activities, particularly when working with large data platforms such as Hadoop and Kafka. In such circumstances, knowing Java can be useful.
The Statistical Analysis System (SAS) is a collection of programs used for statistical analysis, data mining, and business intelligence. It finds extensive usage in the medical and financial sectors.
JavaScript is valuable for web-based data visualization and interactive dashboards. Libraries like D3.js and Plotly.js are commonly used for this purpose.
Data analysis is a common use for MATLAB in academic and research areas, especially in engineering and physics. It gives you powerful tools for working with numbers and showing facts.