Languages

This chapter provides an introduction to popular languages for analytics.

Languages in Analytics

Data analysts often work with a variety of languages, which can be broadly categorized into programming languages and markup languages.

Programming languages are further classified into compiled and scripting languages.

Compiled languages, such as Go, Rust, Java, and C# (“C-sharp”) require a separate compilation step to convert source code into machine-readable code, resulting in faster execution times and better performance optimization.

Scripting languages, such as Python, R, and JavaScript, are interpreted at runtime, providing more flexibility and ease of use, making them popular choices for data analysis tasks.

Markup languages, like Markdown, HTML, and CSS, are used to structure and present data, rather than performing computations.

Data analysts often use markup languages to store, exchange, and visualize data, in conjunction with programming languages for data manipulation and analysis.

Familiarity with various languages across these categories enables data analysts to effectively handle diverse data sources, perform complex analyses, and communicate results in a clear, accessible manner.

In alphabetical order, some of the languages you may encounter include the following.

CSS

CSS (Cascading Style Sheets) is a stylesheet language used for describing the look and formatting of a document or web page written in HTML. While not directly related to data analytics, it’s essential for creating visually appealing dashboards and reports.

Go

Go is a statically typed, compiled language with strong support for concurrent programming. While not as popular for data analytics as Python or R, Go is gaining traction for developing high-performance data processing tools.

HTML

HTML (Hypertext Markup Language) is the standard markup language used to create web pages. It is useful for structuring and formatting web content, including data visualizations and interactive analytics applications.

JavaScript

JavaScript is a widely-used programming language that enables interactivity and dynamic content on the web. In data analytics, JavaScript is commonly used with libraries like D3.js to create interactive visualizations and web-based applications.

Julia

Julia is a high-level, high-performance programming language for technical computing. It is gaining popularity in data analytics due to its speed, ease of use, and extensive library ecosystem, including packages for data manipulation, statistical analysis, and machine learning. It can be used in Jupyter notebooks along with Python.

LaTeX

LaTeX (“la-TECH”) is a markup language used for creating professional-looking documents, including academic papers, capstone reports, theses, and presentations. It is widely used in the scientific and technical communities due to its ability to handle complex equations and symbols with ease.

⭐ Markdown ⭐

Markdown is a lightweight markup language used to create formatted text documents. While not specific to data analytics, it is commonly used to document code, write README files, and create reports in a simple and human-readable format. It is commonly used in Jupyter notebooks along with Python.

⭐ PowerShell ⭐

PowerShell is a powerful scripting language and shell designed for automating tasks and managing configurations in Windows environments. While not commonly used for data analytics, it can be employed for data extraction, transformation, and automation tasks.

⭐ Python ⭐

Python is a popular programming language for data science and machine learning. It offers extensive libraries and tools for data analysis, visualization, and machine learning, making it an excellent choice for data analytics tasks.

R

R is a programming language and software environment for statistical computing and graphics. It is widely used in data analytics for statistical analysis, data manipulation, and visualization. R can be used in Jupyter notebooks along with Python.

Rust

Rust is a systems programming language focused on safety, concurrency, and performance. While not as widely used for data analytics, it can be employed for building high-performance data processing tools and libraries.

⭐ SQL ⭐

SQL is a domain-specific programming language used to manage and manipulate relational databases.

Typst

Typst is a new typesetting option that aims to simplify the document creation process. It provides an intuitive markup language for formatting text, with support for mathematical equations, tables, and figures. It can be compiled into various document formats, including PDF and HTML.