Top 5 Programming Languages for Data Analysts

Considering a career in data analytics? Learn about five of the most popular programming languages in 2021: Python, SQL, R, JavaScript, and Scala. Discover how hands-on programming classes can help you master these skills.

Key Takeaways

• Python, SQL, R, JavaScript, and Scala are five of the most popular programming languages for Data Analysts in 2021.

• Python is known for its easy-to-use syntax and extensive libraries, making it ideal for tasks such as data collection, analysis, modeling, and visualization.

• SQL is an industry-standard database language essential for working with structured data.

• R is a powerful scripting language often used for statistical computing and creating high-quality plots.

• JavaScript is well-suited for data visualizations and interactive capabilities.

• Scala combines functional and object-oriented programming, making it ideal for handling large datasets.

Python

Python has been around for more than three decades and remains one of the most popular programming languages in the world. It’s easy for novice programmers to learn and has a variety of applications in fields like machine learning, AI, and deep learning. Python’s data science libraries, such as Matplotlib, NumPy, Keras, and Pandas, allow it to perform tasks in many tech-related fields.

Data Analysts use Python for tasks like collecting data, analyzing it, modeling it, and creating visualizations. Python is very useful for data analytics due to its wide range of open-source tools for machine learning and data visualization.

Benefits of using Python:

• Intuitive syntax that is easy to learn and use

• Strong support community

• Free and open-source

• Extensive libraries

One of the only drawbacks is Python’s speed, as it performs computations slower than other languages.

SQL

SQL is the industry-standard database language. For those looking to become Data Analysts, it’s the first language to learn. Both Data Analysts and Data Scientists rely on SQL to work with structured data because it provides access to statistics and data. Since SQL is a non-procedural language, traditional programming logic isn't necessary to work with it.

Benefits of using SQL:

• Easy to learn

• Standardized and user-friendly coding language

• Fast querying

• Portability: usable on laptops, servers, and PCs independent of any platform

Despite its many benefits, SQL has a few drawbacks. Interfacing with an SQL database is more complex than adding a few lines of code. Some SQL versions can also be relatively expensive.

R

R is becoming increasingly popular for those working with big data. This powerful scripting language is often used for statistical computing and graphics. It can handle complex datasets, making it a go-to language for anyone in data analytics or data science. R is seen as more approachable than Python for non-developers, as users can create statistical models and compelling visualizations with only a few lines of code.

Benefits of using R:

• Ease of producing publication-quality plots

• Highly extensible language

• Supportive community

• Open-source

A downside of R is its security issues. Since R doesn’t have basic security, it can't be embedded into a web app.

JavaScript

JavaScript is an object-oriented programming language that transforms static HTML websites into interactive sites. It runs on the browser and can update content, control multimedia, animate images, and validate form data. JavaScript is perfect for creating games, designing mobile and web apps, and constructing web servers.

JavaScript is well-suited for data visualizations because of its ability to specify page behavior. D3.js, a JavaScript library, is one of the most versatile visualization libraries and can be used to create stunning, interactive visualizations.

Benefits of using JavaScript:

• Interactive capabilities

• Rich interfaces

• Regular updates

• Prompt feedback to visitors

• Versatility through Node.js servers

Drawbacks of JavaScript include security concerns since code runs on the client-side, making it vulnerable to malicious purposes. Different browsers may also interpret JavaScript code differently.

Scala

Scala is a high-level programming language that combines functional and object-oriented programming. This concise language is ideal for working with large datasets, making it popular among Data Scientists and Data Analytics. Scala is compiled with Java bytecode and runs on a Java virtual machine, making it interoperable with Java. It can be used with Spark to handle massive data sets.

Scala is commonly used for tasks like parallel batch processing, creating web applications, and using Apache Spark to analyze data.

Benefits of using Scala:

• Easy to learn for those with Java knowledge

• Highly scalable and functional

• Expressive typing system ensures consistent and secure statistical abstraction

• Capable of concurrent and synchronized processing

• More readable and concise code

Hands-On Programming Classes at Vtricks Technologies

Vtricks Technologies offers a variety of programming bootcamps for data analytics. Courses cover topics like Python, JavaScript, data science, and more. These bootcamps provide a rigorous exploration of the programming languages most commonly used for data analytics. Vtricks Technologies' bootcamps offer small class sizes and 1-on-1 mentoring.

In addition to in-person classes, Vtricks Technologies provides over 200 live online programming courses. These interactive classes are taught in real-time, giving learners access to instructors who provide feedback and answer questions. 

Ready to start your data analytics journey? Choose Vtricks Technologies and master the skills you need for a successful career in data analysis.