Top 5 Programming Languages For Data Analysis

In today’s world, data is everywhere. The sheer scale of the ready availability of such a huge amount of data brought the invention of a new term to explain it “Big Data”. However, just collecting and holding that data does not matter. Rather it is what you can do with that data that matters. Organisations are turning to data analysis to draw new insights from this data to see what works and what does not. Learning how to manipulate this data is more important now than ever before.

What is Data Analysis?

Data analysis is the process of inspecting, cleansing, transforming, and modelling data to discover useful information to support decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under various names in different business, science, and social science domains.

Top 5 Programming Languages for Data Analysis

The best programming languages for data analysis to learn in 2023 are as follows 

1. Python
2. R
3. SQL
4. JavaScript
5. Julia

1. Python

Python is a versatile language that you can use for data analysis. It has many features that make it ideal for this purpose, including support for libraries and tools that can help you with data wrangling, analysis, and visualisation. Python is also relatively easy to learn, making it a good choice if you're just coding. It is a high-level, general-purpose language that is easy to learn and use. 

Python is well-suited for data analysis due to its wide range of libraries and tools, such as 

• pandas 
• NumPy
• SciPy
• sci-kit-learn

These libraries provide powerful tools for data manipulation, visualisation, and machine learning. Python proficiency will be at the top of the required skill set in data science for at least the next five years. You can strike gold in the industry if you know Python and have a strong aptitude for quantitative reasoning and experimental analysis.

2. R

R is a popular programming language for data analysis and statistical computing. It is a free and open-source software environment with a wide range of packages for statistical analysis, machine learning, and graphical display of data. R is available for Windows, Mac, and Linux platforms.

Data analysts use R because it provides statistical packages for quantitative applications such as,

• Neural networks
• Phylogenetics
• Advanced plotting 
• Nonlinear regression 

3. SQL

SQL is a powerful programming language enabling analysts to manipulate and query data easily. While the syntax can be daunting for newcomers, many resources are available to help get started. SQL is an invaluable tool for data analysis, and learning even the basics can be extremely helpful in understanding and wrangling data. Enroll in Skill-Lync’s SQL for data science course to get certified.

SQL is used for various purposes including,

• Create and manage databases
• Query and manipulate data
• Create reports, analyse trends, and identify correlations. 
• Create data visualisations and dashboards. 
• It is also used to create data warehouses and data marts. 

4. JavaScript

JavaScript is an object-oriented programming language that, by the 2000s, was primarily used in front-end development to create interactive web pages. However, with the introduction of ReactJS, AngularJS, VueJS, NodeJS, and many other frameworks in the 2010s, it has evolved significantly. As a result, it has earned a reputation for creating both the front-end and back-end of websites, frequently using MEAN and MERN stacks.

Because aspiring data scientists can access models and algorithms in the web browser, JavaScript is simple to use. Users can also create interactive data visualisations from datasets on a web-based dashboard.

5. Julia

Julia is a high-level, high-performance dynamic programming language designed specifically for scientific computing. It has a syntax that is familiar to users of other technical computing environments such as MATLAB and R, but also has the ability to easily interface with code written in C, Fortran, or Python. This makes it possible to write high-performance numerical algorithms in Julia without sacrificing the ease of use or flexibility characteristic of these other languages. 

Because of its design goals, Julia is particularly well suited for scientific and numerical computing. It supports complex numbers, linear algebra operations, and random number generation. 

In addition, there are over 100 packages available for use with Julia, which add functionality for,

• Data visualisation
• Machine Learning (ML)
• Digital Signal Processing (DSP) etc.

The combination of performance, flexibility and ease of use make Julia an excellent choice for data analysis.

Tips to Become a Data Analyst

Tip 1: Degree and Domain Knowledge

You must have a degree in any relevant field and domain knowledge in the area in which you deal.

Tip 2: Programming expertise

You should be familiar with programming languages such as Python, R, and JavaScript. Writing programmes will assist you in resolving complex problems.

Tip 3: Data Analysis Tool Knowledge

You should have prior experience working with databases and data analysis software. To analyse trends and drive accurate insights, you should be proficient in writing SQL queries and procedures and be familiar with Microsoft Excel, Matlab, and IBM SPSS.

Tip 4: Data Visualisation Tool Knowledge

A data analyst must be able to generate various business reports using various data visualisation tools. You should be familiar with Tableau, QlikView, and Power BI tools.

Conclusion

You must learn the appropriate programming language for a smooth and successful career as a data scientist. Skill-Lync helps you to learn more about data analysis by providing you with a wealth of resources that you can use to improve your understanding of the subject. We offer courses like SQL for Data Science, PG Program in Data Analytics and Data Science, and Business Analyst Fundamentals for Beginners, which cover everything from the basics to more advanced topics. Book a free demo session to know more!