Find out what are the top coding languages you should learn for a career in data analytics.
If you’re new to data analysis, and you’re not sure which is the right programming language to start with, you’re in the right place.
This article is intended for those interested in a career in Data Analytics / Business Intelligence.
Best Data Analysis Programming Languages to Learn
1. Python
Python is the go-to language for data analysts, and over the years it became the most popular coding language for data analysts and data scientists.
Fast, powerful, and beautiful syntax
As a powerful general-purpose language, dynamic and open-source, it comes with the perfect balance of flexibility, performance, speed, and learning curve.
Python is the creation of Guido van Rossum, and it was officially released in 1991, with the idea behind it be able to process complex concepts with shorter and fewer lines of code.
Easy to learn
Python is one of the easiest programming languages to learn. Unlike other technologies, it is easy to pick up even for those who have never coded before. Its syntax is simple, clean, intuitive, and highly readable.
Plenty of libraries
Another pro is that Python is mature. And it comes with a whopping number of 137000 libraries that play a vital role in machine learning, data science, data manipulation, visualization, and more.
Huge community
What’s also a plus, is that it has a large community behind it, that actively contributes to its continuous improvement. Whatever question you may have or problem you encounter, you’ll 100 percent easily find your answers right away.
Popular Python libraries for Data Analysis include Pandas, Numpy, Matplotlib, Seaborn, Plotly, PyBrain.
- Learning Curve: Easy to learn.
- Libraries: 137.000
- Cost: Free
- Beginner Friendly: Yes
- Job Market Demand: High
2. R
Another favorite top runner programming language for data analysis is R. Some prefer it better than Python.
Built mainly for statistical computing
It’s a very powerful language created by statisticians who wanted a way to make statistical analysis easier.
R is fantastic for exploratory data analysis, data cleaning, and data wrangling, and known as the best tool for beautiful charts and visualizations.
Is it easy to learn? R is considered to be relatively easy to learn. For those having a statistics background, R will be much easier to learn than Python.
Excellent for Data Visualization
Where R shines is at data visualization. With incredible packages like ggplot2, Lattice, Plotly, you can create beautiful visualizations that outperform the ones made with Seaborn in Python, for example.
You can even create interactive vizes and applications, which is amazing in the BI world, using the R tool called SHINY.
A huge plus is its universal IDE, named Rstudio, which helps you to keep your work clean and organized.
- Ease of Learning: Generally, R is considered to be difficult to learn. But for those with statistics background, it’s very easy to learn.
- Libraries: ~17.000
- Cost: Free
- Beginner Friendly: Yes, but not recommended as a first language.
- Job Market Demand: High