programming languages for data analysis

5 Best Data Analysis Programming Languages in 2022 (Trending Now)

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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 coding language for data analysis

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

r programming for data

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

3. SQL

sql-database-learning

SQL is the highest in-demand skill for data analysis.

SQL stands for structured query language and it is used to communicate with databases and data warehouses. It only talks with relational databases with tabular schemas, so basically with rows and columns, in order to easily pull, edit, add or delete data.

Asked in every job description

In every job description for Data Analysts, SQL appears as a strong requirement. So it is very important to take the time to learn it and build a strong foundation on databases, that will play a huge role when you start to pick up harder coding languages like Python or R.

  • Learning Curve: Very Easy to Learn
  • Cost: Free
  • Beginner Friendly: Yes
  • Job Market Demand: High

4. Julia

julia for data analysis

Julia is a general-purpose programming language focusing on scientific computing.

Created for Data Science and ML

It was built specifically for data science and machine learning from the desire to make something better than exists. 

It is high-level and dynamic, free and open-source, with a math-friendly syntax, easy to write and understand.

Faster than Python

It’s JIT (just-in-time) compiled, right before runtime, making it very fast being able to match, at its best, C level.

Data Analysis Packages on the Rise

It comes with vital packages for data wrangling, analysis, and visualization. DataFrames.jl package, for example, was built to be similar to pandas in Python or dplyr in R, perfect for data manipulation, missing data functionalities, sorting, pivoting data, column manipulation, join functions, split-apply-combine, reshaping data. 

Can call external libraries

What’s very interesting about Julia, is that it has the ability to call libraries from Python, C, and Fortran.

If you’re familiar with Python is pretty straightforward to learn. The syntax is clean and is easy to learn, however, we don’t recommend it to be your first programming language.

  • Ease of Learning: Easy to pick up, hard to master.
  • Packages: ~4.000
  • Cost: Free
  • Beginner Friendly: Yes
  • Job Market Demand: Low, but on the rise.

5. SAS

sas for big data analysis

SAS is one of the oldest analytics solutions, which in the past was a monopoly in analytics.

Is SAS a dying language?

With the rise of Python and R, SAS lost ground and many consider it today as a dying language. The new generation doesn’t want to use SAS, and as time will pass and the new generation takes leading positions, many assume SAS will be required less and less.

High usage in big enterprises

However, SAS is still used today in big industries: pharmaceutical, health, finance, and government. If you want to work in these kinds of industries, then SAS might be a good idea to learn. 

SAS is proprietary software, not open-source, and not suitable for personal use. Plus, it’s very expensive, therefore the use in the large corporate world..

The new generation not using SAS, and as time passes by, many consider even big companies will adopt new solutions.

SAS uses an intuitive GUI + PROC SQL,  and it’s very easy to learn especially for ones who already know SQL

  • Ease of Learning: Easy to learn.
  • Cost: Expensive
  • Beginner Friendly: Yes
  • Job Market Demand: High demand in health, insurance, banking, finance, government industries.

Conclusion

First, you should choose either Python or R, stick to it, and become an expert.

But before that, you should definitely learn SQL to get a good foundation of understanding databases in general. SQL is a must-know skill that’s very easy to learn.

Julia is the new shiny thing, built for DS, but it should not be your first and only choice, better to learn it after you mastered Python or R.

Learn SAS only if you’re interested to work in big industries.

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