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SAS is one of the oldest analytics solutions, which in the past was a monopoly in analytics. Job Market Demand: Low, but on the rise.Ease of Learning: Easy to pick up, hard to master.The syntax is clean and is easy to learn, however, we don’t recommend it to be your first programming language. If you’re familiar with Python is pretty straightforward to learn. What’s very interesting about Julia, is that it has the ability to call libraries from Python, C, and Fortran. 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. It comes with vital packages for data wrangling, analysis, and visualization.
It’s JIT (just-in-time) compiled, right before runtime, making it very fast being able to match, at its best, C level.
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It is high-level and dynamic, free and open-source, with a math-friendly syntax, easy to write and understand. It was built specifically for data science and machine learning from the desire to make something better than exists. Julia is a general-purpose programming language focusing on scientific computing. Beginner Friendly: Yes, but not recommended as a first language.But for those with statistics background, it’s very easy to learn.
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You can even create interactive vizes and applications, which is amazing in the BI world, using the R tool called SHINY.Ī huge plus is its universal IDE, named Rstudio, which helps you to keep your work clean and organized. With incredible packages like ggplot2, Lattice, Plotly, you can create beautiful visualizations that outperform the ones made with Seaborn in Python, for example. For those having a statistics background, R will be much easier to learn than Python. Is it easy to learn? R is considered to be relatively easy to learn. R is fantastic for exploratory data analysis, data cleaning, and data wrangling, and known as the best tool for beautiful charts and visualizations. It’s a very powerful language created by statisticians who wanted a way to make statistical analysis easier. Popular Python libraries for Data Analysis include Pandas, Numpy, Matplotlib, Seaborn, Plotly, PyBrain.Īnother favorite top runner programming language for data analysis is R. Whatever question you may have or problem you encounter, you’ll 100 percent easily find your answers right away. What’s also a plus, is that it has a large community behind it, that actively contributes to its continuous improvement. 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.
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Its syntax is simple, clean, intuitive, and highly readable.Īnother pro is that Python is mature. Unlike other technologies, it is easy to pick up even for those who have never coded before. Python is one of the easiest programming languages to learn. 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.
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.Īs a powerful general-purpose language, dynamic and open-source, it comes with the perfect balance of flexibility, performance, speed, and learning curve.