Is Julia a Hype in Data Science?

In the dynamic field of data science, data scientists need to keep a track of the algorithmic development, languages, technology stack used, and databases. Julia is one such major development in the programming language, which earned considerable attention recently due to its unparalleled speed, and user-friendliness.

In this article, we shall deep dive into Julia, its growing significance, and its applications due to which it is worth learning this language in the Data Science career.

What is Julia?

A breakthrough in data science programming languages – Julia is a general-purpose and high-level programming language that every data science professional can use for scientific computing.

Jeff Bezanson, Viral B. Shah, and Stefan Karpinski, the developers of Julia, came from various backgrounds and were keen on finding a language that unified the power of all other existing languages. This gave birth to Julia, a compiled language. The first version of Julia was released in 2012, and came live as an open source, with the speed of C, easy-to-learn, statistic-friendly, and general-purpose language.

Why is Julia the favorite of Data Science Professionals?

Julia is the favorite among the crowd– Why? It has been downloaded over 35 million times as of January 1, 2022; also 3 times more often in 2021 compared to the previous three years taken together, according to JuliaHub. 

• Julia is the favorite of the Machine Learning and computation world due to many reasons: 
• It is open source and free: MIT holds the license and code hosting on Git which helps everyone to view or make necessary changes. 
• Parallel processing ease: It augmentps parallel processing and offers primitives for parallel computing. 
• Great execution speed: It matches the efficiency of FORTRAN and C, making it one of the fastest languages. 
• Compatibility with Jupyter: Julia is compatible with Jupyter and similar IDEs like Vim, VS Code, etc.
• Customized for Machine Learning: Julia supports equations and matrices, it doesn’t demand any external packages like Python or NumPy.

Julia is compiled with the LLVM compiler framework to implement faster runtime efficiency through JIT (Just-In-Time) compilation. It has an interactive command line similar to Python where you can make code bits or one-off scripts with few key presses.

Julia can call libraries like C, FORTRAN, Java, R, etc. You can also call Julia from other languages through embedding APIs. It has the easiest syntax and a friendly debugging feature. Julia also supports metaprogramming, can rewrite the code, and create other programs which remind you of programming languages like Lisp. It has capabilities like parallel computing at every level: multi-threading, instruction-level, Distributed Computing, and GPU computing.

Businesses that make use of Julia the most

Many professionals use Julia due to its ease of use and stunning performance. These beginner-friendly and versatile languages have many applications in the industry; however, let’s take a glance at the most significant applications of Julia.

1. Data Science
   Every data science professional who uses Julia focuses mainly on developing predictive models. Also, the language gives the privilege to use Machine learning packages of TensorFlow and Scikit Learn in Python. Since the language is easy to debug and easy to learn, many data scientists prefer it when compared to Python.
2. Web Development
   The Genie.jl framework of Julia has many similarities compared to Python. You can use this to implement full-stack web development. 
   
   If you are new to a data science career and want to build a comprehensive web application, you might find Python too slow yet complex language. In this circumstance, you can use Julia, a relatively easy-to-learn language. Dash.jl, similar to the Python Dash library, helps you build dashboard applications that support data visualization.
3. Data Analysis
   Data professionals need to understand and extract relevant insights from the available data. Julia helps users with libraries designed to manage data visualization and data analysis. Julia incorporates DataFrames.jl, the package that lets you manipulate data in tabular format. This has an application equal to Pandas, the package of Python. Julia is 20 times faster compared to Pandas.
   
   Julia also helps users in data visualization with Plots.jl, which is like Matplotlib of Python. You can also use Julia to build charts such as bar charts, heatmaps, violin charts, dot plots, and bubble plots.
4. Scientific Computing
   Julia is useful in many fields like astronomy, chemistry, physics, and bioinformatics. Professionals who learn Julia can build an amazing Data Science career with expertise in statistical analysis of heaps of data.
   
   Some of the scientific packages you can use to solve business-specific concerns are QuantumLab, Molly, and Biosequences packages. Based on reviews, Julia’s scientific libraries are more extensive than that of Python with faster computing properties.

Should you learn Julia for Data Science?

Data scientists, developers, economists, and analysts can use Julia to manage daily work. Julia is incredibly fast and has a great array of scientific libraries, making it the perfect alternative to R or Python. It has increased in popularity in recent years, and the tech giants like IBM, Apple, Amazon, Microsoft, and Google, already use Julia.

Though Julia is not best-documented like R or Python, experts suggest Julia will be the revolution of data science. They recommend learning Julia now and adding it to your Data Science certifications since it is going to be the next-big-thing in upcoming years.

However, some other data scientists throw a conservative opinion on Julia, with the opinion to learn Python and R first, since they are the most used programming languages across the world.

It is always good to learn a new language, which has scope to rebuild the future. Learning Julia helps you to streamline Machine Learning workflow and keep your portfolio unique which gives you a competitive edge in the job market.

Ready to fuel your Data Science career with Julia?

Though Julia was developed for scientific computing, once it went live, it witnessed many applications in various industries.  The TIOBE index for Julia is 0.64%, which means every 200th programming concerned query is about Julia.

NASA uses it in the migration patterns of humans, plants, and animals to record their response to changing climate. MIT uses Julia in robots, to enable their activities like walking on uneven or difficult terrains.

Julia is on the rise, but it still has a few miles to travel in terms of developing documentation, packages, community support, and libraries, however, the language makes a lot of milestones in the field of data science.

It minimizes the time-to-market in the places where the greatest challenge is the code execution time. Julia is ideal to use in applications where you use simple Machine Learning algorithms. It evolves steadily which makes it one of the best languages ever in Data Science. To fuel your career in data science with Julia and much-needed programming languages, make sure you learn them in your Data Science certifications.