Is Julia hard to learn?

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Table of Contents

Julia comes with excellent speed and power to perform to tackle complex and extensive codes. Also, it’s a nice choice for numerical computing. The blazing fast speed alongside Python inbuilt interpreter chooses to consider.

While Julia has an easy-to-learn syntax, writing a functional code can be challenging considering that the programming language has very few libraries to support writing codes. Good thing; Julia is developing very fast, thanks to the addition of newer classes, which are continually bringing some fundamental changes – for instance, change behaviors of some functions or changes in types.

Julia is not an object-oriented language; so, it doesn’t offer a perfectly tidy environment like Python. which means you have to ditch some conventional ways found in Python. There are no classes in Julia, meaning you have to do things as in MATLAB, replacing the need for modules such as NumPy for easier ways.
Julia is still developing just like other languages – most of the base language has been written, but there is no guarantee breaking changes won’t be made on this base code. While learning Julia is pretty easy, these changes might inject incompatibility issues like making a code non-compatible with the new releases.

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In this case, you have to go a mile further to solve such problems or write another code. Nonetheless, learning Julia can allow you to contribute to the development of the language. Besides, it offers a programmer the chance to improve their coding abilities for other programming languages – for example, you can develop packages in Julia by translating the existing Python package. This offers you a deeper understanding of both languages.

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Currently, Julia is at version v1.6.1 – this is a stable version. Besides, this version guarantees backward compatibility. At least Julia is maturing into an excellent language for numeric computing. Moreover, it features some advanced subdomains for statistics, optimizations, modeling, machine learning, etc.

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Is Julia hard to learn?
Is Julia hard to learn?

Is Julia easier than Python?

Python has been around for three decades, making it a mature language. Over the years, the developers, alongside the large Python community, have pumped through more resources in terms of packages and libraries to aid development. 

By contrast, Julia was unveiled in the last decade. Though a general-purpose language like Python, it’s much geared towards data science, machine learning, and scientific computing. The creation of Julia is aimed at bringing together the desirable features from popular languages like Ruby, C, Python, R, and MATLAB. 

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Ideally, the developers wanted to inject the proper macros like in Lisp, the dynamism of Ruby, the speed of C, familiar mathematical notation of MATLAB, statistical features of R, as general as Python, and a natural string process in Perl. That should sound like a pretty tricky language to learn – but it isn’t. Julia is straightforward to understand.

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Julia vs. Python; which one is easier to learn? Both languages are described as easy to learn, and moving from one to another should be easier. But, of course, some differences occur, which may make the learning curve different. 

The most appealing feature in Julia is the straightforward syntax. While this syntax is easy to grasp, just as Python’s syntax, it’s more powerful as well as expressive. These two languages can learn operations in parallel. Nevertheless, methods in Python require serialization and deserialization of the data to allow parallelizing between threads. This is different from Julia – they are much more refined, making it easier than Python. Again, Julia has less top-heavy parallelization syntax than Python, minimizing the threshold to its use.

However, the exciting twist is that Python boasts of a massive and active community – a community devoted to Python. since Julia is young, its community is relatively small. So, it is easier to get help in Python than in the Julia community. 

Is Julia easy to learn?

Being an open-source programming language, Julia is easy to download, install and use. Besides, using the various packages is easy – for instance, installing the kernel to make it work within Jupyter notebooks is very easy. 

Generally, picking up Julia is pretty straightforward; however, mastering the concepts can be highly challenging. Learning the Julia concepts is the best way to master Julia, but it takes time – practicing and experimenting on these concepts is the best way to learn how to write high-quality programs. 

Keep in mind the range of libraries in Julia is somewhat limited currently, and that might limit the ability of developers to write production codes. But even with these limitations, coders can write faster code executions, thanks to the easy-to-learn syntax.

As earlier stated, Julia is not an object-oriented programming language, so everything isn’t in objects. Coders, therefore, have to let go of some of the tidiness. Julia offers structures similar to MATLAB but not classes. Besides, it has an exemplary syntax for linear algebra built natively into the language. As such, it replaces the need for other modules. 

Note that Julia is not a whitespace-sensitive language; instead, it uses ending functions and loops with a keyword end. The benefit of this is that there are no scenarios of error – inconsistent use of tabs and spaces in indentation. 

Similar to Python, Julia is dynamically typed – no need to tell which variable has what type. However, Julia typing supports the enforcement of variables to be a specific type, unlike in Python. This offers two benefits; it makes the code run faster than a fully dynamic language and makes debugging easier when handling typed variables. 

Julia has support for Unicode characters as a variable. This eliminates the need to remember challenging to write variables such as x_hat or sigma.

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Is it worth it to learn, Julia?

Julia is a worthwhile language, especially if you find the performance of popular programming languages like R and Python worthwhile. Julia is tailored for data processing, but it’s relatively new to Python and R, which are advanced in that field. The popularity of Julia is steadily increasing, making it a worthwhile language. 

But is it time to fully embrace Julia? 

That depends. If you are just a beginner programmer or data scientist, Julia may not be worth your time. Instead, choose a more acceptable language like Python, as you’ll be able to get more support and assistance when needed. Nonetheless, if you are already an established data scientist or Python and/or R user needing to expand your programming skills, learning Julia for numerical computation is worthwhile. Of course, this is just advice. 

Julia differs from other programming languages for data science because, unlike them, it is compiled – it uses a Just-In-Time compiler (JIT). Below are reasons for considering Julia:

Power and speed

The central idea behind Julia is utmost performance without compromising other features, e.g., ease of use and convenience. While languages like C++ are blazing faster, they feature annoying trade-offs. But Julia brings the finest of both worlds – the efficiency of compiled languages (e.g., C++) and flexibility of interpreted languages like Python. 

Julia can call other language libraries. 

Julia allows importing libraries written in other languages such as R, Python, and Java using packages like RCall, PyCall, and JavaCall. Besides, R and Python can easily interface with Julia, JuliaCall and Julia PyJulia, respectively.

Julia is a solution for a two-language problem.

Julia is dynamic yet faster – once you’ve written it, there’s no need for rewriting. Look, coders code in high-level languages and rewrite performance-critical parts in low-level languages. 

Julia uses multiple dispatches.

Julia allows multiple dispatches, i.e., a function acts differently based on the combination of arguments it receives – for instance, you can pass a Boolean, an integer, and a string and treat each differently.

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Should I learn Julia or Python?

Julia was developed especially for machine learning and scientific calculations – that’s why it’s popular with professionals doing computing. Julia has a much better speed, ease of use, and convenience than Python. But that doesn’t mean that Python is a lacklustre programming language; in fact, Python is a top rated programming language, has a thriving community, and offers a faster start-up speed. 

But if you are venturing into the data science field, it is worth weighing the benefits and shortcomings of each programming language. Specifically, choose a convenient language for you by addressing what you need to learn and use the programming language that best suits your needs. 

Julia outperforms Python in some cases because it has features that Python doesn’t have. That makes Julia a promising language, but it is still young. 

Great Julia features are:

  • Interactivity features an interactive command-line Read Eval Print Loop (REPL) that allows easy addition of commands. 
  • Julia is compiled: it offers a faster runtime performance; it uses the LLVM framework for just-in-time compilation (JIT).
  • Simple syntax just like Python 

Can call other language libraries: Julia works with other 

  • external libraries 
  • A full-fledged debugger

Julia’s advantages are:

  • A syntax optimized for math
  • Speed
  • Automatic memory management
  • A specific design for machine learning and linear algebra
  • Has many machine learning libraries 

Python is a more mature language than Julia – It’s three decades old compared to Julia’s one decade old lifespan thus far. Its benefits include:

  • Less start-up overhead – it takes less time for a Python program to swing into action. 
  • Zero-based array indexing, unlike Julia’s [1] array 
  • Large and dedicated community
  • More third-party packages – meaning there is so much software built around Python 
  • Python is getting faster, thanks to an improved interpreter

While Julia seems all rosy, it has a long way to go before it dislodges Python. Truly, Python is a language that most coders opt for for data science. Again, many companies are using Python for existing projects. With this popularity, Python is the best bet. But that depends on the tasks ahead of you. 

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Is Julia the next significant programming language?

MIT created Julia as a programming language for coders who want an all-in-one programming language. True to this, Julia received a warm embrace, especially from the community of data science and machine learning experts. As earlier stated, Julia’s idea combines speed, usability dynamism, mathematical prowess, and statistical chops of C, Python, Ruby, MATLAB, and R, respectively.  

That should make Julia the next big thing. Ideally, Julia was created for the technological world, courtesy of its unmatched productivity, ease of use, and unmatched speed, according to Alan Edelman. Data scientists and machine learning experts usually deal with large volumes of data. These large-scale supercomputer simulations require a program with power and speed. Julia is broad – it offers capabilities to spread workloads across thousands of processing cores, making it ideal for heavy-duty computations. 

According to MIT, Julia is the only high-level language successfully used in the “petaflop club” to simulate 188 million stars, galaxies, and other astronomical objects on Cori – Cori the 10th most powerful supercomputer in the world. 

Did you know that Julia can be used to power self-driven cars and 3-D printers? Besides, Julia offers enough ability to run precision machines, risk management, and augmented reality. Researchers use Julia for a range of uses at the MIT labs, including creating the Next-Generation Airborne Collision Avoidance System (ACAS-X) for robot navigation and optimizing school bus routing. 

So, is Julia the next significant programming language? 

Currently, Julia has 700+ active open source contributors, 1900+ registered packages, and 2M+ downloads. These are impressive statistics considering Julia is less than a decade old. Nonetheless, though we are yet to see Julia hit the top-10 most popular languages, the TIOBE programming language index highlights Julia as a fast-growing language with massive adoption of Julia by developers. 

Major companies, including Aviva, Capital One, BlackRock, and Netflix, and 700+ universities and research institutions are currently using Julia. The chameleon nature of Julia caters to many different needs. 

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Is it time to learn Julia?

Julia makes for a program tailored for scientific computations, data mining, machine learning, distributed and parallel computing, and large-scale linear algebra – it has succeeded as the language that has managed to woo many professionals along the mentioned lines. 

But is it worthy for anyone to learn Julia now?

It might or might not be the time to learn Julia. Typically, the answer depends on what you intend to get from this programming language. It might be time to learn Julia if you deal in work that requires scientific computations – numeric tools and faster performance. Ideally, Julia features a wide range of libraries covering statistics, data mining, machine learning, and the various branches of science and mathematics.

However, Julia might not be the best language to learn if you primarily want to create an app to distribute to end-users without installing Julia. Again, if you are a web developer, Julia may not offer the best web building experience. Although there is a working MVC framework, it’s not yet full-fledged to provide exemplary services. 

Again, while some people have the flexibility of choosing a language to learn, some don’t. For instance, if you are employed in a company that uses Julia, you’ll indeed have to learn it. 

Researchers and scientists can pivot a new technology much faster than software developers. Software developers must wait until a programming language is popular, and Julia is on a positive note. 

Large companies like MIT, Google, NASA, NY Stock Exchange, etc., are investing in Julia – hiring Julia developers. So, being an early adopter of Julia might be a nice thing; however, you have to set your expectations accordingly. Julia is a powerful language, it’s stable, but it will need time to be mature. 

If you are passionate about programming or you are thinking something long-term, learn Julia. The language is growing, and growing with it may expose you to many programming details. 

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Learn r or Julia?

Data science interfaces and digitization processes are fast-growing. This has given rise to new tools, each tailored for a specific purpose. You can get software specific for cloud computing, infrastructure analysis, etc. If you are beginning your data science and analytics journey, you may be amazed at the plethora of programs to handle such tasks. 

You may wonder which programming language is perfect. More often, data scientists narrow down to Python and R. now; there is Julia. 

R is a popular language among statisticians and analysts across all disciplines. This free-to-use software has 14,000+ additional packages listed on the open-source package – CRAN in R. this means that you can find almost any tool regardless of the operation you intend to do. You may choose to use the free software – RStudio-Server, or the commercial software RStudio-Server-Pro. Both software options offer developers an easy way to create intuitive user interfaces that can allow several users to work in parallel on a project basis. Then, developers can conveniently publish the results with just a click. Still, many users can access this information.

Julia is a blend of several languages, so it combines productivity and accessibility of statistical languages, e.g., R, with compiled languages such as C. However, a statistical language, Julia, can be used as a universal language. Again, the speed of Julia is in the realms of C languages – this is much different from R, which is slower. And it’s because of speed that many developers are embracing Julia. 

By now, it’s clear that Julia is more general; thus can tackle many application types as it supports the general needs of application development. On the other hand, R comes with a plethora of libraries to do many things, but its scope is much lesser than Julia’s. 

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How fast is Julia?

Julia is a unique, game-changing programming language. Its faster than most of the languages as its speed matches closely that of C/C++, which are the fastest languages. In actual-world code, Julia can be up to two times faster than C. 

However, it is worth noting three levels to describe how fast the Julia programming language can be. 

  1. Naïve Julia (Python style): this is a pretty fast Julia, but it can become painfully slow in the case of any mistakes in the type stability.  A short number-crunching program, however, might not cause problems – it will just show if you begin implementing types. 
  2. Idiomatic Julia: this Julia code is written with attention to type stability and allocation, but it isn’t flexible to optimization. The speed of this code is comparable to compiled statically typed, garbage collected languages such as well written GO and Java. Note that a numeric Julia code can beat the languages mentioned above in most instances, thanks to the library functions that are optimized heavily. 
  3. High-performance Julia – this extreme performance Julia is at or above the level of C++ and Fortran. The code can do more with a static allocation of buffers. It feels more like a C code than a dynamic language. The code isn’t fun writing – it’s only necessary when maximum performance is needed. 

While Julia was built for speed, it’ll have an incredibly harder time beating well-written C/C++. These two languages have cutting-edge machine-optimized compilers – and the only way to write faster code is manual assembly optimization. 

Even so, Julia is the fastest dynamically typed, interactive language you can use currently. It isn’t in the same league as C/C++, Rust, or Fortran, but it matches up to any language out there in terms of performance. 

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Many programming languages are being channeled into the market. While some programming languages have a broader scope, others have a narrow scope. So the choice of a language is driven by your intention with that language. 

Ideally, the question of the right or easy-to-use programming language isn’t easy to answer because there is a blur between the boundaries of many languages, and it is becoming even more obscure. This is beneficial but also has limitations. 

That notwithstanding, Julia is reasonably easy to learn, and it should appeal to programmers who have a bias towards data science and machine learning. Since it has an easy-to-use syntax, you can get started quickly in Julia. However, mastering the different concepts requires time and practice – it’s not an easy way. Like any other programming language, learning the nitty-gritty needs a practical approach to the language.

If you are interested in data analysis, you’ll need to install and learn different tools, including the Anaconda platform. 

Julia’s closest competition in terms of function is Python and R. Nevertheless, R is only viable when a developer needs better data visualization, thanks to the robust shiny framework. By contrast, Python is a decent language for image processing, cloud computing, and speech analysis, courtesy of Scikit & Pandas. But R and Python are fantastic for data manipulation. 

Julia comes in handy for its speed; thus, Julia is the best bet if you have resource-intensive applications or time-critical applications. 

In programming, no single language can dominate others for long. There is always an aspect that each language brings to the market, which can make or break a language. For Python it is the ease of use, R statistical prowess, and Julia offers speed, convenience, and high performance. 

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Luis Gillman
Luis Gillman

Hi, I Am Luis Gillman CA (SA), ACMA
I am a Chartered Accountant (SA) and CIMA (SA) and author of Due Diligence: A strategic and Financial Approach.

The book was published by Lexis Nexis on 2001. In 2010, I wrote the second edition. Much of this website is derived from these two books.

In addition I have published an article entitled the Link Between Due Diligence and Valautions.

Disclaimer: Whilst every effort has been made to ensure that the information published on this website is accurate, the author and owners of this website take no responsibility  for any loss or damage suffered as a result of relience upon the information contained therein.  Furthermore the bulk of the information is derived from information in 2018 and use therefore is at your on risk. In addition you should consult professional advice if required.