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What are the best programming languages for data science?

36

Options
Considered

349

User
Recs.

Apr 4, 2022

Last
Updated

Here’s the Deal

36 Options Considered

Best programming languages for data science	Price	Platforms	Current stable version
83 Julia	Free	Windows, Linux, Mac, FreeBSD	1.5.3
76 Python	Free	Windows, Linux, macOS, AIX, IBM i, iOS, z/OS, Solaris, VMS	3.9.1
-- Clojure	-	Windows, Linux, Mac	1.10.1
68 R	Free	-	-
-- Rust	-	Linux, MacOS X, Windows, BSD	1.4.0

Julia

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Top Pro

Fast computation

Certain benchmarks suggest it is capable of outperforming Python and even C (in certain situations where FORTRAN libraries can be utilized). See More

Top Con

Young language with limited support

Julia was released in 2012. Due to its short existence, there is a limited amount of support for the language. Very few libraries exist as of yet, and the community is still quite small (though growing quickly). See More

LearnedKaskuh's Experience

Nice language See More

Specs

Platforms:Windows, Linux, Mac, FreeBSD

Current stable version:1.5.3

Type Model:Dynamically Typed

Top Pro

Multiple dispatch creates very clear code

Top Con

Plotting lib is not good

ExcitedMarqod's Experience

several years of Julia coding for data science See More

Top Pro

Nice regular syntax

Julia code is easy to read and avoid a lot of unnecessary special symbols and fluff. It uses newline to end statements and "end" to end blocks so there is no need for lots of semicolons and curly braces. It is regular in that unless it is a variable assignment, function name always comes first. No need to be confused about whether something is a method on an object or a free function. Unlike Python and Ruby, since you can annotate the types a function operates on, you can overload function names, so that you can use the same function name for many data types. So you can keep simple descriptive function names and not have to invent artificial function names to separate them from the type they operate on. See More

Top Con

Tooling & libs not yet up to par with others like R or Python

Lacks tools like R's Shiny for easy web applications/dashboards. The library ecosystem also doesn't have the breadth yet of R or Python (which is normal). The dataframe/datatable in Julia & data wrangling isn't also up to par yet with R data.frame/data.table/tibble & dplyr. See More

Top Pro

Great standard REPL

Out of the box Julia has a very good Read-Eval-Print-Loop, which both completes functions and types, as well as completion based on history of previous statements. It integrates well with the shell and has an excellent online help system. See More

Top Con

Slow startup of programs due to compilation of the programs & (some of) its libs at startup

Top Pro

Easy parallel computing

Top Con

Package landscape not working well with 1.0

1.0 was very recently released, and most of libraries do not work well with 1.0 yet. Package landscape is fragmented at the moment between packages working in 0.6 and packages working in 0.7/1.0. See More

Top Pro

Though dynamically typed, can use type annotations to improve performance and make some errors easier to catch

Top Con

Installation is not troublefree yet

Despite being 1.0, and because a lot of libraries do not work well in 1.0, installation may be difficult and may require a lot of manual intervention to get it working properly. See More

Top Pro

Easy Python interop

Take advantage of Python's larger ecosystem. See More

Top Con

Multiplication of packages may be unnecessary

Instead of building a stack with core libraries like the numpy/scipy/matplotlib stack in Python, multiple packages are needed to extend Julia base language possibilities. This may be meant to ease development, but it is a problem for users as you have to know that you need this tiny package to do one simple operation that is simply integrated into Scipy for instance. See More

Top Pro

Take advantages of matlab, R, Python

Top Con

1-based array and column major

This design probably comes from Matlab, but makes it unnatural to interface C and C++ and Python. However, arbitrary indexing is possible with later versions of Julia (but the norm is still 1-based indexing). See More

Top Pro

Almost as fast as C

Julia runs almost as fast as (and in fact in some cases faster than) C code. See More

Top Pro

Powerful n-dimensional arrays

Julia has built in n-dimensional arrays similar in functionality as Python's numpy. See More

Top Pro

Lisp inspired macros

Top Pro

High-level code

Julia provides a high level of abstraction, making platform-independent programming trivial and easing the learning curve. See More

Top Pro

Strong dynamic typing

Dynamic and high level, but does not isolate the user from properly thinking about types. Can do explicit type signatures which is great for teaching structured thinking. See More

Top Pro

Amazing learning curve

Julia requires no boilerplate code – a beginner needs to write only the parts they care about. This combined with the REPL provides the best learning experience available. See More

Top Pro

Written in itself

The Julia language is written in itself to a much larger extent than most other languages, so a budding programmer can read through the depths of the standard library and learn exactly how things work all the way down to the low-level bit-twiddling details, which can be englightening. See More

Top Pro

Copyfree license (unlike Python and R)

Top Pro

Function and operator broadcasting

You can perform operations on scalars, for example 2^2 or [1, 2, 3].^2. See More

Top Pro

Function overloading

You can have multiple functions with the same name, but doing different things depending on function arguments and argument types. See More

Top Pro

REPL-based

The Julia REPL allows quickly testing how some code behaves and gives access to documentation and package management immediately in the REPL. See More

Top Pro

Supports unicode

Allows using Greek letters, many superscripts and subscripts in code, making much closer to actual research papers and easier to read. See More

Top Pro

Strong Metaprogramming

Julia allows you to edit Julia code in the language itself and write powerful macros. It is a great introduction to metaprogramming features. See More

Python

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Top Pro

Rich set of libraries and extensions

Reduces the lines of code required to get stuff done. See More

Top Con

Slow

Unless you use wrapped native libraries or compile to native code (Cython, Numba). See More

VersatileTiddalik's Experience

Frustrating to use and inelagant in comparison to other languages See More

Specs

Platforms:Windows, Linux, macOS, AIX, IBM i, iOS, z/OS, Solaris, VMS

Current stable version:3.9.1

GZipped size:22.5MB

Typing discipline:Dynamically typed

Top Pro

SciPy (Scientific Python library)

Top Con

Very limited support for functional programming which is important for data science

Magabro's Experience

I'm a professor teaching Modeling and Simulation for Bioprocess Engineers. I have experience with C, Fortran, Matlab, Scilab and Process Simulators. I recommend Python due to its excellent libraries and beginner friendliness. Moreover, Jupyter-notebook is an outstanding tool for education. See More

Top Pro

Clean syntax

Python's syntax is very clear and readable, making it excellent for beginners. The lack of extra characters like semicolons and curly braces reduces distractions, letting beginners focus on the meaning of the code. Significant whitespace also means that all code is properly and consistently indented. The language also uses natural English words such as 'and' and 'or', meaning that beginners need to learn fewer obscure symbols. On top of this, Python's dynamic type system means that code isn't cluttered with type information, which would further distract beginners from what the code is doing. See More

Top Con

Limited support for functional programming

While Python imports some very useful and elegant bits and pieces from FP (such as list comprehensions, higher-order functions such as map and filter), the language's support for FP falls short of the expectations raised by included features. For example, no tail call optimisation or proper lambdas. Referential transparency can be destroyed in unexpected ways even when it seems to be guaranteed. Function composition is not built into the core language. Etc. See More

Top Pro

Numpy (Numerical Python library)

Top Con

Multi-threading can introduce unwanted complexity

Although the principles of multi-threading in Python are good, the simplicity can be deceptive and multi-threaded applications are not always easy to create when multiple additional factors are accounted for. Multi-thread processes have to be explicitly created manually. See More

Top Pro

Pandas Library

Top Con

It is best suited for scripting, but so are many other languages.

i.e. running js as a script in a node is trivial. Even languages that were not meant to run as a script are easy to use as a scripting language with just a .sh file. See More

Top Pro

Lots of tutorials

Python's popularity and beginner friendliness has led to a wealth of tutorials and example code on the internet. This means that when beginners have questions, they're very likely to be able to find an answer on their own just by searching. This is an advantage over some languages that are not as popular or covered as in-depth by its users. See More

Top Con

Significant whitespace

While proper formatting is essential for any programmer, beginners often have trouble understanding the need and lack the discipline to do it. Add to that all those editors that randomly convert N spaces (usually 8) to tabs and you get an instant disaster. See More

Top Pro

Active and helpful community

Python has an active and helpful community, such as the comp.lang.python Google Groups, StackOverflow, reddit, etc. See More

Top Con

Inelegant and messy language design

The first impression given by well-chosen Python sample code is quite attractive. However, very soon a lack of unifying philosophy / theory behind the language starts to show more and more. This includes issues with OOP such as lack of consistency in the use of object methods vs. functions (e.g., is it x.sort() or sorted(x), or both for lists?), made worse by too many functions in global name space. Method names via mangling and the init(self) look and feel like features just bolted on an existing simpler language. See More

Top Pro

Has many libraries for scientific computing, data mining and machine learning

Python is commonly used in data science and has many libraries for scientific computing, such as numpy, pandas, matplotlib, etc. See More

Top Con

Unflexible userbase

You will be expected to rigidly stick to the coding practices and to do everything by-the-numbers. One of the most common complaints I heard from people who left Plone, which is Python based, to Drupal, which is PHP based, is the community is more like a frat house than a community. If you look for help, make sure you follow the rules of whatever type of group you are requesting help from. See More

Top Pro

Good documentation

The Python community has put a lot of work into creating excellent documentation filled with plain English describing functionality. Contrast this with other languages, such as Java, where documentation often contains a dry enumeration of the API. As a random example, consider GUI toolkit documentation - the tkinter documentation reads almost like a blog article, answering questions such as 'How do I...', whereas Java's Swing documentation contains dry descriptions that effectively reiterate the implementation code. On top of this, most functions contain 'Doc Strings', which mean that documentation is often immediately available, without even the need to search the internet. See More

Top Con

Too opinionated for a general-purpose programming language

While it's a good language to learn and use after you have mastered a couple of other less rigid programming languages, it's definitely not good for first-time learners. Both the language itself and its community have made it quite clear that you should do everything the "Pythonic way" to get the best results, that it feels more like an opinionated framework instead of a general-purpose programming language, which means if you are a first-time learner and getting too "tuned" to the "Pythonic way" it will be much harder for you to learn other less-opinionated languages compared to the other way around. Like any programming languages and/or frameworks, I'd recommend first-time learners to learn less opinionated ones first to open up your mind, then learn some of the more opinionated ones to increase productivity for specific fields of works. After all, programming languages are just some utilities for the human mind to interface with the computers, and there are more suitable tools for different tasks, and you should master the "Pythonic way" (after you already have adequate experience in computer programming) instead of locking your mind too close to the "Pythonic way" as a first-time learner. See More

Top Pro

Easy to get started

On top of the wealth of tutorials and documentation, and the fact that it ships with a sizeable standard library, Python also ships with both an IDE (Integrated Development Environment: A graphical environment for editing running and debugging your code); as well as a text-based live interpreter. Both help users to get started trying out code immediately, and give users immediate feedback that aids learning. See More

Top Con

Might not be very future-proof

Lots of features that will probably be crucial as time goes (good support for parallelism for example) are missing or are not that well-supported in Python. Since 2.x and 3.x still exist, be prepared to switch if something makes 3.x take off in the future. See More

Top Pro

Can be used in many domains

Python can be used across virtually all domains: scientific, network, games, graphics, animation, web development, machine learning, and data science. See More

Top Con

Hard to debug other people's code

As the structure of Python code is based on conventions many developers are not following them and so it is difficult to follow/extract the design of not trivial application from the code. See More

Top Pro

Scikit Learn

An excellent library to start doing machine learning and data science. See More

Top Con

Worst language design ever

Instead of sticking to a certain paradigm, the original writer of the language couldn't make up his mind, and took something from everywhere, but messing it up as he went by. This is possibly one of the worst balanced languages ever. People who pollute their mind with Python and think it's the next best thing after sliced bread, will have to un-learn a lot of garbage 'pythonesque' habits to actually learn how to program. It's not because the academic world uses it a lot, that it's a good language. It says something about the inability of the academic world to write decent code, actually. See More

Top Pro

Jupyter-notebook

Top Con

Does not teach you about data types

Since Python is a dynamically typed language, you don't have to learn about data types if you start using Python as your first language. Data types being one of the most important concepts in programming. This also will cause trouble in the long run when you will have to (inevitably) learn and work with a statically typed language because you will be forced to learn the type system from scratch. See More

Top Pro

Keras

Excellent library for neural networks and deep learning, with access to Theano or TensorFlow, as desired. See More

Top Con

Language fragmentation

A large subset of the Python community still uses / relies upon Python 2, which is considered a legacy implementation by the Python authors. Some libraries still have varying degrees of support depending on which version of Python you use. There are syntactical differences between the versions. See More

Top Pro

Comes with extensive libraries

Python ships with a large standard library, including modules for everything from writing graphical applications, running servers, and doing unit testing. This means that beginners won't need to spend time searching for tools and libraries just to get started on their projects. See More

Top Con

Speed can matter

Python, like many interpreted and untyped languages, is fairly slow compared to compiled languages like C, C++ or Java. Sometimes this won't matter, but when it does you're talking about twenty times slower than C/C++ and ten times slowed than Java. You need to have a plan for what you will do if it is too slow. See More

Top Pro

PyOpenCL and PyCUDA

Speed up number crunching on the GPU. See More

Top Con

Version Confusion with V2.x and V3.x

Top Pro

Full programming language, unlike R

Allows for more rapid deployment of solutions. See More

Top Con

Lack of statistical and machine learning algorithms

Compared to programming languages like R, which were created for Data Science, Python is missing a lot of old and new statistical approaches. See More

Top Pro

One right way to do things

One of the Guiding Principles of Python is that there should be only one obvious way to do things. This is helpful for beginners because it means that there is likely a best answer for questions about how things should be done. See More

Top Pro

Has features of both high and low level language

It is somewhere between C and Java. See More

Top Pro

Good introduction to data structures

Python's built-in support and syntax for common collections such as lists, dictionaries, and sets, as well as supporting features like list comprehensions, foreach loops, map, filter, and others, makes their use much easier to get into for beginners. Python's support for Object Orient Programming, but with dynamic typing, also makes the topic of Data Structures much more accessible, as it takes the focus off of more tedious aspects, such as type casting and explicitly defined interfaces. Python's convention of only hiding methods through prefacing them with underscores further takes the focus off of details such as Access Modifiers common in languages such as Java and C++, allowing beginners to focus on the core concepts, without much worry for language specific implementation details. See More

Top Pro

Portable

Python can run on a wide variety of hardware platforms and has the same interface on all platforms. See More

Top Pro

Databases

Python provides interfaces to all major commercial databases. See More

Top Pro

Cross-platform

Installs and works on every major operating systems if not already installed by default (Linux, macOS). See More

Top Pro

Easy operator overloading

Via special "dunder" methods. See More

Top Pro

Very similar to pseudo-code

When learning Computer Science concepts such as algorithms and data structures, many texts use pseudo-code. Having a language such as Python whose syntax is very similar to pseudo-code is an obvious advantage that makes learning easier. See More

Top Pro

SageMath

Clojure

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Top Pro

Minimal syntax

Being a LISP, programs are simple: they're just functions and data. That it doesn't get bogged down with syntax or the loftier FP concepts like monads makes it one of most approachable functional languages for beginners. See More

Top Con

Confusing error messages

Clojure's error messages more often than not are very confusing. They usually involve stack traces that do not thoroughly explain where the error was caused or what caused it. See More

FascinatingBunene's Experience

Not famous or lacks features essential for data science See More

Specs

Platforms:Windows, Linux, Mac

Current stable version:1.10.1

Site:www.clojure.org

Price:Free, Open Source

Top Pro

Immutability is the default

Clojure programmers are highly encouraged to use immutable data in their code. Therefore, most data will be immutable by default. State change is handled by functions (for transformations) and atoms (an abstraction that encapsulates the idea of some entity having an identity). See More

Top Con

Tied to the JVM and its limitations

Some language constructs were obviously created as workarounds for JVM limitations. This makes the language much less elegant than it could have been. Also, the JVM has a very cumbersome FFI. See More

Top Pro

Huge ecosystem of libraries to work with

There's a very large ecosystem of high-quality Clojure libraries which developers can use. One example is Incanter. It's a great data analytics library and a very powerful tool for dealing with matrices, datasets and csv files. See More

Top Con

Syntax can be alien / jarring for those used to other Lisps

Perhaps some may consider this attribute an advantage. Clojure does not attempt to maintain significant compatibility with other Lisps. So, if you already know a Lisp or are used to the way Lisp works in general, you'll probably be confused if you take a look at Clojure. See these resources for more details on this subject: https://clojure.org/reference/lisps http://stackoverflow.com/q/6008313/2636454 http://gilesbowkett.blogspot.com/2015/01/one-major-difference-between-clojure.html http://softwareengineering.stackexchange.com/q/153128/166211 See More

Top Pro

Tries to solve problems as simply as possible

Simplicity is one of the pillars on which Clojure is built. Clojure tries to solve many problems in software development as simply as possible. Instead of building complex interfaces, objects or factories, it uses immutability and simple data structures. See More

Top Pro

Cross platform

Clojure compiles to JVM bytecode and runs inside the JVM. This means that applications written in Clojure are cross-platform out of the box. See More

Top Pro

Dynamic language

A superb data processing language. While rich type and specification systems are available they are optional. See More

Top Pro

Good for writing concurrent programs

Since Clojure is designed for concurrency, it offers things like Software Transaction Memory, functional programming without side-effects and immutable data structures right out of the box. This means that the development team can focus their energies on developing features instead of concurrency details. See More

Top Pro

Great tool used in automating, configuring and managing dependencies available

Leiningen is a very useful tool for Clojure developers. It helps wiht automation, configuration and dependency management. It's basically a must for every Clojure project. See More

Top Pro

Extensible

Clojure has an elegant macro system which enables language additions, Domain-specific languages (DSLs), to be created much easier than most other languages (with the exception of Racket, perhaps). See More

Top Pro

Incanter

Top Pro

Game is available with which you can learn Clojure

Nightmod is a tool used to make "live-moddable" games. It displays the game's code while you are playing and allows you to inject new code using Clojure. This can be a fun and useful experience for people trying to learn Clojure. See More

Top Pro

No C/Java syntax

Refreshing, BTW! See More

Top Pro

Rich Hickey

The creator is so awesome, he's a feature. Just look up his talks and see why. See More

R

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Top Pro

Rich choice of high quality libraries

Be it data manipulation or plotting there is always a wide variety at CRAN available. See More

Top Con

Too slow

R is too slow to handle big data. See More

SociableSlaineMacDela's Experience

Rich ecosystem of statistical libraries. The entire community is data-sciency-oriented In contrast with Python where not all users are involved in data science. See More

Specs

Versioning:3.5.1

Top Pro

Ggplot2

Top Con

The library ecosystem (mostly CRAN but also github, BioConductor & others) is rather fragmented

There are over 10000 R packages and rising and there are many packages with overlapping functionality. It's not always clear which package is "best" for your needs. The situation is getting better thanks to Hadley Wickham who has rewritten a lot of core functionality from R base & popular but 'old school' packages in a few big alternative packages in a new more modern style and also more coherent (the so called 'Hadleyverse' or 'Tidyverse'). Together these form a framework analogous to what you have in Pydata for data science in Python or Spring for web applications in Java. See More

Top Pro

Lots of packages available

There are lots of different packages available that can be easily searched from the CRAN repo site and downloaded, or installed via the R command line interface. These packages are easy to include in a project or source file, and pertain to a wide variety of topics, from classification, social media analysis, and text processing to interactive 3d plotting and networks (including neural nets). See More

Top Pro

Open source

R is available under an open-source license, which means that anyone can download and modify the code. See More

Top Pro

RStudio is a great IDE

It's free, lightweight, and easy to use, making working with R a pleasure. See More

Top Pro

Knitr + Rmarkdown for (dynamic) documents

Top Pro

Very active and helpful community

The R community, especially on StackOverflow, is extremely helpful and reactive. It is really easy to find help when you are stuck with a certain problem, even as a complete beginner with basic problems. See More

Top Pro

Shiny for web applications/dashboards

Top Pro

Has a wide range of options when it comes to IDEs/GUIs

Among the IDEs available there are several commercial applications as well as free and/or open-source ones, such as R Studio, which features syntax highlighting, project management capabilities, integrated terminal access, decent code completion and on-the-spot parameter hinting, graphical interfaces for package installation and such, and commendable extensibility/developer support. See More

Top Pro

Data.table package for high-performance data manipulations

Top Pro

A variety of implementations for all possible algorithms and statistical methods (nowhere else found)

Top Pro

Microsoft is now involved

Which means more high quality tooling (support for R in Visual Studio) & libs & integrations (e.g. R in SQLServer) & cloud solutions for scalability beyond single core & single host (R server , Spark via sparkR/sparklyr lib,... all on Azure supported by MS) . It also means a lot more 'mainstream' developers are going to be exposed to R (e.g. via SQLServer) and hence R is going to become a lot more popular outside of its traditional academic statistics niche See More

Top Pro

There are so many R packages

There's always already a package for an algorithm described in a paper. See More

Rust

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Top Con

Beginners could be stumped by some special concepts like Ownership, etc and spend extra time on it

Top Pro

Catch errors at compile-time

Since Rust is statically typed, you can catch multiple errors during compile time. This is extremely helpful with debugging, especially compared with dynamically typed languages that may fail silently during runtime. See More

Specs

Platforms:Linux, MacOS X, Windows, BSD

Current stable version:1.4.0

License:MIT/Apache

Top Con

Rust is a systems language not a data science language

Top Pro

Threads without data races

Unique ownership system guarantees a mutable datum to be owned and mutated by only one thread at a time, so there's no data race, and this guarantee is checked at compile time statically. Which means easy multi-threading. Of course, immutable data can be shared among multiple threads freely. See More

Top Con

Hard to find learning resources or libraries

Because it's still a relatively new language, Rust does not enjoy a following as large as other languages/environments. Rust development has also been rather volatile for a long time during the beginning of the development of the language adding to this issue. Because of the small community, it's harder to find useful libraries for projects or any other kind of resource. See More

Top Pro

Built-in concurrency

Rust has built-in support for concurrency. See More

Top Con

Very new and untested

Top Pro

Generics support

You don't have to write same array and dictionary classes hundreds and thousands times for strong type check by compiler. See More

Top Con

Asynchronous I/O is not (yet) a part of the core language

The issue of asynchronous I/O is still being discussed as a language feature, and not yet in the implementation phase. See the RFC on GitHub here. See More

Top Pro

Supports cross compilation for different architectures

Since Rust 1.8 you can install additional versions of the standard library for different targets using rustup/multirust. For example: $ rustup target add x86_64-unknown-linux-musl Which then allows for: $ cargo build --target x86_64-unknown-linux-musl See More

Top Pro

Support for macros

When you identify a part of your code which gets repeated often, which you cannot abstract using functions or classes, you can use Rust's built-in Macros. See More

Top Pro

Compiles to machine code allowing for extra efficiency

Rust uses LLVM as a backend, among other things this allows Rust code to compile down to machine languages. This allows developers to write programs that run as efficiently as possible. See More

Top Pro

Easy to write understandable code

While not as verbose as Java, it still is much more verbose than languages like Go and Python. This means that the code is very explicit and easy to understand. See More

Top Pro

Makes developers write optimal code

Rust is a modern programming language written around systems. It was designed from the ground up this way. It's language design makes developers write optimal code almost all the time, meaning you don't have to fully know and understand the compiler's source code in order to optimize your program. Furthermore, Rust does not copy from memory unnecessarily, to give an example: all types move by default and not copy. Even references to types do not copy by default. In other words, setting a reference to another reference destroys the original one unless it's stated otherwise. See More

Top Pro

Developers of Rust consider data science support in Rust important and work on it

Top Pro

Zero-cost futures or Async IO

Top Pro

Official package manager and build tool

Cargo is the official package manager for Rust. It comes with the language and downloads dependencies, compiles packages, and makes and uploads distributable packages. See More

Scheme

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Top Pro

Multi platform

GNU/Linux, OS X, and Windows versions available. See More

Top Con

Little job market

There are little to none jobs searching for a Scheme programmer. The ones that exist are more related to Research in Maths or Artificial Intelligence. See More

Top Pro

Simple syntax

Scheme syntax is extremely regular and easy to pick up. A formal specification of the syntax fits onto just a few pages; it can be introduced informally in a paragraph or two. Students are not distracted by remembering how to write if statements or loops or even operator precedence because every syntactic follows the same pattern. Ultimately, everything looks something like this: (func a b c) This includes not only user-defined functions but even control flow: (if cond then-clause else-clause) or even primitive operations like define and set: (define foo 10) (set! foo 11) This means that nothing really has special syntactic treatment in the language. There are essentially no weird edge-cases to memorize, and different concepts are given a more equal weight in the language. (Unlike Algol-like languages which tend to given undue weight to loops and assignment statements, for example.) See More

Top Pro

No Magic - it's clear how everything works

Scheme has far less built into the language itself, helping students see that things like OOP are not magical: they are just patterns for organizing code. Everything in Scheme is built up from a very small set of primitives which compose in a natural and intuitive fashion. Having a language that does not accord many things special status helps keep students open minded. This will help students later go between different languages and paradigms from procedural to object-oriented to functional. See More

Top Pro

Great at teaching fundamental programming ideas

Scheme teaches the important, fundamental ideas immediately without the distraction of unnecessary syntax or language features. See More

Top Pro

Great, well known textbooks

There is a set of very strong textbooks introducing CS and programming using Scheme. These books are available for free online. The most famous example--and one of the most famous CS books full stop--is Structure and Interpretation of Computer Programs usually known as SICP. This book introduces fundamental ideas in computer science and covers an incredible amount of material quickly and clearly without requiring any prior knowledge. However, some people find SICP a bit challenging as a first text. Happily, there are other more introductory texts as well. Simply Scheme is a book designed to be read before SICP, easing beginners into the language as well as CS and programming in general. How to Design Programs is another text used in introductory college courses. See More

Top Pro

Robust metaprogramming

The quotation functionality of Lisp allow for extremely powerful, yet syntactically straightforward metaprogramming via macros. This is more powerful than the C preprocessor while being less involved than something like Template Haskell or F# quotations. Using macros to properly decompose a problem domain teaches new developers good habits, improving composibility and reliability when tackling large programs. Scheme metaprogramming also serves as a gentle introduction to domain specific languages. See More

Top Pro

Multiparadigm

Unlike most languages, Scheme actually accords both functional programming and imperative programming roughly equal status. Many other languages like Python and Java are staunchly imperative while SML and Haskell are primarily functional; Scheme is a nice middle ground. Additionally, since Scheme syntax is extremely flexible, it can easily be re-purposed for teaching non-deterministic and logic programming. There is no need to learn a new language like Prolog when the same ideas can easily be expressed with Scheme syntax. This gives students a good perspective on different ways to think about and organize programs, which makes it much easier to move forward to other languages and technologies. See More

Haskell

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Top Pro

Mathematical consistency

As Haskell lends itself exceedingly well to abstraction, and borrows heavily from the culture of pure mathematics, it means that a lot more code conforms to very high-level abstractions. You can expect code from vastly different libraries to follow the same rules, and to be incredibly self-consistent. It's not uncommon to find that a parser library works the same way as a string library, which works the same way as a window manager library. This often means that getting familiar and productive with new libraries is often much easier than in other languages. See More

Top Con

Too academic, hard to find "real world" code examples

Specs

Site:https://www.haskell.org/

Paradigm:functional

Type System:static

Top Pro

Powerful categorical abstractions

Makes categorical higher order abstractions easy to use and natural to the language. See More

Top Con

Documentation for most packages is short and lacking

A few Haskell packages are well documented but this is the exception, not the rule. Most of the time a list of function signatures is what passes for documentation. See More

Top Pro

Popular in teaching

Haskell is really popular in universities and academia as a tool to teach programming. A lot of books for people who don't know programming are written around Haskell. This means that there are a lot of resources for beginners in programming with which to learn Haskell and functional programming concepts. See More

Top Con

Obscure ugly notation

0 = 1 Using "=" like this: <code> -- Using recursion (with pattern matching) factorial 0 = 1 factorial n = n * factorial (n - 1) </code> Example from https://en.wikipedia.org/wiki/Haskell_(programming_language) is quite simply annoying aesthetics. See More

Top Pro

Quick feedback

It's often said that, in Haskell, if it compiles, it works. This short feedback loop can speed up learning process, by making it clear exactly when and where mistakes are made. See More

Top Con

Package manager is unstable & lacking features

Cabal (there are other choices but this is the most popular) can not uninstall a package. Also working at a few locations it is difficult to have the same environment for each one be the same. See More

Top Pro

Referentially transparent

Haskell's Purely Functional approach means that code is referentially transparent. This means that to read a function, one only needs to know its arguments. Code works the same way that expressions work in Algebra class. There's no need to read the whole source code to determine if there's some subtle reference to some mutable state, and no worries about someone writing a "getter" that also mutates the object it's called on. Functions are all directly testable in the REPL, and there's no need to remember to call methods in a certain order to properly initialize an object. No breakage of encapsulation, and no leaky abstractions. See More

Top Con

Language extensions lead to unfamiliar code

Haskell's language extensions, while making the language incredibly flexible for experienced users, makes a lot of code incredibly unfamiliar for beginners. Some pragmas, like NoMonomorphismRestriction, have effects that seem completely transparent in code, leading beginners to wonder why it's there. Others, like ViewPatterns, and particularly TemplateHaskell, create completely new syntax rules that render code incomprehensible to beginners expecting vanilla function application. See More

Top Pro

Forces you to learn pure functional programming

It is pure and does not mix other programming paradigms into the language. This forces you to learn functional programming in its most pure form. This avoids you falling back on old habits and learn an entirely new way to program. See More

Top Pro

Hand-writeable concise syntax

Conciseness of Haskell lets us to write the expression on the whiteboard or paper and discuss with others easily. This is a strong benefit to learn FP over other languages. See More

Top Pro

Easy to read

Haskell is a very terse language, particularly due to its type inference. This means there's nothing to distract from the intent of the code, making it very readable. This is in sharp contrast to languages like Java, where skimming code requires learning which details can be ignored. Haskell's terseness also lends itself to very clear inline examples in textbooks, and makes it a pleasure to read through code even on a cellphone screen. See More

Top Pro

Easy to make DSL

You can handle data flow with your own DSL (Domain Specific Language). Haskell allow us easy to make DSL. See More

Top Pro

Functions curry automatically

Every function that expects more than one arguments is basically a function that returns a partially applied function. This is well-suited to function composition, elegance, and concision. See More

Top Pro

Very few language constructs

The base language relies primarily on function application, with a very small amount of special-case syntax. Once you know the rules for function application, you know most of the language. See More

Top Pro

Easy syntax for people with a STEM degree

Since the basic syntax is very similar to mathematics, Haskell syntax should be easy for people who have taken higher math courses since they would be used to the symbols used in maths. See More

Top Pro

Highly transferable concepts

Haskell's referential transparency, consistency, mathematics-oriented culture, and heavy amount of abstraction encourage problem solving at a very high level. The fact that this is all built upon little other than function application means that not only is the thought process, but even concrete solutions are very transferable to any other language. In fact, in Haskell, it's quite common for a solution to simply be written as an interpreter that can then generate code in some other language. Many other languages employ language-specific features, or work around a lack of features with heavy-handed design patterns that discourage abstraction, meaning that a lot of what is learned, and a lot of code that is needed to solve a particular problem just isn't very applicable to any other language's ecosystem. See More

Top Pro

Open source

All Haskell implementations are completely free and open source. See More

Scala

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Top Con

Complex language

Not for beginners. See More

Top Pro

Algebraic types

Specs

Current stable version:2.13.1

Site:scala-lang.org

Price:Open Source (Free)

Top Con

Can be intimidating for beginners

Scala is an industrial language. It brings functional programming to the JVM, but not with a "start small and grow the language" perspective, but rather a very powerful language for professional programmers. See More

Top Pro

Several data science libraries available (Scala or Java based)

Top Pro

Modern object-functional language that allows high level abstraction

Top Pro

Based on the JVM hence cross platform and much tooling

Top Pro

It's the language of Spark, the leading big data platform

F#

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Top Pro

Native support for Algebraic Data Types

Algebraic Data Types enables the creation of complex data structures in a very readable and reliable manner. See More

Top Con

Only one worthy big ML lib available (Accord.net) and it's not even in F# but C#

Specs

Current stable version:4.1

License:Apache 2.0

IDE Support:Visual Studio, Code, Rider

Top Pro

Full access to all .NET libraries

Top Con

No real data science/ML community in .NET & F#

Top Pro

High performance

Has the same performance profile as C#. See More

Top Pro

Simple ML-style expression based syntax

Top Pro

Easy access to R libraries via the R Type Provider

Top Pro

Great machine learning libraries (second only to Java)

ML.Net, Accord.Net, Infer.Net, many smaller libraries on Nuget including great support for all major databases and cloud providers See More

Top Pro

Allows writing predictable code

Like many other functional languages, F# takes a stance of immutability for most of its constructs (state, values etc...). There are several reasons why having immutable values is good. One of those is that the code you are writing becomes much more predictable and you don't have to worry about any side effects. See More

Top Pro

Concise syntax

F#'s syntax tends to be terse while remaining very readable and easy to understand without being a chore to write. See More

Common Lisp

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Top Pro

Image based runtime

The state of the program may be saved and reloaded as an image, supporting safer modification of the running program. New code may be compiled into the image as the program runs, while late binding ensures that symbol redefinitions take effect throughout the program. See More

Top Pro

Almost as fast as, or faster than, C

Some compilers such as SBCL can be faster than C or other low-level languages, and most compilers can generate fast native code. See More

Top Pro

Condition/restart system

It is easy to recover from errors. Error resolution can be determined by the user at the REPL. See More

Top Pro

Carefully designed for interactive use

Almost all aspects of the language are designed with interactive/repl use in mind. See More

Top Pro

Very Powerful REPL with SLIME

SLIME (Superior Lisp Interaction Mode for Emacs). See More

Bash/awk/sed

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Java

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Top Pro

Broad number of skilled developers available

Cilk

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Hy

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Fortran

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Top Pro

Maximum performance

Fortran compilers are very mature--not even C is faster. See More

Top Con

Verbose syntax for type declarations

Top Con

No library ecosystem for modern data science: exploratory data analysis, statistics, machine learning

Fortran is good for pure number crunching on supercomputers. The available open source libraries are mostly in domains like physics or climate science and they are mostly about things like simulations or partial differential equations solving. So it's more suitable for going from models to data, rather than for data science, which is the reverse: going from data to models. Even if you decide to use Fortran for computation, things like getting data from databases and visualization have to be done with non-Fortran libs. Nevertheless, it's worth noting that data science languages like Julia, R, or Python do use wrapped Fortran libraries (mostly for matrix computations). See More

SAS

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Top Con

Outdated

MightyOizys's Experience

Many years of data/analytic research programming. SAS is great - powerful data manipulation features including a macro language. Ultra complete statistical procedures. Years ago SAS was only licensed (by the SAS Institute) and was rather pricey. But SAS made its Base product (including the programming language) available to individual users for free. Not sure if SAS would be the choice for interactive web development, but otherwise it is a stellar product. See More

Wolfram Mathematica

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Top Pro

Lots of functionality

Top Con

Not free or open source, expensive for commercial non-academic use

Nuance: you can experiment with it for free now in the Wolfram cloud. See More

Top Pro

Very mature

Wolfram Mathematica has been around for a long time without any major changes in the basic design. See More

Top Con

Slow & no generic programming

Top Pro

Built-in CPU Parallelization

Top Pro

Beautiful graphics

Top Pro

Very high level, can solve complex problems in a few lines

Top Pro

Rich ecosystem of libraries

Because all the libraries in Mathematica are developed by Wolfram itself, they are highly coherent and interoperable. See More

Top Pro

Excellent for symbolic computing

e.g. solving differential equations analytically. See More

Top Pro

Functional & rule based programming language

Top Pro

Mathematica invented notebooks more than 20 years ago, long before iPython cloned it

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