5 Types of Programming Languages for Data Scientists5 Types of Programming Languages for Data Scientists5 Types of Programming Languages for Data Scientists

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Businesses succeed or fail based on the quality of their decisions. That’s why it benefits companies to have the highest-quality information available to decision-makers when they need it.

Ensuring the timely delivery of business intelligence to decision-makers is the job of data scientists. These professionals rely on programming languages to convert pools of big data into business intelligence. The types of programming languages used by data scientists range from longtime industry standbys Python and SQL to more recent arrivals such as R, Julia, and Scala.

Individuals who wish to pursue careers as data scientists should understand how data science programming differs from software programming. They must also know how deterministic programming, which always outputs the same result under identical conditions, differs from probabilistic programming, whose output is a probability distribution of potential outcomes.

While software programmers and data scientists often use the same tools, they use them in very different ways. The five programming languages covered in this guide represent the variety of tools that data scientists rely on to ensure an organization’s decision-makers have the information they need to succeed.

Simple, multi-purpose, and powerful; when it comes to programming languages, that’s the winning combination that has made Python a perennial favorite of programmers for the past several years.

The use of Python for data science offers many advantages, but the language presents some challenges for data scientists as well.

Python is described as a high-level programming language because it uses natural language that is easy for people to understand. By contrast, low-level languages such as machine language and assembler use computer code that is nearly impossible for humans to read.

Python is also a general-purpose language in that it is suitable for many programming tasks. It is a dynamic language, because changes to code can be completed as the program is running, as opposed to static languages that allow changes to be performed only when the code is compiled.

One of the attributes that makes Python powerful and easy to use is its object orientation. Object-oriented languages define data and their associated processing, or “methods,” as self-contained entities called “objects.”

The employment service Robert Half lists four benefits of object-oriented programming languages:

The simplest definition of open-source software is code that other people are free to use and modify as they wish. However, as Opensource.com points out, just because open-source code is free to use doesn’t mean that it’s thoroughly tested and reliable. Characteristics of open-source software include the following:

Programming languages convert their code into a desired action in one of two ways, as the website LifeWire explains.

While interpreted code generally runs more slowly than compiled code, interpreted languages such as Python make it easier to write programs that run correctly on such platforms as Linux, Windows, and MacOS.

Much of Python’s popularity among data scientists is due to its suitability for use in many different situations. Because it is a dynamic programming language, Python processes data faster than other languages such as R. Python’s flexibility allows it to integrate with web apps and incorporate statistical code in production databases.

Here are three Python attributes that serve the needs of data scientists.

Python was designed to be easy for beginning programmers to learn and use, as the web specialist 321 Web Marketing describes. Yet it is powerful enough to be used to create artificial intelligence (AI) and data analysis applications. Python’s natural-language syntax makes it easy for programmers to change code and for other programmers to modify, maintain, or reuse the code.

Beginning programmers benefit from learning Python because they are able to create working programs quickly and easily. There are many Python tutorials, several of which can be accessed on the Python Wiki.

In addition to the Python Standard Library, the language benefits from thousands of third-party modules, packages, and libraries. Links to many of these tools are on the Python Wiki. The website Towards Data Science lists 10 Python libraries designed specifically for data science:

Rapid application development (RAD) and prototyping makes it simpler to create and integrate many different Python systems, as the software provider Qburst explains. One of the most popular web frameworks for Python is Django, which automates many coding operations. It also includes a customizable commenting system and tools for creating RSS feeds, Google sitemaps, and other web components.

An alternative to Django for RAD in Python is Flask, which is noted for being simple, flexible, and easy for beginners to customize, as software vendor Flexsin describes.

Python has been around for 30 years, yet it continues to grow in popularity among programmers, especially for data analysis applications. Yet Python is far from the paragon of programming languages. Software services firm Netguru points out that no single programming tool will be the best choice for all applications and purposes.

These are some of the reasons for choosing a language other than Python for data science applications.

Software and IT services provider Mindfire Solutions cites several studies that found Python code runs more slowly than Java, C++, and other popular programming languages. To improve Python’s performance, some developers rewrite existing Python code or replace the default Python runtime with a custom runtime.

While Python includes components that allow more than one operation to be run simultaneously (concurrency), it doesn’t always allow multiple operations to be run in parallel. To explain the difference between concurrency and parallelism, HackerNoon.com uses the example of a restaurant that has one counter for placing orders and another for picking up orders.

Python’s built-in libraries support multiprocessing and multithreading, which are required for parallelism, but its Global Interpreter Lock (GIL) prevents multiple threads from accessing the same object at one time. While there are workarounds to this problem, they are difficult to implement.

Python is a general-purpose programming language that includes many libraries and packages for statistical modeling and other data analysis tasks. By contrast, R is a language designed specifically for data science applications, as described in the next section. Software vendor DeZyre explains that predictive analytics applications are developed in two phases:

However, data visualization is the key to presenting the results of statistical analyses, and in this area, R has a definitive edge over Python.

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The R programming language was designed specifically for data science. The R Project describes R as being similar to the S statistical programming language developed by John Chambers and others at the former Bell Labs, which is now Lucent Technologies. The language’s software suite includes several components:

The R language and development environment features a range of statistical tools for linear and nonlinear modeling, classical statistics tests, time-series analyses, and clustering. It also includes a variety of graphics tools and techniques, including publication-quality plots, formulas, and mathematical symbols. R can be compiled and run on Unix (FreeBSD and Linux), Windows, and MacOS systems.

R supports both functional and object-oriented programming, which means it’s neither a pure functional programming language nor a pure object-oriented language. Charles J. Geyer of the University of Minnesota explains that in a pure functional programming language, all functions act like math functions:

In R, functions do everything, including assignments. Objects in R are specialized data structures that are referred to via symbols or variables. The symbols themselves are objects that can be manipulated just as other objects are. This is a departure from objects in other languages.

R also differs from other object-oriented languages in that it supports three distinct object classes: S3, S4, and RC. None of the three object classes are as purely object-oriented as Python, C++, Java, or other object-oriented languages, according to the website R-Bloggers.

R is highly extensible. That means users can easily design new functions. To support performance-intensive tasks, R links to C, C++, and Fortran code and can call those languages at run time. This means C code can be written to manipulate R objects directly.

The R Foundation explains that the open-source language is available for free under a GNU General Public License from the Free Software Foundation. The R Project provides links to downloads, as well as release notes for the most recent versions of the language.

An advantage that R has over Python for data science applications is vectorization: The tech and software information source DZone explains that when working with two-dimensional matrices, multiple arrays, or complex math computations, Python and other programming languages require a complicated series of for-loops.

By contrast, R’s support for vectorization allows mathematical functions to be performed on complete lists or matrices as if they were single objects. (Note that the NumPy Python library described above adds some of this capability to Python.) The programming website I-programmer describes a vector in R as the equivalent of a one-dimensional array in other languages:

Industry source ActiveWizards lists the top four features of R for data science:

From its inception, R has been designed to “reflect the way that statisticians think and work,”  according to IBM Developer. Where Python is general purpose, R is special purpose: it is intended solely for analyzing data. In fact, Python’s data manipulation module, pandas (described above), is borrowed directly from R.

R offers more powerful data visualization than Python or any other business intelligence platform, according to Towards Data Science. Lastly, R features top-flight machine learning tools and training materials to help data scientists incorporate AI techniques into their data analyses.

When the online tool provider Data Science Plus compared the time required to loop and generate pseudo-random numbers in Python and R, it determined that Python was as much as eight times faster than R when the number of iterations was less than 1,000. However, when the number of iterations exceeded 1,000, R’s “lapply” function (rather than the “for” loop) allowed it to outpace Python.

In the July 2020 Programming Community Index by software developer TIOBE, R reached the number eight position, which is the highest rating a statistical programming language has received on the index. Python and R have supplanted commercial statistical programs such as SAS, Stata, and SPSS, according to the index. This is especially so at research institutes and universities.

In addition, researchers who are working to find a vaccine for COVID-19 have created a surge in demand for people with data analytics, data mining, and statistics skills.

ActiveWizards identifies three shortcomings of R for data science:

When artificial intelligence trainer Data-Driven Science compared R and Python for data science applications, it gave Python the edge in the number of supported data formats and its ability to create data sets. It’s easier to request data from the web in Python than to do so in R, for example. In addition, some numerical modeling analyses in R require use of packages outside R’s core functionality.

Python can be easier for data scientists to learn than R. This is true because Python’s syntax more closely resembles that of other languages they may be familiar with. Also, Python is “production-ready” and integrates easily with an organization’s existing production systems. Conversely, R has been used primarily in academic and research settings, so there are fewer open-source libraries designed for specific industries.

As mentioned above, R is a functional programming language with object-oriented features, while Python is a conventional object-oriented language with a full range of object functionality. Towards Data Science notes that many R users “yearn for the object-oriented capacities that are native to Python.”

Many of Python’s object-oriented features can be added to R via packages.

Tidyverse is described by Towards Data Science as a “major dialect of R” that makes R easier to learn, especially for people without a programming background. However, rather than simply changing R’s interface, Tidyverse remakes the language. This approach runs counter to the spirit of open-source software, which is designed to be easy for users to customize.

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The most popular tool for analyzing data is SQL, which explains why knowing SQL is such an important skill for data scientists. SQL for data science allows the massive amounts of data currently stored in relational databases to be used for advanced analytics applications. The learning source Analytics Vidhya states that “you simply cannot expect to carve out a career in either analytics or data science if you haven’t picked up SQL.”

SQL is a query language whose roots date back to the invention of relational databases in the 1970s by researchers working for IBM. It is now a standard that is recognized by the American National Standards Institute (ANSI) and International Organization for Standardization (ISO).

Two key attributes of SQL are its ease of use and its power: it allows data to be queried, manipulated, and aggregated to generate reports that inform business decisions.

Opensource.com defines a domain-specific language (DSL) as one that is intended to be used in the context of a specific domain. By contrast, a general-purpose language (GPL) is designed to serve a range of business applications.

SQL’s domain is data management. DSLs are able to take advantage of all the attributes of the domain. They are also easier to learn and master than GPLs, and they are designed to meet the specific needs of the developers and experts who work in the domain.

SQL is available in commercial versions such as Oracle and Microsoft SQL Server, as well as in open-source releases including MySQL, PostgreSQL, and SQLite. The primary difference between commercial and open-source versions of SQL is support services. The former are supported by the vendors, while the latter receive upgrades and patches from a community of users, sometimes on a volunteer basis, and sometimes the support requires paying a fee.

Language and DevOps source DZone points out that companies using open-source SQL databases must build their own schemas and customizations to meet their specific needs. That’s why it’s important to consider how well the community of users for a specific SQL version is able to provide the support the business will require.

A query language is used to extract actionable information from a database. The two types of database queries are a “select” query to retrieve data, and an “action” query to request additional operations on the query such as insert, update, or delete.

Data-driven marketing provider TechTarget describes the various uses of queries in SQL:

SQL provides data scientists with an introduction to data analysis by making it easy to work on datasets, which are the foundation of analytics. The analyses can be as basic as counting rows and items using the COUNT() function, or as complex as creating and filtering groups via the GROUP BY statement and aggregate functions such as COUNT, SUM, and AVG.

Since most of the businesses in the world store their valuable data in relational database management systems (RDBMSs), it follows that SQL would be the primary method used by data scientists to tap the massive data stores to extract business intelligence. DZone lists the advantages of RDBMSs for business:

On the DB-Engines Ranking of database management systems, the four most popular DBMSs and five of the top 10 DBMSs are based on SQL.

Before raw data can be queried, it must be processed. For machine learning applications, the raw data must be engineered to convert it to prepared data, as Google explains. Once the data is prepared, feature engineering tunes it to shape the data into the form that the machine learning model expects.

Preprocessing entails several steps:

Software developer Informatica describes how to craft SQL preprocessing and postprocessing commands.

SQL meets most of the data analytics needs of data scientists, but for some applications it can be more complicated than simply using a spreadsheet or other straightforward program. In other instances, SQL may be too generic or lack the special features that data science applications require.

Common data science operations such as statistical analysis, regression tests, and time series require a high level of data manipulation that entails transforming the data into various formats. While SQL is adept at combining data from multiple tables, the high-level data manipulation that data science applications need can be challenging to complete using only SQL commands.

When creating complex queries, Python’s pandas library makes it much simpler to write and understand the query. Python’s commands are broader and more functional than those in SQL, whose queries are usually a combination of JOINS, aggregate functions, and subqueries. In addition, Python commands and libraries are designed for specific purposes, whereas SQL’s are intended to be applied generally.

Relational database firm EdgeDB lists four shortcomings of SQL, all of which relate to the language’s complexity when compared to that of alternatives:

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The Julia programming language was developed by MIT researchers in an effort to combine the best features of such languages as Python, R, Ruby, C, and MatLab. Julia debuted in 2018 and was promoted by its developers as a language that offers the “high-level productivity and ease of use of Python and R” along with the performance of C++.

Julia is best known for its performance and ease of use, according to a survey of Julia users conducted by its vendor Julia Computing, as ZDNet reports. The language uses the “multiple dispatch” paradigm that facilitates the expression of both object-oriented and functional programming patterns. The language’s high-level syntax makes it easy for both new and experienced programmers to learn.

As a dynamically typed language, Julia doesn’t require that types be identified until run time, at which time the actual values to be manipulated by the program become available. However, Julia borrows a feature from static type systems in that certain values can be marked as being of specific types. This makes the process of generating code much more efficient.

As an open-source language, Julia can be used for free under an MIT license. All source code is available to download from a GitHub repository.

Julia’s support for metaprogramming allows sophisticated code to be generated quickly and simply. Julia code is represented as a data structure of the language itself. The objects that represent code can be created and manipulated from within the language, so a program can transform and generate its own code.

Towards Data Science lists Julia’s five best features:

Julia has attracted a great number of users in statistics, data science, engineering, machine learning, computer science, AI, economics, and other technical fields, as ZDNet states. The language is used by financial firms such as Capital One, Aviva, and BlackRock, as well as by more than 700 universities and research institutes.

Julia claims to be the only high-level dynamic programming language to have reached a new level of performance. It achieved 1.5 petaflops per second using 1.3 million threads, 650,000 cores, and 9,300 Knights Landing (KNL) nodes to catalog 188 million astronomical objects in only 14.6 minutes on the sixth most powerful computer in the world.

Julia’s high-level syntax makes it easy to learn by people with and without a programming background. The language supports Unicode, which allows a programmer to use Unicode characters as variables rather than having to remember the keyboard combinations for variables used in Python and MatLab for mathematical expressions.

In addition, Julia programs compile on a range of platforms, including Windows, MacOS, and Linux.

Despite having been in development for six years prior to its release in 2018, Julia lacks the community support that Python and other open-source programming languages benefit from. While a 2019 survey of Julia users found that 93% like the language overall, they did identify areas where Julia comes up short.

This was the top complaint of Julia users surveyed by the vendor in 2019. Towards Data Science identifies three shortcomings of particular note:

Julia’s plotting tools, especially Plots.jl, suffer from long compile times. The problem is blamed on two issues:

Towards Data Science notes that it’s possible to create executables in Julia, but the process is more convoluted than in other languages. Two options are available for packaging and compiling binaries in Julia:

Unfortunately, neither approach is easy to implement, particularly when compared to packaging executables in competing languages.

The Scala programming language is described as a “Java-like programming language” that is noteworthy for its ability to run on the Java Virtual Machine (JVM). Scala was developed by Martin Odersky as a language that can link to existing Java systems rather than as a Java extension. This means Scala can leverage Java tools and libraries, compile to Java byte code, but be developed separately to address the shortcomings of Java, according to DZone. The first version of Scala was released in 2004.

Scala is an open-source language that combines object-oriented and functional programming in a statically typed environment, as software vendor Appinventiv explains. It’s used by companies such as LinkedIn, Tumblr, Apple, Sony, and SoundCloud. Scala is noted for its scalability (its name is derived from “Scalable Language”), its efficient code, and its ability to run on many different platforms.

Scala is an object-oriented language because every value is an object, yet it is also a functional language in that every function is a value. Industry learning source Baeldung describes Scala as a “pure object-oriented language” because it doesn’t support primitive data types, which Java does support. With Scala, developers can define classes, objects, and methods while also being able to use such functional programming features as traits, algebraic data types, and type classes.

Scala is available to download for free from the Scala site. It requires the Java 8 JDK (Java Development Kit) or Java 11 JDK. It is released under the Apache License, version 2.0.

Scala is statically typed, which means a variable’s type has to be known at compile time rather than at run time. This increases the chances that the program will run as expected. The Scala type system supports the following functions:

Despite being decades old, Java remains the second-most popular programming language on the TIOBE index. However, Java has many shortcomings in terms of performance, usability, and code efficiency. Also, Java requires a commercial license.

Scala is presented as an alternative to Java. It allows developers to take advantage of the broadly installed Java infrastructure and the many Java-based tools while gaining performance, productivity, and efficiency via an open-source language.

By supporting Java libraries and other features, Scala lets developers retain their existing Java resources and skills while improving the development process and generating more efficient code. (While JVM is Scala’s primary platform, it also supports Scala.js and Scala Native.)

The Scala compiler converts Scala programs into a set of Java classes or a Microsoft Intermediate Language (MSIL) file for running on the .NET framework. A primary benefit of JVM is that it runs on all major platforms, so compiled Java and Scala programs can run on the platforms without having to be recompiled.

Even though Ruby is a dynamically typed language and Scala is a statically typed language, the two systems use similar syntax that makes their code more efficient. Web and mobile developer Thoughtbot explains that both Scala and Ruby mix functional and object-oriented programming, although Scala leans more toward the functional than Ruby does.

Apache Kafka and Apache Spark are popular big data frameworks that data scientists use commonly to build machine learning models, as software vendor DeZyre explains. While Python, Java, and R can be used with Spark, Scala is the language of choice for several reasons.

As a hybrid functional/object-oriented language, Scala can be difficult for developers to learn. Scala can also run slowly when compiling complex code.

These are other limitations of the language for data science.

Scala type classes use parametric and ad hoc polymorphism to address problems associated with object-oriented polymorphism, such as when implementing a new behavior for an existing or new class, as programmer Alexey Novakov explains. This increases the complexity of the type classes and other type information.

Scala has been in use for several years, but its popularity has increased with the rise of big data analytics. The Scala developer community lacks the breadth and depth of the programmer communities who support Python, Java, and other languages that have been used for several decades. In addition, the range of applications created by Scala developers is narrower than those of Python, Ruby, Java, and other general-purpose development environments.

Despite the growing demand for Scala developers, many programmers who currently use other languages may hesitate to learn Scala because other languages such as R and Python are easier to learn. Even though Scala runs on JVM, it differs from Java in that it supports both object-oriented and functional programming. This means that Java programmers will need to be trained to use Scala.

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Demand continues to grow for data scientists who possess the programming skills to excise timely and relevant business intelligence from the oceans of data being collected and stored in corporate databases. Understanding the strengths and weaknesses of various types of programming languages for data science helps data professionals choose the best path for their career goals.

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GitHub, “The State of the Octoverse: Machine Learning”

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Towards Data Science, “Kotlin vs. Scala: Which Is Right JVM for 2020?”

Towards Data Science, “Programming Languages for Data Scientists”