AI News, Comparison of top data science libraries for Python, R and Scala [Infographic]
Comparison of top data science libraries for Python, R and Scala [Infographic]
Each of these languages is suitable for a specific type of tasks, besides each developer chooses the most convenient tool for himself.
Primarily designed for statistical computing, R offers an excellent set of high-quality packages for statistical data collection and visualization.
Keep in mind, that the choice of programming language and the libraries that you will use, depends on specific tasks, so it’s beneficial to know what are the strong and weak sides of each of them.
Indeed, this list is not complete, many other valuable tools can and have to be examined, but it will definitely be a good starting point for your journey into data science industry.
Which Languages Should You Learn For Data Science?
Data Science is an exciting field to work in, combining advanced statistical and quantitative skills with real-world programming ability.
Your success as a data scientist will depend on many points, including: Specificity When it comes to advanced data science, you will only get so far reinventing the wheel each time.
Much of the day-to-day work in data science revolves around sourcing and processing raw data or ‘data cleaning’.
Performance In some cases it is vital to optimize the performance of your code, especially when dealing with large volumes of mission-critical data.
With these core principles in mind, let’s take a look at some of the more popular languages used in data science.
In approximately order of popularity, here goes: Released in 1995 as a direct descendant of the older S programming language, R has since gone from strength to strength.
is a powerful language that excels at a huge variety of statistical and data visualization applications, and being open source allows for a very active community of contributors.
It has since become an extremely popular general purpose language, and is widely used within the data science community.
Varies — some implementations are free, others proprietary SQL is more useful as a data processing language than as an advanced analytical tool.
Yet so much of the data science process hinges upon ETL, and SQL’s longevity and efficiency are proof that it is a very useful language for the modern data scientist to know.
Many companies will appreciate the ability to seamlessly integrate data science production code directly into their existing codebase, and you will find Java’s performance and and type safety are real advantages.
Yet if your application doesn’t deal with the volumes of data that justify the added complexity of Scala, you will likely find your productivity being much higher using other languages such as R or Python.
Proprietary — pricing varies depending on your use case MATLAB’s widespread use in a range of quantitative and numerical fields throughout industry and academia makes it a serious option for data science.
Verdict — “a useful general purpose scripting language, yet it offers no real advantages for your data science CV” Ruby is another general purpose, dynamically typed interpreted language.
Verdict — “not an obvious choice yet for data science, but won’t harm the CV” Well, there you have it — a quickfire guide to which languages to consider for data science.
The key here is to understand your usage requirements in terms of generality vs specificity, as well as your personal preferred development style of performance vs productivity.
These languages give the right balance of generality and productivity to do the job, with the option of using R’s more advanced statistics packages when needed.
Which freaking big data programming language should I use?
You understand the problem domain, you know what infrastructure to use, and maybe you've even decided on the framework you will use to process all that data, but one decision looms large: What language should I choose?
You'd construct a model in R, but you would consider translating the model into Scala or Python for production, and you'd be unlikely to write a clustering control system using the language (good luck debugging it if you do).
As a result, if you have a project that requires NLP work, you'll face an embarrassing number of choices, including the classic NTLK, topic modeling with GenSim, or the blazing-fast and accurate spaCy.
For example, new features in Spark will almost always appear at the top in the Scala/Java bindings, and it may take a few minor versions for those updates to be made available in PySpark (especially true for the Spark Streaming/MLLib side of development).
This splits people between 'this is great for enforcing readability' and those of us who believe that in 2016 we shouldn't need to fight an interpreter to get a program running because a line has one character out of place (you might guess where I fall on this issue).
Running on the JVM, Scala is a mostly successful marriage of the functional and object-oriented paradigms, and it's currently making huge strides in the financial world and companies that need to operate on very large amounts of data, often in a massively distributed fashion (such as Twitter and LinkedIn).
As it runs in the JVM, it immediately gets access to the Java ecosystem for free, but it also has a wide variety of 'native' libraries for handling data at scale (in particular Twitter's Algebird and Summingbird).
But given that it has a Turing-complete type system and all sorts of squiggly operators ('/:' for foldLeft and ':\' for foldRight), it is quite easy to open a Scala file and think you're looking at a particularly nasty bit of Perl.
But while they're straining to sort out their nest of callbacks in their Node.js application, using Java gives you access to a large ecosystem of profilers, debuggers, monitoring tools, libraries for enterprise security and interoperability, and much more besides, most of which have been battle-tested over the past two decades.
While you shouldn't go overboard (your team will quickly suffer language fatigue otherwise), using a heterogeneous set of languages that play to particular strengths can bring dividends to a big data project.
What is the best programming language for Machine Learning?
By Christina Voskoglou Q&A sites and data science forums are buzzing with the same questions over and over again: I’m new in data science, what language should I learn?
We turned instead to our hard data from 2,000+ data scientists and machine learning developers who responded to our latest survey about which languages they use and what projects they’re working on — along with many other interesting things about their machine learning activities and training.
Little wonder, given all the evolution in the deep learning Python frameworks over the past 2 years, including the release of TensorFlow and a wide selection of other libraries.
We asked our respondents about other languages used in machine learning, including the usual suspects of Julia, Scala, Ruby, Octave, MATLAB and SAS, but they all fall below the 5% mark of prioritisation and below 26% of usage.
Our data reveals that the most decisive factor when selecting a language for machine learning is the type of project you’ll be working on — your application area.
In our survey we asked developers about 17 different application areas while also providing our respondents with the opportunity to tell us that they’re still exploring options, not actively working on any area.
Network security and fraud detection algorithms are built or consumed mostly in large organisations — and especially in financial institutions — where Java is a favourite of most internal development teams.
In areas that are less enterprise-focused, such as natural language processing (NLP) and sentiment analysis, developers opt for Python which offers an easier and faster way to build highly performing algorithms, due to the extensive collection of specialised libraries that come with it.
Here a lower level programming language such as C/C++ that comes with highly sophisticated AI libraries is a natural choice, while R, designed for statistical analysis and visualisations, is deemed mostly irrelevant.
Second to the application area, the professional background is also pivotal in selecting a machine learning language: the developers prioritising the top-five languages more than others come from five different backgrounds.
Embedded computing hardware engineers are also the most likely to be working on near-the-hardware machine learning projects, such as IoT edge analytics projects, where hardware may force their language selection.
Developers who say that they got into machine learning because data science is/was part of their university degree are the least likely to prioritise Python (26%) and the most likely to prioritise R (7%) as compared to others.
There is evidently still a favourable bias towards R within statistics circles in academia — where it was born — but as data science and machine learning gravitate more towards computing, the trend is fading away.
C/C++ is prioritised more by those who want to enhance their existing apps/projects with machine learning (20%) and less by those who hope to build new highly competitive apps based on machine learning (14%).
When building a new app from scratch — especially one using NLP for chatbots — there’s no particular reason to use C/C++, while there are plenty of reasons to opt for languages that offer highly-specialised libraries, such as Python.
- On Saturday, August 24, 2019
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