7 languages essential to be a “Master of Data Science”?

Sep 22, 2017

Data amount is growing fast every day, every hour, and every minute. The speed of new information rises dramatically and by 2010, the volume of generated data is expected to reach 1.7 MB per second.

With the help of experts in Data Science, financial giants increase revenues, decrease time and costs, achieve smart decision-making, develop new products, and introduce best offers on the market.

What skills should IT professionals have to organize eternal processing of streaming data volumes? Let’s talk about programming languages and consider their pros and cons for Data Science.

Python was introduced by Guido van Rossum in 1991 as a compromise between the preciosity of R. Now it is one of the most popular programming languages among engineers processing huge volumes of data. It has a wide range of purpose-built modules and its online services provide API.

What’s so good about Python?

It has a free license and it’s a good thing.  What’s even better is that Python is an easy language to learn because of low barrier to entry.

Any cons?

Type errors might occur rather frequently, for example, passing a String as an Argument to a method.

Java is one of the most popular languages in the world, although it doesn’t have the same opportunities as Python and R have. Thus, in case wireframing is not the main point, Java might become a perfect solution.

Why Java is a good choice?

It is a complied programming language with high performance suitable for general purpose. A lot of systems and apps are built upon Java back-end.

Why Java is not the first choice for data science?

There are not enough libraries for statistical methods in Java. Python and R would be much more productive in processing huge amounts of data.

Pros and Cons

Martin Odersky developed multi-paradigm Scala in 2004. It runs on JVM and provides object-oriented and functional approaches. It’s main advantage is flexibility that makes Scala suitable to work with streaming data.

Scala is not a straightforward language with complex syntax and type system. However, when it comes to processing Big Data on clusters, Scala in combination with Spark makes some smashing effect.

Julia is a “young” programming language released in 2012. Stefan Karpinski was pretty unsatisfied with the R programming language and MatLab. He was one of three Julia founders.

Why Julia?

Free license is an advantage. It’s easy adjusted to scale like Python and as efficient as R. Julia provides perfect performance, dynamic typing, and scripting capabilities.

As a recently created language, it still has gaps to be eliminated – Julia is short of libraries and has no advanced support. However, being five years old, it’s already on radars of Big Data developers and has perfect future prospects.

R is probably one of the most popular programming languages used in Data Science.

It is a descendant of the old S. The language is created by Ross Ihaka and Robert Gentleman at the University of Auckland (New Zealand) in the 90s and is currently supported by R Foundation for Statistical Computing.

Why R is good?

R has a free license and a good range of high-standard open source and domain specific packages, which are suitable for almost any quantitative and statistical app. Non-liner regression, neural network, advanced plotting, and so on are great advantages that R includes. Data visualization comes with the use of libraries such as ggplot2. R also supports matrix algebra pretty well.

Not ideal R

First of all, the performance leaves much to be desired. R is slow and quite specific – indexes from 1 and it has unconventional data structures. However, the fact of its growing popularity shows that the language is efficient for data science and statistics.

MATLAB was developed by MathWorks in 1984. It is a numerical computing language with no free license. Matlab is a perfect choice for quantitative apps that require matrix algebra, image and signal processing, and Fourier transforms.

Perfect choice

MATLAB is ideally suitable for Data Science, processing streaming data, and data analysis. The only “con” is an expensive license.

Structured Query Language was launched in 1974 to define, manage, and query relational databases.

SQL is a readable language due to declarative syntax. It is more suitable for data processing and data analysis.

Which functions are limited?

SQL is perfect in aggregating, counting and summing data, and that’s it.

To become a master in any field, we should constantly learn new techniques. When it comes for programming languages, you should keep on learning various packages and modules in your language, as it’s main goal is to insure 2 Ps: productivity and performance.

Next time we’ll talk about Hadoop and Kafka. Stay connected not to miss.

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