Last Update made on August 19,2016
In the astronomically growing cyberspace of the 21st century, coding is (and will continue being) a hot skill. If you are an experienced programmer, you probably know the way of the world by now and would be smart enough to decide which programming language best compliments and upgrades your existing skill set. Still, spending 10 minutes of your schedule reading through this article won’t harm, as you are likely to discover something you didn’t already know. By and large, this article is targeted at the beginners in data science who have a passion for coding but do not really know where to start – or what to start with?
One of the most popular question that is asked by anyone who is beginning with a career in Data Science is – “What programming languages should I learn to get started in the field of data science?” or “What are the best programming languages for data science?” For people who get confused in choosing the best data science programming languages to learn, DeZyre has come up with a good choice of languages that every beginner in data science must learn -to start with programming. The top programming languages for data science listed in this article are not based on any specific criteria but have been selected based on their popularity in the data science community. Whether you are a data science rookie or a seasoned data scientist, it is good practice to learn all these programming languages to get a good grasp on all data science concepts.Here we have put together a list of 10 programming languages that would give you a broad foundation for becoming a data scientist, from where you would be able to build your way up into the world of data science with relative ease.
Conceptualized by Oracle, Java has strongly emerged as the programming language of choice for developers around the world. The biggest benefit it offers it that once compiled, it can be executed across platforms, thus eliminating the need for language dependent compilers. Java also tops the popularity charts on tech websites such as Mashable and ITworld. The IT industry is fast migrating from a Software Provider to providing on-demand software through the Software as a Service (SaaS) framework. Java users are equally desired in both the models. Java programmers continue to be in demand even as the programming world shifts to an all new domain of SMAC (Social, Mobility, Analytics and Cloud). It is visualized as the programming language of choice for ground breaking domains such as Internet of Things (IoT), apart from enterprise architecture and cloud computing.
Java’s unflinching popularity is also indicated by the fact that eFinancialCareer survey found it to be most in-demand programming language on Wall Street in 2015. Java’s importance was underlined by the ongoing patent battle between Oracle and Google, in which Oracle sued Google in 2010 for allegedly incorporating parts of Java into its Android Operating System.
It finds wide application in our day to day life such as software (MS-Word), embedded systems of electronic devices such as car dashboards, television firmware, airplane applications, etc. This is one reason C still finds itself as one of the introductory languages in undergraduate level computer/electrical/electronic engineering courses.
According to the O’Reily 2015 Data Science Salary Survey, the use of Scala programming language for data science increased by 10% in 2016. Scala programming language is a fusion of object oriented and functional programming languages that helps build robust and scalable data science applications. As organizations aspire to work with growing amount of real-time data, Scala programming language helps data scientists write short and expressive code whilst delivering high performance and typesafe applications that are impressive and valuable. Scala for data science requires a little extra knack of abstraction and thinking. However, once a data scientist becomes familiar with its high level functional features, productivity boosts dramatically. Scalability and number crunching abilities of Scala have made it one among the best programming languages for data science.
Most of the data science projects today follow an agile methodology, data scientists want to change the requirements of the code as they perform data explorations so that they can adjust them at each iteration. Usually, data scientists first write some code with associated tests and then after the tests are complete, the APIs are broken. Every time a data scientist performs refactoring, there is a probability of introducing new bugs and wordlessly breaking the previous coding logic. Scala being a compile language has better advantage in terms of safe refactoring over other data science programming languages like Python.
It is said that there is no short cut to success but if you are a quick learner and want to get up and running with a widely used, easy to learn programming language, Python is what you are looking at. Its USP is readability and compactness. It enables programmers to express same concepts in shorter code fragments. Developers coming from diverse programming backgrounds and used to different styles (object oriented, imperative, functional, procedural) find it easy to adapt to Python. It allows easier scalability which makes it equally suited to handle small scale and large scale applications.
Modern day applications such as Pinterest and Instagram are built using Python. It is rapidly gaining popularity at the academic level and finds itself amongst the most commonly taught programming language in U.S. schools.
SAS (Statistical Analysis System) is a market leader in commercial analytics space with highest share in private organization .It has gained popularity in the data science community because of its wide range of statistical functions with a user friendly GUI that helps data scientists learn quickly. However, the only loophole why most data scientist do not prefer to use SAS is that it is one of most expensive option for doing data science unlike Python and R programming which are open source and freely available. Unless the organization with which you are working uses SAS, as an individual accessing SAS programming language is a costly affair.
For people who already know SQL, SAS is easy to learn programming language as it provides an easy option like PROC SQL. SAS language has about 70% of the market share in terms of job trend over Python and R programming language which together hold 20% of the job market share. For beginners entering in the analytics industry, SAS should be the preferred programming language for you to learn along with Python and however, learners can download the freely available University Edition from here to learn the language.
R is a considerable deviation from the languages we have discussed so far. It is not a substitute for any of the languages we’ve already discussed. R is essentially a dedicated language for statistical computing and graphics. Given the way data is being generated in the 21st century, R has become the favourite language for data analysts and scientists around the world. R has been ranked at No. 6 in the IEEE’s Top 10 Programming Language of 2015 and with the growing influence of Big Data and emergence of Internet of Things, you can be assured that it will continue to be a hot skill for years to come, and beyond.
MATLAB is a must learn programming language for data science, particularly for working with matrixes. MATLAB is not an open source language but is used extensively in academic courses because of its suitability for mathematical modelling and data acquisition. Though MATLAB lacks the volume of open source community driven support, its extensive adoption in academic courses has made it popular for data science. MATLAB programming language is good for data science tasks that involve linear algebraic computations, simulations and matrix computations. LAPACK and BLAS libraries for matrix multiplication in MATLAB are highly optimized that speed up execution. However, MATLAB imposes restrictions on code portability (ability to run code on other computer). Data scientists can run compiled application on other computer using the MCR (MATLAB Component Runtime) components, but the app must have the same version of MCR installed.
CrowdFlower analysis shows that 10%-15% of data scientist job listing require this MATLAB programming skill.
GO is a new comer in the world of data science but its gaining steam because of its simplicity. Golang developed at Google by group of engineers who were frustrated with the use of C++, is an open source language based on C. GO has not been developed particularly for statistical computing but has gained mainstream presence for data programming because of its speed and familiarity. Data scientists can call routine programs, written in other programming languages like Python to make function calls that it cannot accommodate within itself.
Structured Query Language (SQL) has been at the heart of storing and retrieving data for decades. It is used to filter relevant information from an ocean of data. It is difficult to imagine a modern day application that does not use a database at its backend for storing huge amounts of data. SQL is a language that helps you interact with that database. When used with sufficient expertise, SQL can considerably reduce the turnaround time for online requests and queries by extracting only the relevant part of data and processing it rather than processing entire database tables. Microsoft and Oracle have their own versions of it but conceptually speaking, learning any one of these, should be as good as learning the other.
This list of 10 data science programming languages is not meant to be exhaustive or the most comprehensive. While compiling the list, a beginner’s frame of mind is used as a reference point and we have tried to come up with a list that has 10 elements which would give a beginner necessary depth and width required for developing big data. We hope you’ll have fun learning these and add to it.
All the logos for the programming languages have been taken from respective official websites.