Top 6 Data Science Programming Languages for 2019
Data Science has become one of the most popular technologies of the 21st Century. With a high demand for Data Scientists in industries, there is a need for people who possess the required skills in order to become proficient in this field. Besides mathematical skills, there is a requirement for programming expertise. But before gaining expertise, an aspiring Data Scientist must be able to make the right decision about the type of programming language required for the job. In this article, we will go through some of the required data science programming languages in order to become a proficient Data Scientist.
Keeping you updated with latest technology trends, Join DataFlair on Telegram
Introduction to Data Science
Programming forms the backbone of Software Development. Data Science is an agglomeration of several fields including Computer Science. It involves the usage of scientific processes and methods to analyze and draw conclusions from the data. Specific programming languages designed for this role, carry out these methods. While most languages cater to the development of software, programming for Data Science differs in the sense that it helps the user to pre-process, analyze and generate predictions from the data. These data-centric programming languages are able to carry out algorithms suited for the specifics of Data Science. Therefore, in order to become a proficient Data Scientist, you must master one of the following data science programming languages.
Best Data Science Programming Languages
Here is the list of top data science programming languages with their importance and detailed description –
It is easy to use, an interpreter based, high-level programming language. Python is a versatile language that has a vast array of libraries for multiple roles. It has emerged out as one of the most popular choices for Data Science owing to its easier learning curve and useful libraries. The code-readability observed by Python also makes it a popular choice for Data Science. Since a Data Scientist tackles complex problems, it is therefore, ideal to have a language that is easier to understand. Python makes it easier for the user to implement solutions while following the standards of required algorithms.
Python supports a wide variety of libraries. Various stages of problem-solving in Data Science use custom libraries. Solving a Data Science problem involves data preprocessing, analysis, visualization, predictions, and data preservation. In order to carry out these steps, Python has dedicated libraries such as – Pandas, Numpy, Matplotlib, SciPy, scikit-learn etc. Furthermore, advanced Python libraries such as Tensorflow, Keras and Pytorch provide Deep Learning tools for Data Scientists.
For statistically oriented tasks, R is the perfect language. Aspiring Data Scientists may have to face a steep learning curve, as compared to Python. R is specifically dedicated to statistical analysis. It is therefore, very popular among statisticians. If you want an in-depth dive at data analytics and statistics, then R is the language of your choice. The only drawback of R is that it is not a general purpose programming language which means that it is not used for tasks other than statistical programming.
With over 10,000 packages in the open-source repository of CRAN, R caters to all statistical applications. Another strong suit of R is its ability to handle complex linear algebra. This makes R ideal for not just statistical analysis but also for neural networks. Another important feature of R is its visualization library ‘ggplot2’. There are also other studio packages like tidyverse and Sparklyr which provides Apache Spark interface to R. R based environments like RStudio has made it easier to connect databases. It has a built-in package called “RMySQL” which provides native connectivity of R with MySQL. All these features make R an ideal choice for hard-core data scientists.
Referred as the ‘meat and potatoes of Data Science’, SQL is the most important skill that a Data Scientist must possess. SQL or ‘Structured Query Language’ is the database language for retrieving data from organized data sources called relational databases. In Data Science, SQL is for updating, querying and manipulating databases. As a Data Scientist, knowing how to retrieve data is the most important part of the job. SQL is the ‘sidearm’ of Data Scientists meaning that it provides limited capabilities but is crucial for specific roles. It has a variety of implementations like MySQL, SQLite, PostgreSQL etc.
In order to be a proficient Data Scientist, it is necessary to extract and wrangle data from the database. For this purpose, knowledge of SQL is a must. SQL is also a highly readable language, owing to its declarative syntax. For example SELECT name FROM users WHERE salary > 20000 is very intuitive.
Scala stands is an extension of Java programming language operating on JVM. It is a general-purpose programming language having features of an object-oriented technology as well as that of a functional programming language. You can use Scala in conjunction with Spark, a big data platform. This makes Scala an ideal programming language when dealing with large volumes of data.
Scala provides full interoperability with Java while keeping a close affinity with Data. Being a Data Scientist, one must be confident with the use of programming language so as to sculpt data in any form required. Scala is an efficient language made specifically for this role. A most important feature of Scala is its ability to facilitate parallel processing on a large scale. However, Scala suffers from a steep learning curve and we do not recommend it for beginners. In the end, if your preference as a data scientist is dealing with a large volume of data, then Scala + Spark is your best option.
Julia is a recently developed programming language that is best suited for scientific computing. It is popular for being simple like Python and has the lightning-fast performance of C language. This has made Julia an ideal language for areas requiring complex mathematical operations. As a Data Scientist, you will work on problems requiring complex mathematics. Julia is capable of solving such problems at a very high speed.
While Julia faced some problems in its stable release due to its recent development, it has been now widely being recognized as a language for Artificial Intelligence. Flux, which is a machine learning architecture, is a part of Julia for advanced AI processes. A large number of banks and consultancy services are using Julia for Risk Analytics.
Like R, you can use SAS for Statistical Analysis. The only difference is that SAS is not open-source like R. However, it is one of the oldest languages designed for statistics. The developers of the SAS language developed their own software suite for advanced analytics, predictive modeling and business intelligence. SAS is highly reliable and has been highly approved by professionals and analysts. Companies looking for a stable and secure platform use SAS for their analytical requirements. While SAS may be a closed source software, it offers a wide range of libraries and packages for statistical analysis and machine learning.
SAS has an excellent support system meaning that your organization can rely on this tool without any doubt. However, SAS falls behind with the advent of advanced and open-source software. It is a bit difficult and very expensive to incorporate more advanced tools and features in SAS that modern programming languages provide.
So, these were some of the programming languages for a data scientist.
Data Science is a dynamic field with ever growing technologies and tools. Since Data Science is a vast field, you must select a specific problem to tackle. For this, you should select the programming language best suited for it. The programming languages mentioned above, focus on several key areas of Data Science and one must always be willing to experiment with new languages based on the requirements.
Still, if you have any query regarding data science programming languages, feel free to ask in the comment section.