How to Choose the Correct Programming Language to train Your Staff in Data Science?

Presently, most of the organizations are adopting Data Science. Businesses from every sector are continuously looking for Data Science professionals, and at the same time, they are capitalizing heavily on Data Science education programs. Data science skills can bring enormous value to each sector, ranging from banks and retailers, charities, and government. When implementing such an initiative, the organization should plan for a lot of strategic decisions. Organizations have many questions in their mind related to Data Science skills; one of the most popular queries is “which programming language they should teach to their staff?” The Data Science community has frequently discussed this issue, and a fast Google search will disclose a lot of articles and videos discussing the technical benefits and grace of each programming language. However, from a business point of view, the decision typically relies on different factors. Here is some list of questions and answer which help organizations decide which programming language they should teach their employees for data science.

1. The very first question is “Which programming language should staff learn for data science or data analysis, Python or R?”

Currently, both languages are the most popular and adaptable. Both of them are free and open source. Each of the languages has several libraries which streamline the process of transforming, exploring, examining and imaging data. A recent survey shows that R and Python seem to be at the top list of languages for Data Science. Around 76.3% of respondent are using Python, and 59.2% are utilizing R.  The other programming languages such as C/C++, MATLAB, and JAVA are on the list of less used languages. All of these languages hold their own benefits, but when it comes to learning data science, these languages are not that much flexible, manageable, and well-supported.

2. Another question is “which language is recommended for learners Python or R?”

Well, the significant factor is whether the current Data Science teams already prefer Python or R. There is a great benefit of working within a reliable environment due to the following reasons:

• If learners face any issues, they can ask for an existing internal support network.
• Once the code is developed, it can be shared and reused among teams.
• Installation and Whitelisting will possibly be simpler for the IT team.

Thus, it is recommended if the current Data Scientists of organizations are using Python, then go with Python. If they devote more time in R, then choose the R programming language.

3. What happens if the Organization does not have an existing Data Science team, or they don’t have a preference? Then which language is recommended?

Well, in this situation Python is the most suitable language. Python is one of the most popular and rapidly growing Data Science languages. There are enhanced online resources available for learning and troubleshooting Python language. Also, there is a vast range of code libraries which support the common tasks of modern data analytics.  IT team can efficiently manage Python as compared to R. Not only on Data Science, Python is also well known in other perspectives separate from Data Science, for example, Web development. This implies that learners can approach an extensive scope of educational assets and can apply it in different parts of their life.

4. What are the benefits of R?

A survey from Kaggle’s discovered that though Python was the most popular language in all and R was more common among statisticians. R programming language can be a good choice of the programmers because it has been intentionally developed for statistical analysis. This implies that numerous common analytical tools are developed into R; however, Python depends on external packages. Usually, R also had a superior range of statistical and specialist visualization packages, but as soon as Python has become more popular language, it has started to catch up. Currently, whatever thing you can perform in R can be easily simulated in Python.

Well, apart from Python and R there are other languages as well, which a Data scientist can choose, they are:

Java: Java is one of the oldest selections of languages among Data Scientists. Though many new languages have challenged the presence of Java, it never fails to beat them. Java has a unique feature, i.e., write once and run wherever. Once the code is compiled in Java, it can be executed on any platform that supports Java. Therefore, portability is one of the great features of Java. The Java Virtual Machine (JVM) is an excellent tool for Data Science.  There have been two great enhancements in Java: they are Lambda support and REPL support. Thus, Java is a must-learn language for promising Data Scientists.

Scala:  It is one of the best-identified languages with one of the primary user foundation. The Scala has a big user interface. Initially, it was created to run on Java. Therefore, all other platforms which support Java can also run Scala. It is user-friendly and can be changed according to the user’s requests. Thus, it is perfect for high-level coding algorithms.

SQL: SQL is the other favorite language among Data Scientist. It is basically used for large databases. Mostly, it is helpful in handling structured data. Learning SQL can be a good addition to the skills needed to become a Data Science professional. But, there is only a drawback related to this language, i.e., the lack of portability.

Julia: This is a high-level dynamic programming language which is created to address all the numerical and computational requirements; therefore, this language is highly suitable for Data Scientists.  Julia has a distinctive feature i.e., it’s a base library which is integrated with open source C and FORTRAN libraries for linear algebra, random number generation, signal processing, etc.  The association between Jupiter and Julia communities provides an influential browser-based graphical notebook interface to Julia.

Conclusion

From the above points, it has been observed that around 76.3% of respondents are using Python, whereas 59.2% are using R programming language for Data Science. Thus, there is a huge intersection between the communities. Most of the Data scientists are comfortable using both languages.  If the employees learn one language, then it will be easier for them to pick up other languages. Also, the employees can learn a lot more beyond the programming such as mathematics and statistical concepts, to data pipelines and visualization. With the help of any language, it is easy to make an excellent foundation to search these topics and start your voyage as a Data Scientist.