Data science has become one of the most important technologies of the 21st Century. With the high demand for human resources in this industry, it requires many data scientists equipped with the necessary skills.
Besides mathematical skills, data scientists need to master some programming languages. But before acquiring the expertise, an aspiring data scientist identifies the types of programming languages needed for the job.
The following article provides readers with some of the programming languages needed to become a data scientist.
Top 6 important programming languages for data science
Python is a high-level object-oriented programming language used to develop websites and various applications. It is easy to learn and is emerging as one of the best introductory programming languages for first-timers to programming languages.
Python has powerful high-level data structures and a simple yet effective approach to object-oriented programming. Python's command syntax is a huge plus as its clarity, ease of understanding, and flexible typing make it quickly an ideal language for scripting and application development in a wide variety of fields, in most areas. all platforms.
R is a very powerful tool for machine learning, statistics, and data analysis. Therefore, it is very popular among statisticians. If you want to dive into data analysis and statistics, then R is the language for you.
The only limitation of R is that it is not a general-purpose programming language, which means it cannot be used for tasks other than statistical programming.
With over 10,000 packages in CRAN's open source repository, R caters to all statistical applications. Another strong fit of R is its ability to handle complex linear algebra. This makes R ideal for not only statistical analysis but also for neural networks.
There are also other studio packages like Tidyverse and Sparklyr that provide an Apache Spark interface to R-based environments like RStudio which has made database connectivity easier.
It has a built-in package called “RMyQuery”, which provides R native connectivity to MySQL. All these features make R an ideal choice for data scientists.
Scala is an extension of the Java that works on the JVM. It is a general-purpose programming language that has the features of object-oriented technology as well as a functional programming language.
You can use Scala in conjunction with Spark, a Big Data platform. This makes Scala the ideal programming language when dealing with large volumes of data.
One of Scala's most important features is its ability to support large-scale parallelism. However, the Scala programming language has a complicated learning curve and we don't recommend it for beginners.
Ultimately, if your preference is a data scientist who is dealing with a large volume of data, then Scala + Spark is your best bet.
SQL is the most important skill a data scientist must have. SQL or ‘Structured Query Language’ is a language for querying data from organized data sources.
In Data Science, SQL is for updating, querying, and manipulating databases. As a data scientist, knowing how to get data is very important in their job.
SQL is the standard language for relational database systems. All relational database management systems (RDMS) such as MySQL, MS Access, Oracle, Sybase, Informix, Postgres, and SQL Server use SQL as the standard database language.
Julia is a recently developed programming language that is best suited for technical computing. Julia is popular because it's as simple as Python and as fast in performance as C. This makes Julia an ideal language for fields that require complex mathematical operations.
Julia was born and quickly became one of the languages with the ability to operate quickly on large data sets. In a nutshell, Julia tackles any common mistakes made by other programming languages that aren't specifically designed for data science.
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. SAS language developers have developed their own suite of software for advanced analytics, predictive modeling, and business intelligence.
SAS is highly reliable and has been highly regarded by experts and analysts. Companies looking for a stable and secure platform, SAS is a programming language that perfectly suits their requirements.
Although SAS may be a closed source software, it provides a wide range of libraries and packages for statistical analysis and machine learning.
SAS has a great support system for your company. However, SAS fell behind with the advent of advanced and open source software. That makes it difficult and expensive to incorporate the more advanced tools and features in SAS that modern programming languages offer.
Learning these 6 programming languages will give you a certain foothold in the field of data science. Although there is no specific order of preference for each of these programming languages, I think you will probably want to learn more than one. This helps your knowledge base become more flexible and become a true data scientist.