Data science programming languages are powerful tools for data scientists to analyze data and implement algorithms.
They provide data scientists with the ability to visualize data, create models and make predictions. Data scientists should be familiar with at least one language in order to maximize their potential as a data scientist.
There are many different programming languages available that can be used for data science, some of which include Python, R, Julia, MATLAB and SAS.
Each language has its own unique advantages and disadvantages that must be taken into consideration when choosing the best language for a given task or project.
In this article we will explore these various data science programming languages in detail so you can decide which is best suited for your needs.
Data science programming languages
Python is one of the most widely used Data Science Programming Languages along with being an open-source, easy-to-use language.
Established in 1991, this general-purpose and dynamic language supports multiple paradigms from functional to structured and procedural programming.
Python is also faster than most other Data Science Programming languages and requires less iterations to spot any data irregularities. This makes it a great option for data manipulations and learning.
Data processing and mapping are made a lot easier with the packages contained in Python; its ability to create CSV outputs from spreadsheets allows developers to easily read the data inside them.
Therefore, it’s no surprise that Python has become one of the most popular Data Science Programming Languages today.
What’s more, its object-oriented structure allows it to tackle multiple tasks simultaneously and make embedded systems for everything from small electronics to large web applications possible. It is also suitable for larger processing frameworks like Hadoop, thanks to its easy scalability.
Scala is the perfect match of modernity and elegance in the world of programming. Its inception dates back to 2003, but its impact has become widely recognized in recent years.
The primary goal for Scala was to overcome specific limitations with Java. Since then it has found applications across a variety of industries, from web programming to data analysis and machine learning.
It is well-known as a highly scalable language which enables organizations to handle big data more effectively and efficiently.
Above all, its robust support for both object-oriented and functional programming, along with concurrent synchronized processing, makes Scala an attractive offering for modern day enterprises looking for integrated solutions.
R is a high-level programming language beloved by statisticians and other data science pros alike.
Originally created as an open source software solution primarily intended for statistical computing and graphics, its use has now expanded to include several powerful applications in data science.
On top of this, it equip users with the ability to explore data sets and conduct ad hoc analysis through the array of libraries designed specifically for these purposes.
That said, R may be complex and difficult to learn with compared to Python, especially when you are dealing with loops that contain over 1000 iterations.
Making the commitment to master this invaluable skill will be sure to pay-off in the long run as its usefulness increases in almost every aspect of data analysis.
5. SQL – data science programming languages
SQL is a powerful and convenient language for managing data.
It enables users to store, manipulate, and retrieve data stored on relational databases quickly and easily, making it an invaluable tool for any organization that works with large amounts of data.
With SQL tables and queries, data scientists can streamline their workflows by constructing intricate data models more efficiently and accurately.
Considering the ever-expanding need for reliable data storage and retrieval operations, knowledge of SQL is essential for mastering the art of data science.
6. Julia – data science programming languages
For those involved in data science, Julia is a tool worth learning.
Boasting tremendous speed and performance, it can quickly implement mathematical concepts that are essential tools for numerical analysis.
Furthermore, its API can easily be embedded in other programs to open up a world of possibilities.
This makes it an invaluable asset when dealing with matrices as well as both back-end and front-end programming jobs.
Starting to learn Julia will surely help increase productivity and accuracy and get the job done faster.
Conclusion on data science programming languages
In data science, there is no one-size-fits-all programming language. Depending on the task at hand and the data scientist’s preferences, different languages can be utilized to achieve maximum efficiency and accuracy.
By understanding each language’s advantages and limitations, data scientists will be able to choose a suitable tool from their arsenal to complete any given task quickly and accurately.