Data science provides access to non-obvious insights, which can be used for various purposes, from facilitating improvement in the efficiency of companies, to solving global problems. Data science specialists as a result are thought of being the most in-demand and promising job roles for the upcoming years.
If an individual is interested in data science as a career, becoming knowledgeable in various programming languages is a must. This is because companies cannot solve problems using one single programming language. The skills of the user will not be thorough if he or she is not frequently applying the different data science languages. Here are the top data science programming languages investigated and presented with their practical capabilities.
There is nothing better than Python to enter the data science world. Over 60 percent of data scientists and data engineers apply Python every day in design, data analytics, and ML. Its application scope is not just restricted to the evaluation of data alone. Python also offers many GUI frameworks, and toolkits that make the development of desktop applications a breeze. Various important tasks such as data collection, modeling, analysis, and visualization are very well supported by this language compared to others. In addition, Python can be used to make libraries and tools from scratch.
Merits/demerits: This programming language is clear, intuitive, and easy to learn, and the best option for beginners. Python supports both object-oriented and procedural programming languages. However, it is comparatively slow from other languages and is rarely seen on the client side. Besides that, smartphone-based applications rarely implement it.
For graduates or data analysts seeking to earn a certification in Python programming, Certified Associate in Python Programming (PCAP), Certified Entry-Level Python Programmer (PCEP), and Certified Expert in Python Programming (CEPP) are the best data science certifications available, offering several opportunities in Python-related jobs.
Scala is an impactful general-purpose language created so that the users of data science programming languages can work on specific operations that can be done using different proceeds that offer flexibility for the development process. This language is ideal for data scientists working on high-volume data sets. It has more than 1 75,000 libraries with endless functionalities.
Merits/demerits: It is excellent and scalable to work with data analytics and it uses an expressive typing system to ensure that statistical abstraction is consistent and secure. However, it has limited community presence and offers limited backward compatibility.
C/C++ are compiled easily and used in data science as they allow programmers to have wider command of their applications. These languages have a low-level nature, which allows data scientists to dig deeper regarding certain application aspects that wouldn’t be possible otherwise.
Merits/demerits: These data science programming languages are fast and the only languages that can assemble over a gigabyte (GB) of data in less than a second. However, C/ C++ programs are unable to support dynamic memory allocation and are not secure. These are commonly used for platform-specific applications only.
Java is an object-oriented, class-based, high-level programming language designed to have only a few implementation dependencies. This language is intended to allow programmers to write once, run anywhere (WORA), which means that there is no need for recompilation and the compiled code can run on all Java supported platforms.
Merits/demerits: It is best used to create complete applications and make desktop or mobile applications incredibly easy. Java is much more efficient than other languages as uses true garbage collections while most data science programming languages delete post-execution. However, this language does not provide any backup facility making it insignificant to use by some data scientists.
SQL is a vital language to learn because it is needed by a data scientist to handle structured data. It offers variables, logical directives, looping, and so on and is a fourth-generation language whereas C++ and Java are third-generation languages. SQL is made up of statements that begin with a command or a keyword, e.g. DEVELOP, and ends with a semicolon.
Merits/demerits: SQL can be used in servers, laptops, personal computers, and even on few smartphones. Using this language, it is possible to view multiple database structures. However, it has complex interfaces and higher operating costs, creating difficulty for some data engineers or scientists to access it. Reasons Why Collaboration App is a Must to Develop for Product Owners in 2021