A programming language is a formal type of language that comprises instructions that generate different types of output depending on the given instructions. Note that this type of language is applied in computer programs to implement various algorithms and is used in tons of applications.
What is Data Science?
Data science is a broad field incorporating the combination of scientific methods, data analysis, Artificial Intelligence, and statistics to extract valuable information from a given data. The individuals who took part in data science and referred to as data scientists. Note that these individuals use a vast combination of skills to make this successful.
Low-Level Vs. High-Level Programming Languages
You need to understand that a high-level programming language works from a user-oriented perspective and is designed in a way that is easy to execute and convert an algorithm into an easily read program code.
A low-level programming language is a machine-based type of language. The low-level program is termed as machine operations that are performed before carrying out any task.
Differences Between Low-Level and High-Level Programming Language
- Programmers easily comprehend high-level programming language compared to the machine itself. Contrary, machines understand low-level language compared to human beings.
- High-level programming language is easy to debug compared to the low-level language.
- High-level language requires an interpreter to translate it into machine code, while low-level language needs an assembler to translate the instructions.
Top 10 Programming Languages in Data Science
Python
This is one of the programming languages that has gained popularity among most data scientists. It is considered the most reliable alternative in a wide variety of tasks and domains. It is mainly applied in machine learning, artificial intelligence, and other popular technology forms.
Also, Python supports other vital operations such as modeling and visualization, analysis, data collection, and other significant areas in big data. You can create multiple complex visualizations like Sankey Chart, Pareto Analysis chart, Scatter Plot etc. using different libraries offered in Python.
As per Python Developers Survey 2020 Results by JetBrains 85% of the survey respondents use Python as their main programming language.
JavaScript
JavaScript is ranked among the popular programming languages that people are learning daily. This language is mostly applied in website development due to its nature and potential for creating interactive web pages. Besides, it is an incredible choice in developing visualizations in big data.
However, this programming language does not have the data science operating packages and in-built functionalities compared to other languages.
Scala
This is an exceptional general-purpose language perfectly suited in data science operations. If you are looking to begin a career journey in data science, this is the best option. Scala is the best alternative, especially if you are working with large volumes of data. It can also be implemented in Spark to manage large volumes of siloed data.
In addition, Scala has a concurrency support backup that makes it a unique choice for data scientists. It has a vast number of libraries and an endless number of functionalities to choose from. You need to choose Scala if you have a good understanding of Java.
R
Within the past few days, R has gained popularity towards the helm of other programming languages. This is because it is highly extensible and easy to learn and comprehend. It creates a good environment for graphics and statistical computing for its users.
Analysis has revealed that R is the most powerful scripting language available. It can handle large volumes of complicated data sets. It is mainly used in data science, especially in things that entail statistical operations.
SQL
To become an exceptional data scientist, you need to learn and comprehend SQL. This is vital since all data scientists require SQL skills to handle structured forms of data. This language type gives you access to statistics and data, thus being an essential resource in data science.
Note that SQL is a standard and the most preferred programming language when it comes to relational databases.
Julia
Julia is a programming language that was specifically meant to be used in computation and numerical analysis. This language offers versatility supporting both parallel and distributed computing. It works fast enough to enhance interactive computing and can easily switch to low-level if the need arises.
Java
Even though this name appears on JavaScript, Java is completely a different thing. It is mostly applied in android apps, desktop applications, credit card programming, among other applications. Note that there is a big difference in how Java and JavaScript are written, assembled, and executed.
The Java code is compiled and used in building applications that run in virtual machines and browsers, while JavaScript is in the form of text that only runs in browsers. Note that Java is an object-oriented programming type of language.
C/C++
The C/C++ programming language offers incredible skills in building statistical and data tools. Note that this programming language translates directly to Python and its functional capabilities. This language is essential since it helps in data compilation quickly. It aids in the development of highly functional tools that allow severe fine-tuning.
However, this language can be complicated to master, especially if you have never learned any programming language before. Data scientists use the C/C++ programming language when handling scalable projects.
MATLAB
This is a very powerful tool used in statistical and mathematical computing. It supports the implementation and creation of user interfaces and algorithms. MATLAB is mainly used during the process of learning data science. It is used as a knowledge stepping stone in data science. Also, learning MATLAB is an easy way of shifting into deep learning.
Note that MATLAB is primarily used in academia to learn and teach numerical analysis and linear algebra.
SWIFT
This is another powerful programming language mainly used in iOS macOS, watchOS, iPadOS, among others. SWIFT is one of the programming languages that are mostly applied in most technology researches and innovations. This programming language is currently incorporated in the production of Apple platforms.
This is because SWIFT is easy to learn and implement. Note that this is one of the programming languages considered as a basic option for all data scientists.
Conclusion
It is evident that there are many programming languages in town to learn and grasp their functionality. However, you need to research which programming language is easy to start with to create a solid foundation in data science. The reality is that programming languages are not easy to grasp; they only need an open mind and critical thinking to comprehend.
