Let’s dive into the most important and popular programming languages for data science that can turn out to be very beneficial when looking for a data science job.
Not only the simplest, but it is the most effective programming language that you can learn without any prior knowledge. Google uses Python to enhance its search engine capabilities. Other top-tech companies that implement python for data science include Netflix, Spotify, and Instagram. Even if you are not a data science professional, you can start with it. Python programming language finds extensive use for machine learning and artificial intelligence tasks. If you want to pursue a successful career in data science, learning python for data science is a must.
- Easy to use as it is similar to the English language
- It focuses on versatility in development and does not follow the standard configuration.
- Highly beneficial for organizations that want to generate clear reports.
- It supports OO, procedural, and functional programming methodology.
- Fun to use and easy to understand.
- Open-source language with a wide community.
- Does not interact with weak components of the mobile operating system.
- Speed of development is low compared to other languages.
- No web browser integration.
- Object-oriented programming language
- Simple and secure
- Cheap and economical to maintain.
- It is platform-independent and you can run it on any machine even without the installation of any software.
- Portable and stable.
- You have to deal with complex codes.
- Requires significant memory space.
- Does not provide a backup facility.
Being one of the modern programming languages, it is a strong competitor of Python. Scala runs on JVM and can also address the issues that come with Java. It is also used for web programming purposes and can perform complex machine learning algorithms. The best part about this programming language is that it can handle complex data queries. It not only provides support for object-oriented programming but also for functional programming. It is a fast, efficient, and compact language as compared to Java.
- Provides pretty good IDE support.
- Fun to use and easy to pick up.
- Highly functional and great for data analytics.
- Can handle big projects with accuracy and efficiency.
- Highly scalable (Its name is derived from the words, scalable and language)
- Capable of performing complex machine learning operations.
- It supports a wide range of languages and frameworks.
- Sometimes you have to deal with bugs.
- It is not completely free and open-source.
R is an open-source programming language that was built mainly for statisticians. The interesting part is its creators are also statisticians who developed it to help the community. With this programming language, you can deal with all kinds of statistical computation including the formation of charts and graphs. Although it is not as easy as Python and Java, you can learn it within a few months if you are ready to devote time. There are many libraries for data science in R that make it diverse. So, the learning can take much time if you do not have any prior knowledge because it has a steep learning curve.
- It is an open-source programming language
- Provides exemplary support for data wrangling.
- Highly compatible language.
- Platform-independent language.
- Provides eye-catching reports.
- Using R one can develop statistical tools.
- With the wide array of packages, this language appeals to a wide range of industries.
- Does not have support for dynamic and 3D technologies.
- It utilizes more memory as compared to other languages.
- Not an ideal option while dealing with big data.
- R lacks basic security.
Julia is a powerful programming language that can compete with Java and approach the performance of C++. Though it is a high-level, high-performance language, you can use it to learn it very quickly and easily. Being a dynamic and fast programming language, it can provide the best numerical computation speed. You can also use it to implement mathematical concepts such as Linear Algebra. In short, many of its features are developed for computational science and numerical analysis. In the world of web development, it can be used both for front-end development and backend development purposes. Keep in mind that Julia is very fast and works more quickly than Python, R, and C++.
- Offers the easiest syntaxes.
- Julia programs are 10X-100X faster than Python and R.
- Easy to learn.
- Best libraries for mathematical operation and automatic differentiation.
- Supports an astounding degree of interoperability between unrelated libraries and codebases.
- Free and open source.
- Powerful shell-like capabilities.
- Jupyter notebook integration.
- It is hard to master.
- Does not have enough killer applications.
- It mostly serves scientific niches.
Having gone through the most popular programming languages for data science, it’s time to master one or more of these languages for a successful data science career. However, python programming language is making its contribution and gaining popularity for data science and machine learning projects because of its extensive support for data science and machine learning libraries.