The statistics above reveal that the increased prevalence of computers and electronic devices has given rise to the demand for programming languages. Basically, they serve as a set of instructions that direct computers to behave and perform tasks.
Knowledge of programming languages becomes an important skill, and mastering any of these languages can allow you to be successful in the business world.
Scope of the programming language (s)
Here in this article, we will discuss the pros and cons of the major programming languages that can prove to be of great benefit to you.
Not only is it the simplest, but it is the most efficient programming language that you can learn without any prior knowledge. Even if you are not a data science student, you can start with this. This language is mainly used for machine learning and artificial intelligence.
The good news is that you don’t need to pay a fee to get started with Python. So if you are interested in web development, data science, and coding, you should learn Python as a programming skill.
Nowadays, various companies use Python for various purposes.
Some of the companies that use Python include Instagram, Google, Netflix and Spotify. More importantly, it is used by Google to improve its search engine capabilities.
- Easy to use because it is similar to the English language
- It focuses on versatility in development and does not follow the standard setup.
- Useful for businesses in generating clear reports.
- It supports OO, procedural and functional programming methodology.
- Fun to use and easy to understand.
- Open source language with a large community.
- Works for all kinds of operating systems.
- Widely used programming language.
- Does not interact with weak components of the mobile operating system.
- Development speed is low compared to other languages.
- No web browser integration.
- Not the best scripting language.
- 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 installing any software.
- Portable and stable.
- An excellent coding language.
- You have to deal with complex codes.
- Requires significant memory space.
- Not an attractive look.
- Does not provide backup function.
Being one of the modern programming languages, it is a serious competitor to Python. It can also fix problems occurring with Java and runs on JVM. 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 supports object-oriented programming, but also functional programming.
It is a fast, efficient and compact language compared to Java and Python.
- Provides fairly good IDE support.
- Fun to use and easy to learn.
- Very functional and ideal for data analysis.
- Can handle large projects with precision and efficiency.
- Highly scalable (its name is derived from words, scalable and language)
- Able to execute complex machine learning algorithms.
- It supports a wide range of languages and frameworks.
- Sometimes you have to deal with bugs.
- It’s not completely free and open-source.
It is an open source programming language that was built primarily for statisticians. Interestingly, its creators are also the statisticians, and it seems they developed it to help each other. With this programming language, you can handle all kinds of statistical calculations, including the formation of tables and graphs.
While it’s not as easy as Python and Java, you can learn it in a matter of months if you’re willing to spend some time. There are many libraries for data science in R that make it diverse. So, learning can take a long time if you don’t have any prior knowledge because the learning curve is steep.
- It is an open source programming language
- Provides exemplary support for data management.
- Highly compatible language.
- Platform independent language.
- Provides eye-catching reports.
- Can develop statistical tools.
- With the wide range of packages, this language appeals to a wide range of industries.
- Does not support dynamic and 3D technologies.
- It uses more memory compared to other languages.
- This is not an ideal option when it comes to dealing with large data.
- R lacks basic security.
Last but not least, it is a powerful programming language that can compete with Java and approach the performance of C ++. Although it is a high level and capable language, you can use it to learn it very quickly and easily. Being a dynamic and fast programming language, it can provide the best digital calculation speed. You can also use it to implement mathematical concepts such as linear algebra.
In short, many of its features are developed for computer science and digital analysis. In the world of web development, it can be used for both front-end development and back-end development purposes.
Remember, Julia is very fast and works faster than Python, R, and C ++.
- Offers the simplest syntaxes.
- 30 times faster than Python.
- Easy to learn.
- Best libraries for math operations and automatic differentiation.
- Supports an amazing degree of interoperability between libraries and unrelated code bases.
- Free and open source.
- Powerful shell-like capabilities.
- Integration of the Jupyter notebook.
- It’s hard to master.
- Doesn’t have enough killer apps.
- It mainly serves scientific niches.
Now that we have gone through the most popular programming languages for data science, remember that they are not without their drawbacks and drawbacks. The data science industry is quickly overcoming its weaknesses and constantly improving.
In 2021, Python is a high level language and considered the best language because it has many libraries for data science and a large number of packages. However, other programming languages make their contribution and can perform various data science functions. If you want to learn programming languages, you need to know strict syntax, c objective, GUI applications, PHP developers, data analysis, reusable code, active community, memory allocation, visual content of the web application hypertext preprocessor and garbage collector.
Well, which of the programming languages mentioned above do you find most useful? Python or Scala?
Interesting related article: “What is software?” “