Best Languages for Data Science Beginners and Pros | iCert Global

If you aspire to be a data scientist, you need to learn some computer languages. There is no single language that can do everything. That is why it is required to learn more than one language. Learning the appropriate languages makes you more efficient in solving data science issues. These are required frequently. For instance, Python was extremely popular during the 2010s when data science was growing. In an Indeed study, it was discovered that data science and Python skills from 2014 to 2019 were the key to kick-start a tech career in 2020.

Things to Keep in Mind Before Selecting a Programming Language:

• What kind of work will you perform with information?

• How does your organization leverage data science?

• What does your business want to accomplish?

• What are your own career aspirations?

• Languages you can speak?

• How hard are you willing to work to learn more?

Most Used Programming Languages for Data Science ?

Python

Python is the most significant data science language. This will hold true for the next five years at least. If you understand Python and how to fix problems with numbers and experiments, you will be great at this profession.

Python is a special and flexible programming language. People use it in many ways, like:
• Using tools like NumPy and SciPy to work with numbers and data
• Building websites with Django and Flask
• Organizing and managing information
• Making smart programs using machine learning, like decision trees

R Programming

R is another powerful language for data science. It became popular very quickly.
R is great at working with numbers and making graphs. It also has over 8,000 helpful tools created by people around the world. Statisticians use R to make predictions and create graphs.

It is supported in machine learning by packages like Gmodels, RODBC, TM, and Class in building smart systems. R is also a good option if you are comfortable writing research reports.

Java

Java is a widely used programming language that is over 30 years old. Java is applied in developing desktop applications, web applications, and mobile applications. Java executes on a platform known as JVM (Java Virtual Machine) that enables it to execute on various computers.

Most big companies prefer using Java since it is simple to add features on their project without slowing them down. That is why it is commonly applied in big machine learning frameworks.

JavaScript

JavaScript is a popular programming language. It was initially used to render websites interactive and engaging. But over time, it grew stronger with ReactJS, AngularJS, and NodeJS. Now, you can utilize JavaScript to create the front (users' interface) and back (their functionality) ends of websites.

SAS (Statistical Analysis System)

SAS is a data and number-working device, and it is appropriate for activities like business reports, forecasting, and data analysis. SAS has been in existence since 1976, and the majority of people in the world of data trust it.

Scala

Scala is also a Java-compatible language. It can be applied to big projects with a lot of data. Scala is usually used with a system named Apache Spark, which assists in the data task distribution and executing them with high speed.

TensorFlow

TensorFlow is a very useful math and machine learning program. It assists you in managing enormous amounts of data. You can split your work into a number of small works and execute them simultaneously on a number of computer chips (CPUs and GPUs). This accelerates training large computer systems (neural networks) significantly.

C#

C# (pronounced "C-sharp") was developed by Microsoft. It is 20 years old. C# is similar to Java but with some new and helpful functionalities. Microsoft simplified data science with C# by including Hadoop support and making ML.NET, a tool for assisting you in creating intelligent applications for different computer systems.

Ruby

Ruby is also a language that is frequently used to deal with text. It is also used by developers to experiment with ideas, build servers, and perform mundane tasks. Ruby is not as widely used in data science as Python or R, but it has some useful tools nonetheless.

For data science, Ruby can be used with:

• iruby – allows you to run Ruby in Jupyter notebooks.

• rserve-client – allows Ruby to communicate with R, another data tool.

• Jongleur – helps to organize and change data.

• Rb-gsl – offers access to mathematical functions in the GNU Scientific Library

Languages Used for Programming

Before you select a programming language, think about what you do. For example, financial folks use R to read stock market data and predict prices. In stores or online, computer programmers use Python to develop programs that suggest products to shoppers.

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Conclusion

Choosing the appropriate programming language can help you progress in your data science career. Keep your goals, work needs, and what recruiters need in mind. Keep learning and enhancing abilities. iCert Global offers easy data science courses that make you successful.

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