Top 5 Programming Languages for Data Sciencehttps://guttulus.com/wp-content/uploads/2018/12/IMG_0568-1024x665.jpg 1024 665 tony tony https://secure.gravatar.com/avatar/aa9bbdf8f1e6bbf534778ecea7c0c925?s=96&d=mm&r=g
With the advent of AI and deep machine learning, data science has grown in popularity among the tech savvy and deservedly so. The future belongs to those who embrace data science in their businesses and daily lives.
It is no surprise therefore that a lot of people are enrolling into data science classes and training camps just to keep up with the trend. More and more people are looking to understand the art of drawing insights from both structured and unstructured data using scientific algorithms and other research tools.
At the heart of data science are programming languages which are used to analyze the data obtained, perform data visualizations, select data and other data analysis functions. In this blog post, we want to explore the top 5 programming languages which are used for data science and why they are preferred by data scientists. Here are the top 5 programming languages used for data science;
Top 5 Programming Languages for Data Science
No programming language has been more significant in statics and data analysis than R. R is a gem to data miners and data scientists because of its simplicity and effectiveness when it comes to analyzing various sets of data.
R is loved by data scientists because of its simplicity and the strong object-oriented options which it affords the programmers. The object oriented programming facilities that it offers, give it an upper hand over the other programming languages.
With R, you can easily represent various forms of data, do mathematical computations, create vectors, create matrices, create arrays and data frames. It is therefore not surprising that even the mega companies such as IBM, Google and Facebook, use R in their data science escapades.
Whereas R is bespoke in statistics, Python is more of a multipurpose programming language with a number of capabilities. Its ability to carry out a number of tasks make it a favorite among programmers.
Python has a large number of libraries which will help you to carry out a number of tasks in data science. These tasks include automation of the user graphic interface, handle multimedia data, handle databases and to process text data.
Python is very easy to learn and is fun to work with. It is therefore not surprising that a lot of programmers looking to get into data science, learn python first before advancing to higher level programming languages such as R.
Java is one of the first programming languages that was used by data scientists. It has stood the test of time and in an era where more bespoke languages such as R are preferred, Java is still relevant and is preferred by the experienced data scientists.
One of the best attributes of Java that has helped it withstand the test of time, is the fact that is highly portable. Once the code has been compiled, it can be run on any platform that supports Java. You can therefore easily transfer the code from one platform to the other and it will still be effective.
Java Virtual Machine is the other feature of Java that data scientists use on a daily basis. It is however good to admit that Java is a bit complicated to learn and will take some time before you understand its fundamental principles but once you have understood what it entails, the rest will come to you very easily.
Scala was originally designed to run on Java and make Java learning and application a bit simpler. Scala can be termed as a simplified and more user-friendly version of Java. It has a very intuitive user interface which makes it ideal for day to day use.
Scala is easy to learn and is fun to work with on a day to day basis. Scala is getting revamps and improvements every year and promises to become one of the best programming languages in data science in the near future.
Structured Query Language or SQL, is a programming language specifically designed to deal with large volumes of data stored in large databases. It is particularly helpful in managing structured data. If you intend to understand how to handle large volumes of data, process structured data and get an understanding of managing databases, then you need to learn SQL.
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If you intend to get data science services from Runrex or get advice on how to get started on data science training, give us a call today and we will gladly be of service. Prepare yourself for the future by embracing data science today! Talk to us here at Runrex and we will gladly be of service to you. Our customer service team is always ready and willing to attend to you and they are available at all times. Give them a call today and get started on the amazing journey of data science.