Best Programming Language For Data Science
With the enormous amount of data being generated every second with the numerous activities of people over the Internet, there has been a surge in the demand for data scientists, to handle this massive data.
The past two decades have observed an explosion in the field of Data Science. Regardless of the size or type of your company, it is crucial to employ data scientists in the same. This is because Data Scientists are required to sift through this huge amount of data and extract meaningful insights to help improve the decision-making capabilities of a company.
There are various benefits a company can get with the accessibility of the data. Hence, there is an exponential increase in the demand for skilled and trained data scientists. The average annual salary of a Data Scientist in India is found to be around INR 698,000 at an early-career.
To decide the programming language you need to master, you need to consider platform framework, interests, data science environment, organization, and career path. Since R programming language has outpaced other ones in a very short period and has become one of the most prominent languages in Data Science, you can go with R online course to achieve expertise in the same.
Some In-demand Programming Languages in Data Science
There are many programming languages you can learn that can be applied in Data Science. It is always recommended that you master more than one because not one language can be used to solve all kinds of business challenges. Though it depends on the task you are given and the type of your organization. Let us explore some of the most in-demand programming languages in Data Science.
- R Programming
One of the most popular languages in Data Science, R is used for statistical modeling and analysis. In recent years, it has outpaced many programming languages and is found to be a continuously evolving language.
With R, you can design a wide range of statistical models. The archive of the public R package comprises almost 10000 networks.
It is an open-source programming language and you don’t need a license to work with R.
It provides exemplary support for the wrangling of data. In addition to consisting of a vast array of packages, it facilitates you with quality plotting and graphs. R can be paired with programming languages such as C, C++, Java, and Python, hence it is highly compatible.
Also, R facilitates machine learning operations such as regression, classification, and developing artificial neural networks. It is platform-independent and can easily run on Windows, Linux, and Mac.
R is a dominant language when you are required to develop statistical tools, therefore it is referred to as the lingua franca of statistics. There are many resources for learning R online.
The recent years have seen Python be on top of the required skills in Data Science. And the next five years are going to observe the same. You become the most preferred candidate for employers if you master the concepts of Python, along with a strong aptitude for experimental analysis and quantitative reasoning.
It is the flexibility of Python that makes it the most preferred language in Data Science. It is a faster and better option when data manipulation is to be done, as it has less than 1000 iterations. Its flexibility allows you to build solutions for a variety of use cases, by using the Python toolset.
Some of the use cases include:
- Performing data mining by using modules such as NumPy, SciPy.
- Classifying, sorting, and categorizing data
- Creating web services by using Django and Flask frameworks.
- Developing algorithms in Machine Learning like random forest and decision trees.
A quote by Peter Norvig states about Python this way:
“Python has been an important part of Google since the beginning and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we are looking for more people with skills in this language”.
Java has been ruling over the minds of programmers for the past three decades and is a favorite among desktop, mobile, and web developers. It runs on a powerful and sophisticated environment called Java Virtual Machine or JVM.
Its high scalability is the reason why enterprises use it for creating large-scale systems in machine learning. Once you launch a project in Java, you don’t need to worry about its performance, it can scale without any issues in performance.
As the name itself describes, SAS is commonly used in areas like predictive analytics, business intelligence, data management, and multivariate analysis for performing statistical modeling. Also, you can leverage SAS for accessing data in various formats, managing and manipulating it, splitting and merging datasets, and run statistical methods for data analysis.
You have now realized that every programming language has different use cases. As in, R is used when you need to perform data mining in a financial institution and Python is used when you are required to create a recommendation engine to suggest different products to the customer.
You have to choose the correct programming language to make your career successful in Data Science. There are online training courses that train you with the language of your choice based on various factors. Also, the training goes smoothly, at the hours you choose to learn. What else do you need to get trained?
Get yourself enrolled now.