reasons-to-learn-r-programming-for-a-successful-career-in-data-science

Reasons to Learn R Programming for a Successful Career in Data Science

Introduction

Data Science is one of the most important subjects in the world today. Almost all the organizations that you have heard of are using it to empower their businesses. With a thorough understanding of this subject, you can accumulate, understand, and process large amounts of customer and business data.

Perhaps the most important function of data science is to figure out customer behavior. Data scientists are able to understand their target customers and develop strategies accordingly. This particular aspect has led to a huge demand for data scientists and data analysts in the corporate world.
This is why many aspiring job seekers are turning to Data Science with R certification training to further their prospects in this domain.

What is Data Science?

Data Science is the field of study that deals with tools to gain insights and extract knowledge from huge datasets. Methods such as machine learning, signal processing, statistical learning, probabilistic modeling, visualization, data mining, and pattern recognition are used in data science. The insights obtained from the data are then used to understand and solve problems – mostly that of businesses.

For example, data science is used by companies like e-commerce giant Amazon to understand their customers. They use customer data to analyze customer buying behavior and preferences. So, when a customer is looking for an item online, they are also shown relevant items that they might like.

Over the years, huge growth has been observed in the field. Currently, in India, data science is used in different sectors like information technology, healthcare, e-commerce, banking, pharmaceuticals, and finance. After the USA, India is the next best country for data scientists. India accounts for every 1 out of 10 analytics or data science jobs. The banking and finance sector comprises 44 % of the total data jobs in the country. Not just that, in the coming years, more than 39,000 data science positions will be created in the aviation and agriculture sector.

Data Science is expected to influence fields such as space exploration, transportation, and cybersecurity.

Programming languages used in data science

Here are some of the programming languages used in data science:

Python

Python is a high-level, interpreted language that is famous for its simple syntax. Over the years, it has become a popular choice for data science engineers due to its variety of libraries. Moreover, it has an easy learning curve. Its simplicity and high readability make implementing algorithms easier.

It offers many libraries like Pandas, SciPy, Matplotlib, and Numpy. Activities like data visualization, analysis, predictions, and preservation are possible through these libraries. Advanced libraries like Tensorflow and Pytorch offer Deep Learning tools for data engineers and scientists.

R Programming

For activities requiring statistical analysis, R is the perfect option for a data scientist. But, compared to Python, it has a steeper learning curve. The language offers more than 10,000 useful packages for performing statistical operations. The language can easily handle linear algebra and neural networks. The data visualization library called ggplot2 is an important component.

It has a runtime environment called RStudio that you can easily connect to a database. You can connect it with SQL using the packed called RMySQL. Moreover, it supports object-oriented concepts and generic functions.

Scala

Scala is a programming language that supports both object-oriented and functional programming concepts. Like another programming language Java, its code is compiled into Java bytecode. You can execute it on the JVM (Java Virtual Machine). It is popularly used along with Apache Spark, a big data platform. It is very popular for handling large volumes of data. Another important aspect of the language is its ability to perform parallel processing.

But, due to its steep learning curve, it is not recommended for beginners.

SQL

Every data scientist needs to learn SQL for handling databases. SQL is a language used for storing and retrieving data from a relational database. It is crucial for updating, manipulating, altering, and data wrangling. SQL has other implementations such as PostgreSQL and SQLite.

SAS

SAS stands for Statistical Analysis and is similar to the R programming language. The software was developed for business intelligence, analytics, and predictive modeling. It offers many libraries for machine learning and statistical analysis.
Using SAS, you can extract and organize the data that will help in identifying data patterns. Furthermore, being platform-independent, you can run it on any operating system like Windows or UNIX. It offers excellent tools for data cleansing.

However, among the programming languages mentioned above, R has emerged as one of the best and most used tools for data science.

Why learn R programming?

We will now look at the top reasons to learn R programming:

Platform independence

You can execute R on any operating system such as Windows and UNIX, having varied software/hardware. This makes it easier to use, especially if you are a beginner in the field of data science. So, as an employee, if you are working on a data science problem on a Windows machine, then don’t worry. The client, at the other end running a UNIX system, can easily run your code.

Expansive library

R has a huge library consisting of more than 10,000 in-built packages and functions to make life easier for you. There are packages for data visualization, data manipulation, data processing, machine learning, and statistical modeling. You are free to explore and play around with these packages.

Moreover, being open source, you can modify the functions and implement your own as per your requirements.

Strong community support

The language is supported by a vast community of developers who constantly update it. So, if you have any problems regarding the language, you will find a solution online on various programming community forums such as StackOverflow.

Developing web applications

You can build web applications using the R Shiny package and through the RStudio. Using this facility, you can create interactive dashboards and embed your data visualizations. You can explain the data more easily and aesthetically.

High demand in the job market

R has a huge demand in the technology job market. R programmers make more than $117,000 a year.  In India, R programmers make around INR 500,000 every year. Learning R can open multiple job opportunities in the following positions:

  • Data scientist
  • Data analyst
  • Financial analyst
  • Quantitative analyst
  • Database administrator
  • Data Architect

Conclusion

As per the current trends in the data science market, R may be the frontrunner among all programming languages. So, major organizations are encouraging their staff to learn the language and develop expertise in it. With more than 2 million users using the language across the world, R is considered the most popular analytic tool.

Therefore, to secure a lucrative data science job at companies such as Google, IBM, Accenture, Facebook, or SAS, you can learn R programming. You can take up a course on R programming to equip yourself with the required skills and become job-ready in the data science domain.

Note: This post is sponsored by Simplilearn – A leading certification training providers

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