The exponential increase in data in this digital world and the pressing need to analyze and construct insights from the data has led to the rise in demand for specialized data scientists. Today, data science has emerged as the most popular and sought-after field by professionals and students alike. For data scientists, in order to transform raw data into furnished data products, they need a tool that provides a comprehensive environment to analyze, process, transform and visualize information, and R programming is one of those programming languages. R programming is such an important tool for Data Science that it is the first choice for many statisticians and data scientists. Top companies including Google, SpaceX, Hulu, Amazon, Ubisoft, Twitch, Goldman Sachs, and a ton of other companies and organizations use R for data science work.
However, Learning R programming as a beginner can be a frustrating challenge if you’re not sure how to approach it. So, if you’re struggling to learn R programming, you are definitely not alone. Here are a few quick tips to learn R programming for beginners.
What is R Programming?
R is a programming language and free environment developed in 1995 by Ross Ihaka and Robert Gentleman. The ‘R’ was derived from the first letters of their names. It provides a wide variety of statistical computing and graphical techniques. All the industries including finance, big pharma, insurance, fashion, media, and video games need people who can work with data. So, it is essential to learn R programming as a beginner. Here are a few tips to learn R programming for beginners easily.
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Quick Tips to learn R programming for beginners
Step 1: Make yourself comfortable for learning R programming
The first step towards learning anything is finding the motivation to learn. Before you dive into R programming tutorials and projects, spend some time really thinking about why you want to learn R programming; how it would really help you in your career and future. Find something that motivates you in this process and define your end goal. This is crucial because doing anything without a goal is just aimless wandering.
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Step 2: Start with learning basic syntax
Learning the basic syntax of R programming is a crucial part because it is the foundation on which your entire knowledge of R programming will depend on. However, learning syntax is a boring process for many. So, if you feel like you’re zoning out, try working on real-world problems that interest you and learn as much syntax as you can along the way.
Step 3: Start working on structured projects
Once you’re clear on R programming syntax, you need to move on to structured projects. Working on projects is a great way to learn and apply what you’ve learned so far. It also challenges you to learn new things and solve problems as you go. However, diving head-first into unique and difficult projects can demotivate you and make your progress still. So, look for structured projects that you can work on and gain experience.
Step 4: Build your own projects
By now, working on structured projects would have given you ample exposure and experience. The next stage of learning R programming as a beginner is to build projects on your own. Working on unique projects that interest you will give you a great idea of how far you’ve come as a data scientist. Even if you find it difficult to build projects on your own, you can get them from a lot of data science communities.
Step 5: Keep learning
Learning is a never-ending process. If you want to master R programming and excel in data science, you need to keep learning R programming. If you can easily build projects on your now, move to difficult projects that will test your expertise. Each project that you take must challenge you to learn something that you didn’t know before.
Thus, following these tips will help you learn R programming as a beginner. As a premier business school in India, Praxis offers a 9-month full-time post-graduate program in Data Science. With our vast experience in business education, we offer students both the time to understand the complex theory and practice of data science concepts and the guidance from knowledgeable faculty who are available on campus for mentoring. We also have a well-structured campus placement program that ensures interview opportunities with the most significant companies in the field.