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A Complete Guide to the Quick Learning Path of R

Data Science is now the talk of the town. You might have heard about it many times in meetups or webinars or speeches etc. People, now days are really emphasizing the value of data science and analytics. When you hang out with your friends and peers and discuss your career path with the techpreneurs, Data Science will be the most common technology which you will hear about. Techpreneurs will guide you better why this technology is demanding in today’s world. Let us discuss the same in this blog. That-

  • Why Data Science is the most demanding career?
  • How can you get an entry in the growing career of a Data Science using R language?
  • What will be your learning Path of R?
  • What do I need to learn in R?
  • How much time is required to learn R?
  • A magic trick to learn R programming quickly?

Let’s get started!

It started from the day when I started my career planning. I used to scroll day and night to find out which career I should opt? what will be the scope of that career? And the many similar questions! But there was always a one similar answer I found every time. I also came across the fact that shows 80% of the UK, USA, Canada, and many other top-level organizations are currently hiring data scientists, and this is expected to grow more by 28% in the near future. (Well, who knows you will be the next Data Scientist!)

The rising popularity of the R

Do you know, Facebook, Google, and Microsoft also make use of the R programming language. For these tech giants, R is used as one of the choices for data scientists, R is used as a-

Use of R in Academia:-

The popularity of R is not limited took for the corporate sector but the academic scientists and researchers also use this programming language. It is said that R is the most common and language which is presented in the journal named – Nature. But yes, it is difficult to make data scientists from right after academics. Indeed, if the students are taught the R programming language during their academics, then we can get of all programming languages. It will also increase in the industry.

Use of R in Corporate:-

R is the best choice tool at giant technical companies like Microsoft, Bing, Azure, etc. Their financial and marketing departments make use of the R programming language. Other than these, Bank of America, Ford, TechCrunch, Uber and Trulia like large MNC’s also make use of R programming language to hire a data scientist.

Figure: The growth of the R programming language is arising since 2014

Data is still a data whether it is for a retailer or a charity. As per the statement of Karl Hoods who is a chief digital and information officer, states that-

“If we have a large amount of data which is obviously gathered at daily basis and we want that data to be utilized to solve business related issues, spot trends and make decisions to support new ideas, companies need the people who have mix skills of statistics, databases, data visualization, machine learning, coding and data preparation like skills.” To become a Data scientist, the knowledge of programming languages one need to be familiar with are:-

  • Python
  • R
  • Java
  • SQL
  • Scala
  • MATLAB, etc.

Among all these programming languages, R programming language is the most frequently used language. R is an open-source software environment which is used for statistical computing and graphics. The package of R which is publicly available for the learners contains more than 8,000 networks contributed packages. Various top organizations such as Microsoft support R-based computing.

R is not only a programming language but it’s a complete software like Tableau, SPSS, etc. R provides a wider range of tools which you can use for data visualization, data wrangling, and machine learning.

The R programming language compels you to dive deep in Data Science and help you to solidify the skills that are essential to get specialization in data science.

For whom R is for?

R is for

  • Scientists
  • Researchers
  • Data Analytics
  • Biostatisticians
  • Economists

What will be your learning Path of R?

  • Choose the Learning platform

The first step to initiate learning of any new technology is to figure out your interest and choosing a platform for this. If you look at the figures for the training of R programming languages, you will find that people are making search a on the search engine. Either they are looking for the MOOC or free online courses or some of them are looking for the best coaching available in their localities. If you want to learn R programming language, then you have two options:

  • Learn Online

Learn online means you will be sitting comfortably and learning through different ebooks, youtube videos, self-paced tutorials from their website. Online courses can be paid or unpaid. But yes, you can earn various course discount vouchers (check discount offers now on Data Science Online Training)

  • Learn Offline

Well, offline training of R is suggested only to those people who want to experience classroom training.

  • Learn by doing

Writing R code with real-time examples will definitely help the learner of R. It helps in understanding the logic and problem solving of R programming at a deep level.

  • Put your learning into practice

It is important to apply your learning with real-world program exercises which can help to test the knowledge you have attained with tailor-made quizzes.

  • Land your dream job

Ensure you have exercised more than enough the basic and advanced concepts of R programming deeper level. When you find yourself thinking like a data scientist, then you can start looking for opportunities to land your dream job.

What do I need to learn in R?

There are many concepts available, so many suggestions and study material due to which it become difficult to figure out the exact what you need to learn. Such a mountain of content will surely confuse the newbie to know or find the golden nuggets that bring you the high return on investment and the clear picture of where to start.

Core skills of R

  • Data Wrangling

80% of the work is done in data science in data manipulation. You need to spare extra time for the wrangling of your data which will help in putting up in the shape fo your data.

  • Data Visualization

Ggplot2 is among the best data visualization tools. The great part of this tool is that you can learn how to think about data visualization. This is a great framework to create a deep structure for all kind of statistical and data visualization. With the use of this tool, you can learn how to visualize your data. Also, when you collaborate ggplot2 and dplyr together (by using the chain technology) which can find insights into your data with the help of which data very effective.

  • Machine Learning

Technical newbies who just entered in the machine learning of IT industry can begin their career using R programming language. R provides many best tools and resources to begin the journey with machine learning

You can write your first program in R after learning the core concepts of the R programming language. But before diving into the language, you first need to go through the following steps:-

  • Installing R programming
  • Installing R studio

………and now you are ready to get familiarize with the fundamentals of R programming such as-

Core concepts of R programming:

  • Overview of R
  • Installation of R on various platforms such as Windows, Mac and Linux
  • Hello World in R
  • Editors and IDE available for R
  • Installation of R Studio on various platforms such as Windows, Mac, and Linux
  • Overview of R Studio Desktop
  • Variables and Operators in R
  • Functions in R
  • Flow Control Statements in R
  • Packages in R

Advanced concepts of R programming:

  • Functions
    • Overview of Functions
    • Creating user-defined functions
    • Function calling
    • Delayed execution of the function
    • Nested functions (function within function)
    • Lexical scoping
    • Search Path
    • Scope of function
  • Classes
    • Overview of classes
    • Generic functions
    • Implementing user-defined classes
  • Data Structures in R
    • Vectors
    • Attributes
    • Sets
    • Map
    • Matrices and arrays
    • Data frames
    • R vectorization
    • Scalars
    • Doubles

How much time is required to learn R?

R is easy to start language and this is now not the task that is performed by industries experts only or the people with technical background only.  The learning of R programming is deep and demands the use of high standards with the help of this programming language. Since you are putting a lot of efforts in learning the R programming language, let us make a rough plan and estimate the total time you required to learn R.

Roughly,

If you start preparing from basic concepts, then it will take 1.5 to 2 months. After having in-hand experience of the core concepts, rush yourself to learn advanced concepts of R programming which will take another 2 to 3 months.

Now, the crux is the issue. You have limited time and learning a new programming language is the investment of time.

A magic trick to learn R programming quickly

Gone are the days when hard-core technical knowledge was required to enter into the IT industry, especially in the coding profession. We are living in the era of modern technology where even teachers provide training to the students through online mode. There are many training providers who provide MOOC (Massive Open Online Courses) in data science using R programming.

If you enroll for a formal online training, it can lessen your learning time by approximately 30%.  There are a variety of top reasons that make me believe online training is best to take. Because of the forefront of the digital environment, you can reach out any technology with just a few clicks.

If you want to perform some serious data exploration in R, you need to have mandatory data visualization and data manipulation, as mentioned above. But don’t get “shiny new object syndrome”.

Spending 100 hours on R programming will yield giant better returns rather spending 10 hours in 10 different technologies or tools.

Therefore, taking on a creative and independence freeform challenge is always the best way to learn something new!

Continue with the discussion in the comment section below… Happy Learning!

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