Learn R and Become a Data Scientist

The popularity of the R programming language is increasing, especially in data science and analytics.

As it provides better data visualization techniques, R programming plays a crucial role in statistics.

However, learning this language could be frustrating if you do not have a clear path to approach it. In the past, you might have struggled a lot while learning R or any other language.

Trust me; you are not alone!

Don’t blame it on yourself entirely or the language; the issue might have been in your approach. The way you learn something has a massive hand in delivering the final result.

If you have a clear strategy of how and why you should learn a particular language, the chance of your being proficient in it amplifies. Similarly, if your goal and strategy do not align, you might get bored with the language and end up quitting it midway.

It is analogous to learning a spoken language.

So, when you are ready to learn R, get your motive clear in the first place, be it expanding your knowledge or finding a career in data science. Next, prepare a strategy and align it with your goal.

…And start learning.

In this article, I will talk about some good resources to learn the R programming language that will give you the right approach to making things easier.

But first things first,

What is the R Programming Language?

R is an open-source programming language for graphics and statistical computing.

It was developed in 1993 by Ross Ihaka & Robert Gentleman. It is similar to the programming language – S. You could say that R programming language is an implementation of S, combining lexical scoping semantics. The software is written majorly in C, R, and Fortran.

R offers a wide-ranging variety of both statistical and graphical techniques in addition to being highly extensible. It includes classical statistical testing, linear & non-linear modeling, time-series analysis, clustering, and classification.

One of R language’s major strengths is being effortless in producing a well-designed publication-quality plot, including mathematical formulae and symbols.

The capabilities of R

R is a kind of integrated suite consisting of software facilities you can use for calculation, graphical representation, and data manipulation.

It includes:

• Effective storage and data handling facility
• An integrated, coherent, and large collection of data analysis tools
• A suit of various operators useful in calculating arrays in specific matrices
• Simple, effective, and well-developed programming language with loops, conditionals, and user-defined variables
• Graphical facilities to analyze data and display it on hardcopy or on-screen
• R is capable of extension through packages. In fact, about 8 packages are supplied using R distribution, while more can be added with the CRAN site family.
• Cross-platform interoperability
• Instead of using a compiler, R uses an interpreter, which helps make code development easier.
• It associates well with different databases and brings information from MS Access, Excel, MySQL, Oracle, SQLite, etc.
• It integrates powerful tools for communicating reports in various forms such as HTML, XML, CSV, PDF, and interactive sites with R packages.
• R packages come with diverse codes, features, and functions tailored for statistical modeling, data analysis, machine learning, visualization, data imports, and manipulation.

How does R assist in data analysis?

Data analysis using R takes place in a series of different steps:

• Program or import: Programming with R or you can import data from databases and files into the R software environment
• Transform: Data organization happens by transforming a column into a variable during a row into an observation. Observe your interests, create a new variable as a function of current variables, and discover observation statistics.
• Visualization: Data representations in a graphical form to recognize trends, patterns, and data exceptions easily.
• Models: These are complementary visualization tools such as computational or mathematical tools to answer observational questions.
• Communication: Communicating results with others, right from visualization to modeling, with the help of easy-to-produce, print-quality plots to share with anyone in the world.

Who uses R and why?

R is trusted not only in academics but also by large companies, including Google, Facebook, Airbnb, Uber, and more. It is used in healthcare, consulting, government, insurance, energy, finance, media, almost everywhere. They use it for statistical inference, machine learning algorithms, and data analysis.

You see, the demand for R is in various sectors. In addition to this, data analysis is undoubtedly shaping present-day businesses. Even though there are plenty of tools available, R stands out. It’s because you may have:

• Excel and PowerBI, but they lack modeling capability;
• Python is great for AI and ML but does not have communication features;
• SAS is good for statistical analysis but not free
• Tableau is excellent for graphical representations, but it needs to do better in decision making and statistics.

However, R fills the gap by offering an excellent learning curve with a good balance of data implementation and analysis.

Hence, it makes sense to learn R for data manipulation and analysis and even become a data scientist.

And this is why data scientists use R to understand data, perform manipulation, make the best approach, and communicate with others through reports, dashboards, or web apps. In this way, a single platform performs all the work.

Now you know how R works and why you should go for it, but where to learn R?

Is it that difficult to learn?

If you asked me these questions a few years ago, I’d have said yes, it is a bit difficult due to its complex structure. But now, packages are introduced to overcome this issue, which has made data manipulation easier and intuitive, and graph creation is rather easy.

Packages like TensorFlow and Keras let you create high-end ML techniques; you can call Python, C++, and Java in R and connect with Hadoop or Spark. And R has evolved in terms of computational speed as well.

So, do you want to learn R?

I’m assuming a YES!

Let’s find some good resources to learn R.

Data Scientist with R

Gain the R skills that can help you build your career as a Data Scientist with Datacamp. To start the course, you don’t need any prior knowledge or experience in this field.

They will teach you the versatile R language and how you can use it to import, manipulate, visualize, and clean data, which are the basic integral skills you require. With interactive exercises, get hands-on experience with famous R packages such as ggplot2 along with Tidyverse packages such as readr and dplyr.

The course will also introduce you to some real-world datasets that help you learn machine learning and statistical techniques needed to write functions and conduct cluster analysis all by yourself.

All you need to do is start this course, grow R skills, and continue your path to become a successful data scientist. They offer 75+ hours of learning resources. It includes introducing the language to master data analytics basics with typical data structures like matrices, vectors, data frames, etc.

R Programming A-Z

Udemy brings R Programming A-Z with hands-on exercises to help you become a data scientist. The course is divided into 8 sections, 82 lectures, and takes approximately 11 hours to complete.

They teach you R step-by-step, and you will learn valuable concepts that are applicable right away after every lecture. And another great thing is that they teach you concepts with the help of live examples. The whole training is full of real-world analytical challenges that you will solve during your lecture and your homework exercise.

Anyone with any skill sets can learn this course, but you need to learn the R language and take up exciting challenges. The course material teaches you its core principles and how to create variables, vectors, loops, and functions.

You will also learn about the normal distribution and practice with financial data, statistical data, and sports data. Besides, you will learn how to use the R Studio and customize it based on your preferences.

At the end of this course, you would install R packages and understand Large Numbers, integers, double, character, and more. The course also includes advanced visualization using GGPlot2 along with homework solutions and bonus tutorials.

Statistics with R

Coursera offers this course – Statistics with R Specialization to help you master R for data analysis, including modeling, inference, and Bayesian techniques. This course is completely FREE and offered by Duke University.

This course will gain skills like statistical inference, linear regression and statistics, RStudio, R programming, exploratory data analysis, statistical hypothesis testing, Bayesian statistics, Bayesian linear regression, Bayesian inference, regression analysis, and model selection.

The specialization will teach you how to visualize and analyze data in the R programming language and then create reproducible reports. You will learn to display statistical inference in its unified nature and perform modeling and other techniques to make data-driven decisions.

The course will also help you communicate results correctly, organize and visualize data using R packages, and critique decisions and claims. It will help you build your portfolio with various projects in data analysis to demonstrate your knowledge and skills in addition to landing a high-paying job.

This beginner-level course takes approximately 7 months for completion, features a flexible schedule, full online lectures, and a shareable certificate upon completion.

Getting Started with R

Another course by Coursera in this list is – Getting Started with R.

This is a beginner-level course that takes about 2 hours to complete, and you can access it only on your desktop with no download needed. This guided project teaches you the R programming basics to take your first step towards data analysis.

Here, you will learn how to use R Studio or the R GUI and the different data structures and types used in this language. Finally, they will teach you how to install the R packages and import your data sets into R Studio’s workspace.

There are no necessary prerequisites to complete this project; just basic computer knowledge is sufficient. In the guided project, your workspace would be a cloud desktop you can access from your browser. Your instructor will guide you in a split-screen via a video to make you understand things step-by-step.

Udacity

Learn R programming to be a data scientist with Udacity. The approximate time this course takes is 3 months with 10 hours of effort every week, and it needs no hard prerequisites.

The syllabus includes teaching you how to code in R, command line, SQL, and Git so you can solve data-related problems. You will learn SQL basics like JOINs, sub-queries, and aggregations and use them to answer business problems.

Learn fundamentals, including data structures, loops, functions, and variables. In addition to it, you will learn how to visualize data through GGPlot2.

The program includes real-life projects with immersive content developed by experts, mentor support, and career services like resume and portfolio reviews. Study at your own schedule, and get personalized feedback, practical tips, and additional suggestions for more resources.

ML Scientist with R

Master the R language to become a confident machine learning scientist with Datacamp. They offer 15 courses in total with 60+ hours of effort in learning R. Here; you will enhance your R skillsets with a toolbox and perform unsupervised and supervised learning.

They will teach you how to process data for model creation, train and visualize the models, and test their performance. In addition to this, you will help tuning up their parameters to execute better performance.

Meanwhile, you will also learn Bayesian statistics, Spark, and natural language processing (NLP). They will teach you machine learning basics for classification, how you can foresee future events through linear regression, random, forests, xgboost, and additive models.

You will also learn dimensionality, clustering, ML in Tidyverse, logistic regression, cluster analysis, ML with a caret, tree-based models, support vector machines, topic modeling, hyper-parameter tuning, and more.

Data Analytics with R

Edureka offers the training program– Data Analytics with R to help you acquire expertise in data manipulation, visualization, exploratory data analysis, mining, sentiment analysis, and regression.

The training can also help you learn R Studio for case studies over social media and retail. They have designed this course to offer the skills and knowledge needed to become a data analytics professional.  It covers basic R concepts to advanced topics such as decision tree ensemble, collaborative filtering, and more.

The modules walk you through important terminologies such as business intelligence, data & information, business analytics, and more. You will learn data import methods, exploratory data analysis, clustering, linear & logistic regression, supervised ML techniques, ANOVA, R packages, creation of plots, and more, along with project work.

To take up this course, you need a basic knowledge of statistics. It includes 30 hours of online classes where you will get practical assignments to complete after each class, along with lifetime access to the course with presentations, class recordings, installation guides, and quizzes. Get a certificate on course completion.

YouTube

Learn R on YouTube with Barton Poulson, who teaches the basics of R language and statistical computation.

The tutorial covers topics like how to install R, things about R Studio, plot function, packages, histograms, bar charts, scatterplots, summary function, overlaying plots, and describe function.

He also teaches how to select cases, factors, data format, how to enter data, import data, the concepts of hierarchical clustering, regression, principal components, and more.

Codecademy

Codecademy introduces you to the fundamental concepts of the R programming language. There are no specific prerequisites to learn this course or any coding knowledge needed.

Here, you will learn how to organize data, modify it, and clean the data frames. They will also teach you to build data visualizations and display insights. In addition to this, you will learn hypothesis testing and statistics to excel in the field of data analysis.

The course syllabus also includes the fundamentals of aggregates and joining tables with dplyr;  calculate mode, mean, and median; and statistics like quartiles, interquartile range, and quantiles.

You can also test your knowledge through quizzes to sharpen syntax and memory. It takes around 20 hours to complete the course, and with the Pro plan, you can earn a certificate.

Datamentor

Datamentor course includes unlimited access to 45+ videos, interactive assignments, R Essentials eBook, and a project.

It familiarizes you with the fundamentals of data science, its processes, and the different steps you must take to complete a data science task like getting data, exploring, modeling, and communicating the report.

Conclusion

With lots of resources available, learning the R programming language is no more a difficult task. All you need is to have a passion for learning and a strong desire to venture into the field of data science.

So, are you an aspiring data science professional? 💡

Learn R with the help of the above-mentioned amazing courses.