Learn R and become a data scientist

The R programming language is growing in popularity, especially in the data science and analytics field.

R programming plays an important role in statistics as it provides better data visualization techniques.

However, without a clear path to tackling this language, learning this language can be frustrating. In the past, you may have had a really hard time learning R or other languages.

Trust me; you are not alone!

Don’t blame everything on yourself or your language. The problem may be in your approach. The way you learn something has a huge impact on the final outcome.

Having a clear strategy for how and why you should learn a particular language increases your chances of becoming proficient in that language. Similarly, if your goals and strategies are not aligned, you may get bored with the language and quit midway through.

It’s similar to learning a spoken language.

So if you’re ready to learn R, be clear about your motivations first, whether it’s to expand your knowledge or find a career in data science. Next, prepare your strategy and align it with your goals.

…and start learning.

In this article, we will discuss some great resources for learning the R programming language that will give you the right approach to make your work easier.

But first,

What is the R programming language?

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

Developed in 1993 by Ross Ihaka and Robert Gentleman. This is similar to the programming language S. The R programming language can be said to be an implementation of S that combines lexical scoping semantics. This software is primarily written in C, R, and Fortran.

In addition to being highly extensible, R offers a wide range of both statistical and graphical techniques. This includes classical statistical tests, linear and nonlinear modeling, time series analysis, clustering, and classification.

One of the great advantages of the R language is that it makes it easy to create well-designed, publication-quality plots that include formulas and symbols.

Features of R

R is an integrated suite of software functions that can be used for calculations, graphical representation, and data manipulation.

This includes:

• Effective storage and data processing facilities
• A unified, consistent collection of large-scale data analysis tools
• A set of different operators that are useful when calculating a given array of matrices
• A simple, effective, and well-developed programming language with loops, conditional statements, and user-defined variables
• Graphical capabilities to analyze data and display it in hard copy or on screen
• R can be extended through packages. In fact, the R distribution provides about eight packages, but you can add many more using the CRAN site family.
• Cross-platform interoperability
• R uses an interpreter instead of a compiler, which makes code development easier.
• It works well with a variety of databases and retrieves information from MS Access, Excel, MySQL, Oracle, SQLite, and more.
• Powerful tools are integrated with the R package for communicating reports in a variety of formats, including HTML, XML, CSV, PDF, and interactive sites.
• The R package comes with a variety of code, features, and functionality tailored for statistical modeling, data analysis, machine learning, visualization, data import, and manipulation.

How does R help with data analysis?

Data analysis using R is done in a series of different steps.

• Program or import: You can program using R or import data into the R software environment from a database or file.
• Transformation: Data organization is done by converting columns to variables while converting rows to observations. Observe what you’re interested in, create new variables as a function of current variables, and discover observation statistics.
• Visualization: A graphical representation of data to easily recognize trends, patterns, and data anomalies.
• Models: These are complementary visualization tools, such as computational and mathematical tools, for answering observational questions.
• Communicate: Communicate your results with others, from visualization to modeling, with print-quality plots that are easy to create and share with anyone in the world.

Who uses R and why?

R is trusted not only by academia but also by large companies such as Google, Facebook, Airbnb, and Uber. It is used in almost every field including healthcare, consulting, government, insurance, energy, finance, and media. Used for statistical inference, machine learning algorithms, and data analysis.

There is demand for R in a variety of fields. In addition to this, there is no doubt that data analytics is shaping business today. There are many tools available, but R stands out. This is because the following may be considered.

• Excel and PowerBI, but they lack modeling capabilities.
• Python is great for AI and ML, but it doesn’t have communication capabilities.
• SAS is good for statistical analysis, but it’s not free
• Tableau is great for graphical representation, but needs further improvement when it comes to decision-making and statistics.

However, R bridges that gap by providing a good learning curve that balances data implementation and analysis.

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

That’s why data scientists use R to understand data, perform operations, develop optimal approaches, and communicate with others through reports, dashboards, or web apps. This way, a single platform does all the work.

Now that you know how R works and why you should use it, where can you learn R?

Is it that difficult to learn?

If you had asked me these questions a few years ago, I would have said, “Yes, it’s a little difficult because of the complex structure.” But now packages have been introduced to overcome this problem, making data manipulation easier and more intuitive, and creating graphs much easier.

Packages like TensorFlow and Keras allow you to create high-end ML techniques. Call Python, C++, and Java in R and connect to Hadoop or Spark. And R has also evolved in terms of computational speed.

So, want to learn R?

I think YES!

Let’s find some good resources to learn R.

Data Scientist with R

Get the R skills that will help you build a career as a data scientist with Datacamp . No prior knowledge or experience in this field is required to start the course.

We’ll teach you the versatile R language and how to use it to import, manipulate, visualize, and clean up data (the basic essential skills you need). Interactive exercises give you hands-on experience with popular R packages such as ggplot2 and Tidyverse packages such as readr and dplyr.

This course also introduces several real-world datasets to help you learn the machine learning and statistical techniques you need to create functions and perform cluster analysis all by yourself.

All you have to do is start this course, improve your R skills, and stay on your path to success as a data scientist. Over 75 hours of learning resources are provided. This includes an introduction to the language to master the basics of data analysis using common data structures such as matrices, vectors, and data frames.

R programming AZ

Udemy offers R Programming AZ with practical exercises to help you become a data scientist. This course is divided into 8 sections, 82 lectures, and takes approximately 11 hours to complete.

It teaches R step-by-step, so you’ll learn valuable concepts that you can immediately apply after each lecture. And another great thing is that you can learn concepts using real-life examples. The entire training is full of real-world analytical challenges that you solve during lectures and homework exercises.

This course can be studied by anyone with any skill set, but it requires learning the R language and tackling exciting challenges. In this course material, you will learn its core principles and how to create variables, vectors, loops, and functions.

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

By the end of this course, you’ll have installed R packages and understand big numbers, integers, double-precision floating point, characters, and more. This course also includes advanced visualization using GGPlot2, along with homework solutions and bonus tutorials.

Statistics using R

Coursera offers a course called Statistics with R Specialization to help you master R for data analysis, including modeling, inference, and Bayesian methods. This course is completely free and provided by Duke University.

This course teaches skills such as 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. .

In this specialization, you will learn how to visualize and analyze data and create reproducible reports in the R programming language. Learn how to perform modeling and other techniques to display statistical inference of a unified nature and make data-driven decisions.

This course will also help you communicate your results correctly, use R packages to organize and visualize data, and critique decisions and arguments. It will help you build your portfolio with various projects in data analysis and demonstrate your knowledge and skills as well as land a high-paying job.

This entry-level course takes approximately seven months to complete and features a flexible schedule, fully online lectures, and a certificate to share upon completion.

Introduction to R

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

This is a beginner-level course that takes approximately 2 hours to complete, requires no download, and can only be accessed on your desktop. In this guided project, you’ll learn the basics of R programming to take your first steps into data analysis.

Here you will learn how to use R Studio or R GUI and the various data structures and types used in this language. Finally, we’ll show you how to install R packages and import data sets into your R Studio workspace.

There are no prerequisites required to complete this project. Basic computer knowledge is sufficient. In guided projects, your workspace is a cloud desktop that you can access from your browser. Instructors will guide you through split-screen videos to help you understand step-by-step.

Judas City

Learn R programming to become a data scientist with Udacity. The approximate time for this course is 3 months with 10 hours of weekly study. No difficult prerequisites are required.

This syllabus includes teaching you how to code in R, command line, SQL, and Git to solve data-related problems. Learn SQL basics such as JOINs, subqueries, and aggregations, and use them to solve business problems.

Learn the basics of data structures, loops, functions, variables, etc. In addition to that, you will learn how to visualize data through GGPlot2.

The program includes real-world projects with immersive content developed by experts, mentor support, and career services such as resume and portfolio reviews. Learn on your own schedule and receive personalized feedback, practical tips, and additional suggestions for more resources.

ML Scientist with R

Master the R language with Datacamp and become a confident machine learning scientist. A total of 15 courses are offered with over 60 hours of effort to learn R. Use the toolbox to enhance your R skillset and perform unsupervised and supervised learning.

Learn how to process data to create models, train and visualize models, and test their performance. In addition to this, you can also adjust parameters to improve performance.

At the same time, you will also learn about Bayesian statistics, Spark, and natural language processing (NLP). We teach you the basics of machine learning for classification, how to predict future events through linear regression, random, forest, xgboost, and additive models.

You will also learn about dimensions, clustering, ML on Tidyverse, logistic regression, cluster analysis, ML using carets, tree-based models, support vector machines, topic modeling, hyperparameter tuning, and more.

Data analysis using R

Edureka offers a training program “Data Analytics with R” that helps you gain expertise in data manipulation, visualization, exploratory data analysis, mining, sentiment analysis, and regression.

This training will also help you learn R Studio for social media and retail case studies. We designed this course to provide you with the skills and knowledge you need to become a data analysis professional. Covers basic R concepts to advanced topics such as decision tree ensembles and collaborative filtering.

This module covers important terms such as business intelligence, data and information, and business analytics. As you work on projects, you will learn how to import data, exploratory data analysis, clustering, linear and logistic regression, supervised ML techniques, ANOVA, R packages, creating plots, and more.

A basic knowledge of statistics is required to take this course. It includes 30 hours of online classes with practical assignments to complete after each class. Also includes lifetime access to courses including presentations, class recordings, installation guides, and quizzes. Get a certificate upon completing the course.

YouTube

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

This tutorial covers topics such as how to install R, R Studio, plotting functions, packages, histograms, bar graphs, scatter plots, aggregate functions, overlay plots, and the describe function.

It also explains concepts such as how to select cases, factors, and data formats, how to enter data, how to import data, hierarchical clustering, regression, and principal components.

code academy

Codecademy introduces the basic concepts of the R programming language. There are no specific prerequisites or required coding knowledge to study this course.

Here you’ll learn how to organize data, modify data, and clean up data frames. We also teach you how to build data visualizations and display insights. In addition to this, you will learn excellent hypothesis testing and statistics in the field of data analysis.

The course syllabus also includes the basics of aggregation and table joins using dplyr. Calculate the mode, mean, and median. Statistics such as quartiles, interquartile range, and quantiles.

You can also test your knowledge and strengthen your syntax and memory skills through quizzes. The course takes approximately 20 hours to complete. You get a certificate with the Pro plan.

data mentor

Datamentor courses include unlimited access to over 45 videos, interactive assignments, the R Essentials eBook, and projects.

Understand the basics of data science, its processes, and the various steps you need to take to complete data science tasks such as acquiring data, exploring, modeling, and communicating reports.

conclusion

Learning the R programming language is no longer difficult due to the abundance of resources available. All you need is a passion for learning and a strong desire to explore the field of data science.

So, are you an aspiring data science professional? 💡

Learn R with the great courses listed above.