R is a programming language that has been around since the 1990s. It incorporates machine learning algorithms, data analysis, statistical inferences, and more. Companies and organizations in the academic, healthcare, and government industries use R programming the most, as it is a preferred language for statistics and visualization. Although R is not the easiest programming language to learn, many great courses will help you understand it. This article will cover the top R Programming courses to help you learn it.

What to Expect from R Programming Courses

Learning R can open many doors in data analysis, machine learning, and statistical modeling. As a language geared towards data science, R provides robust functionality for statistical inference and machine learning, but it’s not always the easiest to learn. Fortunately, many courses offer structured, step-by-step guidance, from foundational topics to advanced techniques.

Here’s a look at some of the best R programming courses, including essential details like course length, prerequisites, pricing, and learning style.

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Video Hours	64 Hours	Approximately 57 Hours	Around 8 Hours	2-4 Hours a Week for 4 Months	10.5 Hours
Length of Access	365 Days	4 Weeks	N/A	4 Months	Unlimited
Prerequisites	None	Intermediate Level Skills Required	Fundamental Knowledge of Computer Programming and Creating Variables	None	None
Learning Style	Online Self-Learning	Online Courses, Flexible Deadlines, 4-Week Duration	Online Self-Learning	Self-Paced Online Learning	Online Learning At Your Own Pace
Pricing	$450	$39	$45/month, Free Trial Available	$50 – $300	$89.99

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R Programming Certification Courses Reviews

1. Dooey: Data Science With R Programming

The Dooey: Data Science With R Programming course is more than just a programming class; it’s a comprehensive dive into data science through the lens of R programming. This course is designed not only to teach R but also to equip learners with the skills they need to work with data science in various professional environments. Here’s a breakdown of what this course offers and why it’s a solid choice for anyone looking to master R and apply data science techniques.

Key Topics Covered

The course spans 64 hours of online learning and covers:

1. Data Science Essentials: Learn the fundamentals of data science, including practical applications and industry insights.
2. Data Visualization: Understand how to create powerful, clear visualizations that make data insights more accessible.
3. Predictive Analytics: Gain insights into predictive models, helping you make informed decisions based on data trends.
4. Data Structures in R: Develop a strong foundation in R’s unique data structures, including vectors, data frames, and lists.

Extensive Learning Time with Flexible Access

With 64 hours of content, this course provides significantly more material than most other options. This extended learning time means that complex topics are explored in depth, allowing for a more thorough understanding. Dooey also provides 365 days of access, letting you work at your own pace and revisit topics as needed, which is ideal if you want a less rushed, in-depth learning experience.

Hands-On Learning with Real-Life Industry Projects

One standout feature of the Dooey course is its ten real-life industry projects. These projects simulate actual scenarios you may encounter in a data science role, providing hands-on experience that bridges the gap between theoretical learning and practical application. This real-world experience is invaluable, particularly for beginners, as it allows you to apply your skills in meaningful ways. Few courses offer this level of practical application, making Dooey a strong choice for those looking to build job-ready skills.

No Prerequisites – Ideal for Beginners

One of the most appealing aspects of this course is that there are no prerequisites. You don’t need a background in programming or data science to start, making it ideal for complete beginners. This approachability sets the course apart, as it teaches foundational concepts from the ground up. If you’re new to data science and interested in learning R programming as a starting point, Dooey is structured to support you right from the basics.

Who Can Benefit?

While the course is designed for beginners, it’s also suitable for those already working in technical fields who want to expand their data science knowledge. Here are some professionals who might find this course especially beneficial:

• Software Developers: Learn data science principles and R programming skills that can expand your project capabilities.
• IT Professionals: Gain insights into data analytics to enhance your current role or pursue new opportunities.
• Analytics Professionals: Refine your skills with R and broaden your understanding of data visualization and analytics.

Course Structure and Goals

The course is organized into ten lessons, each focused on different levels of data science and R programming. You’ll progress from foundational knowledge to more advanced concepts, ensuring a well-rounded education. Some key learning goals include:

• Using Association Rules with the Apriori Algorithm: This widely used algorithm in data mining helps you understand association rules, which are crucial for predictive analytics.
• Graphics in R for Data Visualization: Learn how to create effective graphics in R, enhancing your data storytelling and analytical presentations.

Cost and Value

Priced at $599, this course may seem like an investment, but it provides considerable value. With a full year of access, extensive video content, and hands-on projects, you’re getting a well-rounded educational experience that’s likely to pay off in terms of skills and career advancement.

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2. Coursera: R Programming By Johns Hopkins University

The R Programming course on Coursera, offered by Johns Hopkins University, is a respected choice for those wanting to advance their R programming skills within a structured, rigorous framework. Part of the larger Data Science Specialization and Data Science: Foundations Using R Specialization, this course serves both as a standalone learning experience and a stepping stone toward professional certification in data science. With its high-quality instruction and comprehensive curriculum, it has drawn hundreds of thousands of learners worldwide.

Key Features and Accessibility

This course is accessible to a broad audience, with features designed to support international learners:

• Language Support: Though the primary language is English, subtitles are offered in multiple languages, including Arabic, French, Portuguese, Chinese (Simplified), German, Russian, and more. This makes the course accessible for non-English speakers and widens its appeal to students globally.
• Financial Aid: Coursera provides financial aid to eligible students, making this a budget-friendly option for learners who qualify. And, while there is a fee for those pursuing certificates, you can audit the course for free, allowing you to access core content without cost.

Learning Outcomes and Career Impact

Designed to elevate your R skills to an intermediate level, this course targets practical outcomes in data science, including:

1. Mastery of R Loop Functions: Develop a solid understanding of loop functions, which are essential for automating tasks and processing data efficiently.
2. Profiling and Performance Analysis: Learn how to use R profilers to monitor and optimize code performance, a valuable skill for data-heavy programming tasks.

Career Benefits: According to Coursera, 70% of students report career gains, from promotions and pay raises to new job opportunities, underscoring the course’s potential to make a tangible impact on your professional journey.

Instructors

The course is led by three esteemed professors from Johns Hopkins University’s Bloomberg School of Public Health. These instructors bring both academic rigor and practical insights to the course, creating a learning experience grounded in real-world data science applications. Their expertise is a major reason behind the course’s high enrollment numbers and positive reputation.

Course Structure

The course is organized into a four-week module structure, each focusing on different aspects of R programming. The content combines video lectures, readings, and quizzes, offering a flexible learning experience that allows you to set your own deadlines:

• Week 1: Background, Getting Started, and Nuts & Bolts
  This introductory week covers the foundational aspects of R, including setup and basic concepts. This module is designed to get you up and running with R and introduce you to the course’s structure.
• Week 2: Programming With R
  In this week, you’ll dive deeper into R’s programming capabilities. Topics include basic syntax, functions, and control structures, giving you a solid base for writing functional R code.
• Week 3: Loop Functions and Debugging
  This week focuses on loop functions, such as apply, lapply, and sapply, which are critical for performing repetitive tasks efficiently. You’ll also learn debugging techniques, helping you troubleshoot issues effectively as you write more complex code.
• Week 4: Simulation and Profiling
  The final week introduces you to simulation and profiling, where you’ll learn to simulate data and optimize your code’s performance. These skills are essential for real-world applications, especially in research and analytics.

Best Fit for Intermediate Learners

While highly informative, this course is best suited for intermediate learners. Some prior knowledge of programming or basic statistics, especially regression, is recommended to fully benefit from the material, although not strictly required. This background will help you understand the course’s advanced concepts more comfortably.

Flexible and Self-Paced Learning

The four-week course structure is designed for flexibility, allowing you to complete assignments at your own pace. The immersive experience includes multiple forms of assessments and self-study tools, creating an engaging environment that lets you balance learning with other commitments.

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3. PluralSight: Programming R For Data Analysts

The PluralSight: Programming R for Data Analysts course is designed specifically for those who want to use R programming in data analysis. It covers a range of topics from beginner to advanced levels, making it a great choice for those looking to build a structured understanding of R’s analytical capabilities. However, this course assumes you have some programming experience, particularly with creating variables and understanding programming basics. Here’s a closer look at what this course offers and why it might be right for you.

Course Structure: Beginner to Advanced in Three Stages

PluralSight’s course is divided into three distinct stages, making it easy to progress from foundational knowledge to more complex applications in data analysis:

1. Beginner Level:
   • Building Your First R Analytics Solution: This section helps you get started with a complete solution, introducing you to R’s environment and core concepts.
   • Programming with R: Learn essential programming elements in R, including syntax, functions, and the structure of R scripts, which sets a strong foundation for further exploration.
2. Intermediate Level:
   • Creating and Debugging R Programs: This lesson dives into practical programming, teaching you how to troubleshoot, debug, and enhance code efficiency—a vital skill for data analysis.
   • Customizing R Environments: Gain knowledge on how to set up R environments tailored to your specific data needs, allowing for a more streamlined workflow.
3. Advanced Level:
   • Building Tables with R: Learn how to create and manipulate tables, a crucial aspect of data analysis for handling and presenting data effectively.
   • Applying Functions in R: Master advanced functions and apply them to solve real-world analytical problems, a valuable skill for optimizing and automating analysis.

These stages ensure a smooth transition from introductory R concepts to advanced data analysis techniques, covering the core skills that every data analyst needs.

Course Duration and Format

The entire course lasts about eight hours. This makes it one of the shorter R programming courses available. It includes six videos, each between 30 minutes and two hours. This format is ideal for those seeking a focused, time-efficient course. You can complete it over a few days or weekends. It’s perfect for busy professionals wanting to fit learning into their schedules.

Hands-On Learning with Practical Examples

The course does not include real-life projects like some others. However, it features numerous practical examples and exercises. These reinforce your learning. The structured approach ensures you’re not just watching. You will actively apply concepts as you learn. This hands-on element makes transitioning to real-world data analysis with R much smoother.

Prerequisites: Not for Absolute Beginners

It’s important to note that this course requires basic programming knowledge. Familiarity with variables and general programming principles is essential. If you’re new to programming, some concepts may be challenging. However, if you have any programming experience, even at a basic level, you will likely find this course manageable and beneficial.

Subscription Model and Access

PluralSight uses a subscription model. For $159 per year, you gain unlimited access to their entire course library, including this R programming course. This model is advantageous if you want to explore other topics. You can branch out into areas like Python, SQL, or data visualization under the same subscription.

Who Should Consider This Course?

This course is a good fit for:

• Aspiring Data Analysts: Especially if you already have some programming experience and want to upskill with R for data-focused roles.
• Working Professionals: Those in analytics or related fields who need a concise yet comprehensive R programming course that they can complete quickly.
• Programmers New to R: If you’re familiar with programming and want to quickly learn R for data analysis, this course will help you get started effectively.

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4. EDX: Master Key Concepts Using the R Programming Language

The Master Key Concepts Using the R Programming Language course from Harvard University is a specialized option offered through EDX, focusing on data analysis techniques tailored for the life sciences. It’s an in-depth choice within Harvard’s Professional Certificate in Data Analysis for Life Sciences program, making it particularly appealing if you’re aiming for a career in data analytics within fields like biology, genetics, or bioinformatics. This program blends advanced analytics techniques with foundational mathematical concepts, delivering rigorous training from a prestigious institution.

Course Focus and Learning Outcomes

This program covers four core courses, each designed to build your analytical and programming skills in R while emphasizing applications in the life sciences:

1. Statistics and R: Introduces the basics of statistics in R, setting the groundwork for data analytics applications in scientific research.
2. Introduction to Linear Models and Matrix Algebra: Covers essential linear algebra concepts and introduces matrix algebra, vital for handling large datasets in bioinformatics and similar fields.
3. Statistical Inference and Modeling for High-Throughput Experiments: Explores statistical models tailored for high-throughput biological experiments, allowing you to analyze large data volumes with confidence.
4. High-Dimensional Data Analysis: Tackles methods for analyzing high-dimensional datasets, a frequent requirement in genetics and genomics research.

These topics are taught by experienced Harvard faculty, including a professor and associate professor from HarvardX, offering a blend of theoretical and practical expertise. This program is ideal if you’re looking for a reputable, structured way to learn advanced R programming within a specialized life sciences context.

Flexible Schedule for Busy Learners

Each course in this program lasts four weeks, with an estimated commitment of 2–4 hours per week. This structure provides flexibility if you’re working or balancing other commitments. The entire program runs for about four months, allowing you to gain a professional-level certificate without a full-time time commitment. You can learn at your own pace within these weekly timeframes, which makes it accessible to those with limited time.

Real-World Application: Ideal for Life Sciences

The focus on life sciences makes this program uniquely valuable for those in or entering fields like biology, medical research, or pharmaceutical analytics. You’ll gain skills that are directly applicable to scientific research settings, especially those requiring high-dimensional data analysis or complex statistical modeling. This targeted approach means that the material is highly relevant to real-world applications, giving you practical insights into handling and interpreting life sciences data.

Investment and Value

The program is priced at $696 for the complete set of four courses. While this is a one-time payment, it includes access to all the course materials and assessments across the four months. You cannot purchase individual courses within the program, so this is an all-inclusive commitment. However, considering the high-caliber instruction and targeted life sciences focus, it’s a worthwhile investment for those looking to enhance their professional qualifications.

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5. Udemy: R Programming For Data Science With Real Exercises!

The Udemy: R Programming For Data Science With Real Exercises! course is a practical, beginner-friendly course designed for those who want to learn R programming at their own pace. Offering lifetime access, it’s perfect for individuals who prefer a flexible, non-traditional learning environment. Here’s a breakdown of what this course provides and why it might be a good fit for you.

Key Course Highlights

The course is structured to introduce you to fundamental R programming skills with a focus on hands-on practice:

1. Programming in R: Learn the basics of coding in R, including syntax, functions, and data handling techniques.
2. Using R Studio: Discover how to navigate and use R Studio, a popular integrated development environment (IDE) for R programming.
3. Working with Various Data Types: Gain experience working with different types of data, including financial, sports, and general data, providing a well-rounded view of R’s applications.

Lifetime Access and Flexible Learning

Unlike traditional courses that impose deadlines, Udemy courses allow you to learn at your own pace with lifetime access. Once you purchase the course, it’s yours to revisit as often as you like, with no expiration date. This makes it ideal if you have a busy schedule or prefer a relaxed learning approach, as there’s no pressure to complete it within a specific timeframe.

Short yet Comprehensive Structure

With a total of 10.5 hours of content, this course is relatively short, making it accessible for those with limited time. Despite its compact duration, it’s comprehensive and well-organized:

• Eight Sections: Each section builds on the previous one, helping you gradually increase your knowledge.
• 82 Lectures: The course is broken down into manageable lectures, allowing you to work through the material in digestible chunks.

Additionally, the course includes real exercises, so you can practice as you go. This hands-on approach is particularly useful for solidifying your understanding of R programming through real-world applications.

Ideal for Complete Beginners

One of the strengths of this course is its accessibility to beginners. You don’t need any prior programming experience to start, making it an excellent option for those who are new to both coding and data science. The course is straightforward, guiding you step-by-step through each concept, ensuring a smooth learning experience for newcomers.

Mobile-Friendly Learning

The course is available on the Udemy app, which allows you to learn on the go. You can watch lessons on your mobile phone, enabling you to turn idle time into productive learning moments, whether you’re commuting, waiting in line, or simply away from your computer.

Affordable Pricing with Frequent Discounts

This course is priced at $89, but Udemy often offers site-wide discounts, sometimes bringing the price down to as low as $12.99. This affordability makes it a great option if you’re on a budget but still want access to high-quality learning materials. The frequent discounts make it a particularly cost-effective choice.

Outcomes and Goals

By the end of the course, you should be able to:

• Program effectively in R: Gain confidence in using R for various data science tasks.
• Navigate R Studio: Use R Studio to write and manage code efficiently.
• Work with Diverse Data Types: Apply R to analyze different kinds of datasets, making it a versatile tool for data science projects.

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Frequently Asked Questions (FAQs) for Best R Programming Training Courses

1. What is R programming, and why is it popular?

R is an open-source programming language used mainly for statistical computing, data analysis, and graphical representation. Developed in the 1990s, it has become essential in fields like academia, healthcare, government, finance, and technology. R’s popularity stems from its capabilities in statistical modeling, machine learning, and data visualization. It allows users to handle large datasets and perform complex analyses efficiently. R is also compatible with Windows, Mac, and Linux, making it versatile across industries and research areas.

2. Is R programming difficult to learn?

R can be challenging for beginners, especially those with no programming experience. Its syntax and data handling methods differ from other languages, requiring a learning curve. However, many courses make it easier by offering step-by-step guidance, practical projects, and real-world applications. Beginner-friendly courses, like Dooey’s Data Science with R Programming, introduce foundational concepts gradually, helping new learners build essential skills before tackling advanced topics.

3. What are the benefits of taking an R programming course?

An R programming course offers a structured way to learn data analysis, statistical modeling, and visualization. These courses often include hands-on projects, allowing you to apply your knowledge to real-world data problems. Completing a recognized R course can also enhance your resume, showcase your data science skills, and boost your qualifications for data-focused roles. This may lead to career advancement or higher salary prospects.

4. Which industries commonly use R programming?

R is widely used in data-heavy fields, including academia, healthcare, government, finance, and tech. For example, healthcare researchers use R for studies and clinical trials. Financial analysts rely on it for risk assessment and forecasting. In academia, R supports statistical research and data science education. Government agencies use it for public health and socio-economic studies thanks to its robust data handling and statistical capabilities.

5. What factors should I consider when choosing an R programming course?

Answer:
When choosing an R programming course, consider the following factors:

• Skill Level: Courses vary in difficulty, from beginner-friendly to advanced. Identify a course that matches your current skill level.
• Learning Style and Format: Some courses are self-paced, while others follow a structured schedule. Consider your learning preference and availability.
• Budget: Course prices range from free options to higher-cost certifications. Evaluate your budget and explore financial aid options if necessary.
• Course Duration: If you have limited time, a shorter course may be more practical. However, longer courses tend to be more comprehensive.
• Certification Options: A certificate can be useful for career progression. Confirm if the course offers an official certificate upon completion.

6. Are there prerequisites for learning R?

Not all R courses have prerequisites. For instance, beginner-oriented courses like Dooey and Udemy require no prior programming knowledge, while courses like Pluralsight recommend a basic understanding of computer programming and variable creation. Checking each course’s prerequisites before enrolling ensures that you choose one suitable for your background and learning objectives.

7. How long does it take to learn R programming?

The time required to learn R varies based on the course and individual learning speed. Courses in this article range from shorter options, like Pluralsight’s 8-hour course, to more extensive programs like Harvard’s 4-month EDX program. Generally, with consistent practice, beginners can start grasping R fundamentals within a few weeks, while proficiency in advanced concepts may take a few months.

8. Can I learn R programming at my own pace?

Yes, many R Programming Courses offer a self-paced format. Platforms like Udemy and Pluralsight provide on-demand access to course materials, allowing you to progress at your own speed. Other courses, such as Harvard’s EDX program, follow a structured schedule, making them better suited for learners who prefer guided, time-bound instruction. Self-paced courses are ideal for busy individuals who need flexibility in their learning journey.

9. Are R programming courses expensive?

The cost of R programming courses varies significantly. Some platforms, such as Coursera, allow users to audit courses for free, with an option to pay for certification. Prices for premium courses range from $89 (Udemy) to $696 (Harvard’s EDX program). Subscription models like Pluralsight’s $159/year plan also offer access to a range of courses, making it a cost-effective choice for those interested in multiple subjects.

10. Can I get certified in R programming?

Yes, many R programming courses offer a certification upon completion. Certifications are valuable for showcasing your skills to employers and can be beneficial if you’re transitioning into a data science career. Dooey, Harvard EDX, and Coursera all offer recognized certificates, which can help improve your resume and demonstrate your proficiency in R programming and data analysis.

11. What skills can I expect to gain from an R programming course?

R programming courses typically cover skills like data manipulation, data visualization, statistical modeling, and RStudio programming. Advanced courses may delve into topics such as machine learning, predictive analytics, data structures, and the application of statistical methods in real-world scenarios. By the end of a course, you should be able to clean, analyze, and visualize data, as well as build statistical models using R.

12. Are there any free resources for learning R programming?

Yes, some platforms offer free trials and financial aid for learners. Coursera’s R programming course from Johns Hopkins University, for example, can be audited for free, with the option to pay if certification is desired. Additionally, various online tutorials, open-source documentation, and resources on platforms like RStudio and CRAN provide valuable, free learning materials.

Conclusion

In today’s data-driven world, proficiency in R programming is an invaluable asset. It benefits professionals across various industries. Whether you are an aspiring data scientist or a seasoned analyst, learning R can broaden your career prospects. It also deepens your understanding of data analysis.

The courses highlighted in this article provide a diverse range of learning experiences. They cater to different skill levels, budgets, and learning styles. You can choose from comprehensive programs offered by reputable institutions like Harvard and Johns Hopkins University. There are also flexible options on platforms like Udemy and Pluralsight. This variety ensures that there is a suitable course for everyone, regardless of prior programming experience.

By selecting the right course and committing to continuous practice, you can master R programming. This will unlock its powerful capabilities for data visualization, statistical modeling, and machine learning. As you embark on this journey, you will acquire essential technical skills. You will also gain the confidence to tackle complex data challenges in your career.

In summary, investing in R programming courses is a crucial step toward becoming a proficient data analyst or scientist. With the right course and resources, you can harness R’s full potential. This will enable you to drive insightful data analysis and make informed decisions. Such decisions can positively impact your organization and career. Start your journey today. Equip yourself with the tools necessary to excel in the ever-evolving field of data science.