Course syllabus

In previous courses, you learned how to use structured thinking to solve business problems; prepare, clean, transform, and analyze data in spreadsheets and databases; and tell effective data stories. As part of your growing skillset, you also learned how to create dynamic and interactive data visualizations in Tableau. Up until now, the skills you learned were closely tied to the features and capabilities available in spreadsheets, SQL databases, and Tableau. But what if you want to work with your data in more custom ways? Or what if the standard tools don’t have the exact functionality you need? This is when the R programming language can be super helpful. Using R, you will gain additional flexibility and control over your data and analysis.

1. Foundations: Data, Data, Everywhere
2. Ask Questions to Make Data-Driven Decisions
3. Prepare Data for Exploration
4. Process Data from Dirty to Clean
5. Analyze Data to Answer Questions
6. Share Data through the Art of Visualization
7. Data Analysis with R Programming (this course)
8. Google Data Analytics Capstone: Complete a Case Study

In this course, you will learn how to use the R programming language to work with your data without tool limitations. You will get plenty of practice using R for statistical analysis, and RStudio—an integrated developer environment (IDE) for R that you will use to create advanced data visualizations with lots of detail. R makes it easier to present your data with beautiful, artistic style. A few other advantages of R include its:

• Popularity: R is frequently used for data analysis
• Tools: R has a convenient library of ready-to-use tools for data cleaning and analysis
• Focus: R was created with statistics in mind; data analysts can conveniently use a rich library of statistical routines
• Adaptability: R adapts well for use in both machine learning and data analysis projects
• Availability: R is an open source programming language

After you get comfortable and more confident using R and RStudio, you might find that you are curious to learn and add even more programming languages to your skillset (and resume). Pretty exciting, right?

Course content

Course 7 – Data Analysis with R Programming

1. Understanding the basics of R: R is a programming language that can be used to perform tasks in every phase of the data analysis process. In this part of the course, you will learn about R and RStudio, an integrated developer environment (IDE) for R. You will explore the benefits of using RStudio to work with R. RStudio enables you to easily leverage the features and functionality of R.
2. Programming using RStudio: In this part of the course, you will explore the fundamental concepts associated with R. You will learn about functions and variables that you can use in your calculations and other programming. You will also learn about R packages, which are collections of R functions, code, and sample data that you can use in RStudio.
3. Working with data in R: The R programming language was designed to work with data at all stages of the data analysis process. In this part of the course, you will examine how R can help you structure, organize, and clean your data through functions and other processes. You will learn about data frames and how to work with them in R. You will also revisit the concept of data bias and how you can use R to address it.
4. Visualizations, aesthetics, and annotations: R is a great tool for creating detailed visualizations. In this part of the course, you will learn how to use R to generate and troubleshoot visualizations. You will also explore the features of R and RStudio that can help you improve the aesthetics of your visualizations. You will learn how to annotate visualizations and save the changes.
5. Documentation and reports: R has a number of different options to explore when you are ready to save and present your analysis. In this part of the course, you will explore R Markdown, a file format for making dynamic documents with R. You will learn how to format and export R Markdown and incorporate R code chunks in your documents.
6. Course challenge: At the end of the course you will apply everything you have learned in the Course Challenge. The Course Challenge will ask you questions about the key skills you have been practicing and will give you an opportunity to demonstrate those skills in three scenarios.

Are you already familiar with R programming?

If you have used R and RStudio before, you might find the first two weeks of this course a review of basic topics that you already understand. Feel free to skip these foundational videos and readings and proceed to the weekly challenges for Week 1 and Week 2. The weekly challenges will help prepare you for the course challenge at the end of this course. To earn the certificate, you need to score 80% or higher on all graded activities in the program.

What to expect

You can expect to finish this course in about four to five weeks. That involves completing all the activities, including:

• Videos of instructors teaching new concepts and demonstrating the use of tools
• In-video questions that pop up during or at the end of a video to check your learning
• Readings to introduce new ideas and build on the concepts from the videos
• Discussion forums to discuss, explore, and reinforce new ideas for better learning
• Discussion prompts to promote thinking and engagement in the discussion forums
• Qwiklabs to introduce real-world, on-the-job situations, and the tools and tasks to complete assignments
• Practice quizzes to prepare you for graded quizzes
• Hands-on activities to reinforce learned skills for the graded quizzes
• Graded quizzes to measure your progress and give you valuable feedback

Hands-on activities promote additional opportunity to build your skills, so try to get as much out of them as possible. Assessments are based on the approach taken by the course to offer a wide variety of learning materials and activities that reinforce important skills. Graded and ungraded quizzes will help the content sink in and reinforce important skills. Ungraded practice quizzes are a chance for you to prepare for the graded quizzes, and both the graded and ungraded quizzes can be taken more than one time.

As a quick reminder, this course is designed for all types of learners, so no degree or prior experience is required. Everyone learns differently, and the Google Data Analytics Certificate has been designed with that in mind. Personalized deadlines are just a guide, so feel free to work at your own pace. There is no penalty for late assignments. If you prefer, you can extend your deadlines by returning to Overview in the navigation pane and clicking Switch Sessions. If you already missed previous deadlines, click Reset my deadlines instead.

If you would like to review previous content or get a sneak peek of upcoming content, you can use the navigation links at the top of this page to go to another course in the program. When you pass all the required assignments, you will be on track to earn your certificate.

Tips

• Try to complete all the activities in order, since new information always builds on previous lessons.
• Treat every task as if it is real-world experience. Have a mindset that you are working at a company or in an organization as a data analyst. This will help you apply what you learn in this program to the real world.
• Repeat demonstrated tasks on your own for extra practice and speed. For example, after you follow along with a video once or twice to perform the demonstrated tasks, try performing the same tasks without playing the video and receiving help from the instructor’s prompts.
• Even though they aren’t graded, be sure to participate in and complete all of the practice activities. They will help you build a strong foundation as a data analyst and prepare you for the graded assessments.
• Take advantage of all the additional resources provided, including discussion forums and links to external articles for more information.
• When you encounter useful links in the course, remember to bookmark them so you can refer to the information for study or review.
• Additional resources are free, but some sites place limits on how many articles you can access for free each month. Sometimes you can register on the site for full access, but you can always bookmark a resource and come back to view it later.
• Maximize the value of hands-on activities. Hands-on activities supplement the demonstrated tasks by encouraging additional practice with similar scenarios. A programming language’s syntax will become more natural to you the more you practice using it.
• Create a notebook or document to keep track of things to remember about the R syntax. This will become a handy and personalized reference that you can use throughout the rest of the program and anytime later.

The R-versus-Python debate

People often wonder which programming language they should learn first. You might be wondering about this, too. This certificate teaches the open-source programming language, R. R is a great starting point for foundational data analysis, and it has helpful packages that beginners can apply to projects. Python isn’t covered in the curriculum, but we encourage you to explore Python after completing the certificate. If you are curious about other programming languages, make every effort to continue learning.

Any language a beginner starts to learn will have some advantages and challenges. Let’s put this into context by looking at R and Python. The following table is a high-level overview based on a sampling of articles and opinions of those in the field. You can review the information without necessarily picking a side in the R vs. Python debate. In fact, if you check out RStudio’s blog article in the Additional resources section, it’s actually more about working together than winning a debate.

Languages	R	Python
Common features	- Open source - Data stored in data frames - Formulas and functions readily available - Community for code development and support	- Open-source - Data stored in data frames - Formulas and functions readily available - Community for code development and support
Unique advantages	- Data manipulation, data visualization, and statistics packages - "Scalpel" approach to data: find packages to do what you want with the data	- Easy syntax for machine learning needs - Integrates with cloud platforms like Google Cloud, Amazon Web Services, and Azure
Unique challenges	- Inconsistent naming conventions make it harder for beginners to select the right functions - Methods for handling variables may be a little complex for beginners to understand	- Many more decisions for beginners to make about data input/output, structure, variables, packages, and objects - "Swiss army knife" approach to data: figure out a way to do what you want with the data

Additional resources

For more information on comparing R and Python, refer to these resources:

• R versus Python, a comprehensive guide for data professionals : This article is written by a data professional with extensive experience using both languages and provides a detailed comparison.
• R versus Python, an objective comparison : This article provides a comparison of the languages using examples of code use.
• R versus Python: What’s the best language for data science? : This blog article provides RStudio’s perspective on the R vs. Python debate.

Key takeaways

Certain aspects make some programming languages easier to learn than others. But, that doesn’t make the harder languages impossible for beginners to learn. On the flip side, a programming language’s popularity doesn’t always make it the best language for beginners either.

R has been used by professionals who have a statistical or research-oriented approach to solving problems; among them are scientists, statisticians, and engineers. Python has been used by professionals looking for solutions in the data itself, those who must heavily mine data for answers; among them are data scientists, machine learning specialists, and software developers.

As you grow as a data analytics professional, you may need to learn additional programming languages. The skills and competencies you learn from your first programming experience are a good foundation. That's why this course focuses on the basics of R. You can develop the right perspective, that programming languages play an important part in the data analysis process no matter what job title you have.

The good news is that many of the concepts and coding principles that you will learn from using R in this course are transferable to other programming languages. You will also learn how to write R code in an Integrated Development Environment (IDE) called RStudio. RStudio allows you to manage projects that use R or Python, or even a combination of the two. Refer to RStudio: A Single Home for R & Python for more information. So, after you have worked with R and RStudio, learning Python or another programming language in the future will be more intuitive.

For a better idea of popular programming languages by job role, refer to Ways to learn about programming . The programming languages most commonly used by data analysts, web designers, mobile and web application developers, and game developers are listed, along with links to resources to help you start learning more about those languages.

Learning Log: Get ready to explore R

Overview

So far, you’ve learned about data management, analysis, and visualization. In this upcoming course, you will learn the fundamentals of R and how it can be used to perform the same data analysis steps you already know. Now, you’ll complete an entry in your learning log considering R as a tool for data analysis. By the time you complete this entry, you will be ready to start learning this new and exciting tool!

R you ready?

Before you start writing your learning log entry in the template linked below, let’s discuss what exactly R is. R is a programming language used for statistical analysis, visualization, and other data analysis. As a data analyst, you will use R to complete many of the tasks associated with the data analysis process. Understanding how it works and why you use it is crucial to developing a mastery of data analytics.

Like the other tools you have already learned in this program, R will be an important part of your data analysis toolkit. You don’t need any previous experience with R for this course; you’ll get a chance to learn the basics and practice writing R code yourself. Then, you can even try using R for your capstone project later!

In the learning log template below, you’ll get a chance to reflect on learning R and what you’re most excited about for this course.

Access your learning log To use the template for this course item, click the link below and select “Use Template.”

Link to learning log template: Get ready to explore R

OR

If you don’t have a Google account, you can download the template directly from the attachment below.

Learning Log Template_ Get ready to explore R

Reflection

Take a moment to reflect on learning about R. In your learning log template, write 2-3 sentences (40-60 words) in response to each question below:

• Which parts of R are you excited to learn about?
• Which parts seem most challenging?

When you’ve finished your entry in the learning log template, make sure to save the document so your response is somewhere accessible. This will help you continue applying data analysis to your everyday life. You will also be able to track your progress and growth as a data analyst.

Meet and greet

In this course, you’ll learn how to use R to supercharge your data analysis. You’re about to embark on an exciting and rewarding learning journey!

Please write 2-3 sentences (20-60 words total) to check in with your fellow learners, exchange ideas, and introduce yourself if you’re new to the course. Feel free to craft your own response, or use the following questions for prompts:

• What is your professional background?
• What led you to enroll in this course?

Then, add 3-5 sentences (60-100 words total) sharing your thoughts on programming. Feel free to craft your own response, or use the following questions for prompts:

Background in programming:

• What programming languages have you worked with?
• What personal or professional projects have you used programming for?
• What do you enjoy most about programming?
• If you are new to programming, what are your feelings about learning how to program using R—are you excited, a little nervous, or both?

Finally, visit the discussion forum to read what other learners have written, engage with two or more posts, and share your feedback.