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About this course

This is the fourth course in our Data Analysis in R career path. Building on the knowledge and skills you developed in the previous three courses, we’ll introduce you to some more advanced R programming skills.

First, you’ll learn techniques like string indexing and concatenation to interpret, process, and analyze text data. Next, you’ll leverage the lubridate package to overcome the unique difficulties of working with dates and times in R. At the end of this course, you’ll complete the second part of our Intermediate R project on building an efficient, reproducible data analysis workflow using R and R Studio.

This course focuses on the following:

  • Developing essential knowledge of interpreting, processing, and analyzing text data
  • Building proficiency at working with strings, dates, and times in R
  • Completing an in-depth data analysis project workflow using R

    Lessons in this course

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    How Our Intermediate R Course Works

    We’re committed to helping you develop cutting-edge and in-demand data analysis and career skills. That’s why we’ve created the Data Analyst in R career path, including this Intermediate R course. R is the programming language of choice for many data analysts because it was built specifically for statistical computing and data analysis.

    If you’re seeking data analysis career skills, you deserve the best possible teaching method to help you achieve your goals. That’s why when you learn with Dataquest, you’ll learn by doing. Unlike most online R courses, we don’t teach with training videos. Instead, you’ll solve real problems using real code, starting day one.

    In this Intermediate R Part II course, you’ll start developing data analysis skills right away. As you complete our series of hands-on, interactive lessons, you’ll learn how to manipulate and analyze strings, work with dates and times, and complete other routine data analysis tasks.

    Learning with Dataquest will also allow you to organically grow your data science portfolio. We know that one of the first and most important things a hiring manager looks for in a potential candidate is a strong portfolio of data science projects. It’s our goal to give you everything you need to land the data analyst position you’ve been waiting for. That’s why all of our courses include practice problems to hone your skills and guided projects to strengthen your portfolio and demonstrate your proficiency.

    Additionally, when you sign up with Dataquest, you’ll become a part of our robust data science community. Whether you need help overcoming an obstacle or a second opinion on your resume, we’re here to help!

    Here’s a glance at our Intermediate R Part II course:

    • This course is the fourth course in the Data Analyst in R Career Path. It consists of the four lessons listed below, which cover in-depth programming concepts in R.
    • You’ll write real code with dozens of practice problems so you can apply your skills.
    • At the end of each course, you’ll complete a guided project to reinforce your new knowledge and expand your portfolio.
    • When you complete this course, you’ll receive a certificate that you can share with your professional network.
    • Once you complete this course, you’ll be ready for more advanced R courses.
    • Engage with our friendly community of data professionals, get feedback on your projects, and keep building your skills.

    Who Is this Intermediate R Course for?

    This R course requires knowledge of fundamental R concepts. Whether you’re a beginner who wants to expand their R skills or an experienced programmer who wants to revisit foundational concepts, this course is for you!

    There are many kinds of people who could benefit from this course — here are a few examples:

    • Data analysis beginners looking for fundamental data analysis knowledge
    • R users seeking a deeper understanding of the inner workings of R
    • People who want a career as a data analyst or statistical programmer
    • People seeking remote work
    • Anyone who works with data in telecommunication, finance, education, and healthcare
    • Junior data analysts or data scientists who want to advance in their current positions
    • Anyone who wants to be able to capture, process, and interpret data
    • Students who want to develop a competitive portfolio
    • Experienced R users who want to fill in the blanks and brush up on the basics

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