Learn about R programming in detail
β±οΈ Length: 5.9 total hours
β 4.45/5 rating
π₯ 3,066 students
π February 2022 update
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- Course Overview
- Foundational Immersion in Statistical Computing: This comprehensive 5.9-hour course serves as a dedicated gateway into the world of R, a language specifically architected for data manipulation, calculation, and graphical display. It transitions students from basic syntax to logical programming structures, ensuring a robust understanding of how R functions as a backbone for modern data science.
- Structured Learning Path for Non-Programmers: Designed with the absolute beginner in mind, the curriculum avoids overwhelming technical jargon, instead focusing on the core mechanics of the language. It bridges the gap between theoretical data concepts and practical code execution, making it ideal for those transitioning from spreadsheet-based analysis to programmatic workflows.
- Real-World Contextualization: Rather than teaching functions in isolation, the course contextualizes R programming within the broader landscape of analytics. It highlights why R remains a preferred choice for statisticians, researchers, and data analysts globally, emphasizing its reproducibility and the vast ecosystem of open-source packages available to users.
- Updated Content for Modern Standards: With the February 2022 update, the course material reflects contemporary best practices in coding, ensuring that learners are not utilizing deprecated functions but are instead learning the most efficient and readable ways to write R scripts in the current professional environment.
- Requirements / Prerequisites
- No Prior Coding Experience Necessary: The course is built from the ground up, meaning you do not need to have a background in Python, Java, or C++. It is perfectly suited for individuals whose only previous exposure to data might be through basic software like Microsoft Excel or Google Sheets.
- Hardware and Software Setup: Students will need a functional desktop or laptop computer running Windows, macOS, or Linux. The course guides learners through the installation of the R base language and the RStudio Integrated Development Environment (IDE), both of which are free and open-source.
- Basic Quantitative Literacy: While advanced mathematics is not required, a comfortable grasp of basic arithmetic and a logical mindset will significantly accelerate the learning process. An interest in how data can be used to answer questions is the primary driver for success in this module.
- Commitment to Hands-On Practice: Because programming is a kinesthetic skill, the course requires a willingness to engage in typing code alongside the instructor. Passive watching is discouraged in favor of active experimentation within the RStudio console.
- Skills Covered / Tools Used
- Mastering the RStudio Interface: Participants will learn to navigate the complexities of the RStudio IDE, including the source editor, the environment pane for variable tracking, and the console for immediate command execution, which is essential for a professional coding workflow.
- Data Structures and Object Types: Detailed instruction is provided on the fundamental building blocks of R, including atomic vectors (numeric, character, logical), matrices for multidimensional data, lists for heterogeneous collections, and the indispensable data frame for tabular data management.
- Algorithmic Logic and Control Flow: The course delves into the logic of programming by teaching students how to implement “if-else” statements for conditional execution and “for” or “while” loops to automate repetitive tasks, which are critical skills for scaling data analysis.
- Introduction to the Tidyverse Ecosystem: Learners are introduced to the modern “Tidyverse” approach to data science, specifically focusing on data manipulation using the ‘dplyr’ package and the “grammar of graphics” for sophisticated data visualization via the ‘ggplot2’ library.
- Functional Programming Basics: Beyond using built-in commands, students learn the art of writing their own custom functions. This allows for the creation of reusable code blocks that can simplify complex analytical pipelines and reduce the likelihood of manual errors.
- Benefits / Outcomes
- Transformation of Raw Data into Insight: By the end of the course, students will possess the capability to import messy datasets, clean them using programmatic methods, and extract meaningful summaries that can drive business or academic decisions.
- Enhanced Professional Versatility: Mastering R opens doors to high-demand roles in bioinformatics, financial modeling, and social science research. This course provides a tangible skill set that can be added to a resume to demonstrate technical proficiency in a data-driven job market.
- Preparation for Advanced Analytics: This course acts as a prerequisite for more complex topics such as machine learning, predictive modeling, and big data processing. It establishes the “coding muscle memory” needed to tackle sophisticated statistical packages without being hindered by basic syntax errors.
- Development of Reproducible Workflows: One of the primary outcomes is the ability to create scripts that document every step of an analysis. This ensures that findings are transparent and can be easily replicated by colleagues, a standard requirement in professional data science environments.
- PROS
- Highly Rated Instruction: With a 4.45/5 rating, the course is recognized for its clarity and the instructor’s ability to demystify complex coding concepts for a large student body of over 3,000 learners.
- Optimal Course Length: At nearly 6 hours, the course is concise enough to be completed in a weekend but deep enough to cover the essential mechanics of R without skipping over foundational theory.
- Practical Orientation: The focus on RStudio and modern packages like those in the Tidyverse ensures that the skills learned are immediately applicable to current industry standards.
- CONS
- Scope Limitations: As a beginner-focused course, it primarily targets foundational syntax and data handling, meaning those looking for deep dives into specific machine learning algorithms or complex API integrations may need to seek supplementary advanced coursework.
Learning Tracks: English,Development,Programming Languages
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