Learn Data Manipulation, Visualization, and Analysis in R Programming from Scratch
β±οΈ Length: 1.9 total hours
β 4.60/5 rating
π₯ 603 students
π April 2025 update
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- Course Overview
- Comprehensive Entry into Data Science: This course serves as a dedicated portal for individuals looking to pivot into the world of data analytics and statistical computing without any prior technical background or programming experience.
- Streamlined Learning Methodology: With a runtime of just under two hours, the curriculum is meticulously designed to eliminate unnecessary fluff, ensuring that every minute of your study time contributes directly to your functional mastery of R.
- Industry-Current Standards: Reflecting the April 2025 update, the instructional content integrates the latest best practices, library updates, and coding conventions used in the modern data science industry.
- Foundational Logical Strength: The course emphasizes the core logic of functional programming, helping students understand not just how to write code, but the underlying principles of how R processes information.
- Interactive Coding Experience: The instructional style focuses on a “learn-by-doing” approach, where students engage in follow-along exercises that transition them from passive observers to active practitioners.
- Accessible Technical Training: Specifically tailored for those who find traditional computer science intimidating, the course breaks down complex mathematical and coding concepts into relatable, bite-sized lessons.
- Requirements / Prerequisites
- Zero Prior Knowledge Required: This course is built from the ground up, meaning you do not need to have experience with Python, Java, or any other programming language to succeed.
- Basic Computer Literacy: Students should be comfortable navigating their operating system, downloading software, and managing files on a Windows, macOS, or Linux machine.
- Software Accessibility: Access to a computer with a stable internet connection is necessary to download the R base language and the RStudio Integrated Development Environment, both of which are free.
- Mathematical Foundation: A basic understanding of high-school level arithmetic and logical reasoning will significantly assist in grasping data transformations and statistical functions.
- Commitment to Practice: A proactive mindset and a willingness to troubleshoot common syntax errors are essential, as programming is a skill best learned through trial and error.
- Skills Covered / Tools Used
- RStudio Workspace Mastery: You will gain a thorough understanding of the RStudio interface, including the source editor, the console, the environment pane, and the essential plot viewer.
- Dynamic Data Structures: Deep dives into the construction and manipulation of vectors, matrices, lists, and the highly versatile data frames that serve as the backbone of all R-based analysis.
- Data Type Management: Learn how to correctly identify and handle numeric, character, logical, and factor data types to prevent common processing bugs and analytical errors.
- The Tidyverse Ecosystem: An introduction to the modern suite of R packages, specifically focusing on dplyr for intuitive data filtering, selection, and mutation of large-scale datasets.
- High-Impact Visual Communication: Utilizing the power of ggplot2 to create professional, publication-quality charts including scatter plots, histograms, and customized bar graphs.
- Statistical Computation: Practical implementation of descriptive statistics, allowing you to compute means, medians, variances, and standard deviations directly within the R command line.
- External Data Integration: Techniques for importing raw data from various formats such as CSV and Excel files, as well as exporting your cleaned datasets for external reporting.
- Benefits / Outcomes
- Immediate Career Utility: You will acquire the ability to perform basic data cleaning and visualization, which are high-demand skills in sectors ranging from finance and healthcare to digital marketing.
- Enhanced Academic Research: This course provides a solid foundation for students and researchers who need to move beyond simple spreadsheets to conduct reproducible empirical analysis.
- Workflow Automation: Learn how to automate repetitive data tasks that would normally take hours in manual software, significantly increasing your daily productivity and accuracy.
- Algorithmic Thinking: Beyond just R syntax, you will develop a structured way of thinking that allows you to break down complex problems into small, solvable programmatic steps.
- Portfolio Foundations: By the end of the module, you will have authored several original scripts and visualizations that can be showcased as part of a fledgling data science professional portfolio.
- Global Community Readiness: Completing this course prepares you to join the global R community, enabling you to read technical documentation and participate in developer forums effectively.
- PROS
- Exceptional Time Efficiency: Achieve a functional understanding of a major programming language in less than two hours, making it perfect for busy professionals.
- Proven Quality and Trust: A strong 4.60/5 rating from a diverse group of over 600 students validates the instructor’s clear teaching style and the course’s practical value.
- Modern Relevance: Frequent updates, including the latest 2025 refresh, ensure that you are not wasting time on deprecated functions or outdated packages.
- Low Barrier to Entry: The “Absolute Beginner” focus ensures that no student feels left behind, regardless of their previous exposure to technology or math.
- CONS
- Introductory Scope: Due to its concise and focused nature, this course serves as a foundational launchpad rather than an exhaustive encyclopedia of advanced topics like machine learning or big data architecture.
Learning Tracks: English,IT & Software,Other IT & Software
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