Master statistics using R: Coding, concepts, applications

Unlock the power of data by learning statistics the modern way—hands-on, intuitive, and with real-world tools. This course is designed for students, researchers, and professionals who want to move beyond memorizing formulas and truly understand how to analyze data. Using R programming and the tidyverse, you’ll build both the coding fluency and the statistical intuition you need to work like a real analyst.

We’ll start at the ground level: organizing messy datasets into tidy data, writing clean and reproducible code, and visualizing information effectively. From there, you’ll gain practical experience with the logic of inference—sampling variability, distributions, confidence intervals, and hypothesis testing—through approachable, step-by-step examples. Along the way, you’ll see how t-tests, chi-square, correlation, and regression all fit together under the same framework.

But this isn’t just another lecture-heavy course. You’ll code alongside me with guided exercises, code-along scripts, and real datasets, building a skill set you can apply immediately to assignments, theses, publications, or workplace projects. You’ll also explore more advanced techniques like bootstrapping, resampling, and regression modeling, reinforcing how these tools extend beyond the classroom and into research and professional practice.

By the end of this course, you’ll be able to:

• Write R code that is clean, efficient, and reproducible.
• Apply a broad set of inferential statistical methods to real data.
• Visualize results in clear and compelling ways.
• Develop the confidence to approach data like an experienced analyst.

Whether you’re new to statistics, transitioning into a data-focused role, or seeking a stronger foundation for research, this course offers a comprehensive, structured, and practical pathway to mastering statistics with R. Join today, and start building the tools to transform data into knowledge.

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