Description
Master statistics using R: Coding, concepts, applications. This course allows participants to learn statistics in a modern, practical, and intuitive way. This course is designed for students, researchers, and professionals who want to go beyond memorizing formulas and gain a deep understanding of data analysis. Using the R programming language and the Tidyverse toolset, participants will gain not only coding skills but also the statistical understanding necessary to work as a data analyst. The course starts with the basics and covers organizing messy datasets, writing clean, reproducible code, and effective data visualization techniques. It then teaches the logic of statistical inference, including sampling variability, distributions, confidence intervals, and hypothesis testing, using step-by-step, easy-to-understand examples. It then introduces t-tests, chi-square, correlation, and regression in an integrated framework. This course is not just lecture-based, but through guided exercises, scripting, and working with real data, participants will learn skills that they can immediately apply to academic assignments, theses, papers, and professional projects. Advanced techniques such as bootstrapping, resampling, and regression modeling are also taught to provide the tools necessary for research and professional work.
What you will learn
- R programming and data organization:
- R programming for data analysis.
- Writing clean and reproducible R code.
- Data management skills with Tidyverse.
- Organizing data with dplyr and tidyr.
- Data visualization with ggplot2.
- Working with unordered, real-world data sets.
- Create clear and professional plots.
- Organizing projects for reproducibility.
- GitHub coding scripts are also included.
- Basic statistical concepts:
- Understanding sampling variability.
- Examining statistical distributions.
- Central limit theory in practice.
- Standard error and confidence intervals.
- The logic of hypothesis testing.
- Null hypotheses versus alternative hypotheses.
- P-values and significance test.
- Effective comparison of statistical tests.
- Creating analytical understanding in a practical way.
- Inferential statistics and modeling:
- Performing t-tests in R.
- ANOVA and group comparisons.
- Chi-square test for categorical data.
- Linear Regression Modeling in R.
- And…
This course is suitable for people who:
- Students and early stage researchers:
- Psychology students learning statistics.
- Biology and neuroscience disciplines that use R.
- Beginners in public health data analysis.
- Social science students in research methods.
- Graduate students writing their dissertations with data.
- Young researchers preparing articles.
- Students who need reproducible R workflows.
- Professionals who are switching careers to data-driven roles:
- Healthcare professionals learning R statistics.
- Educational researchers who analyze student data.
- And…
Course details Master statistics using R: Coding concepts applications
- Publisher: Udemy
- Instructor: Adam Dede , Mike X Cohen
- Training level: Beginner
- Training duration: 28 hours and 23 minutes
- Number of lessons: 204
Course topics
Prerequisites for the Master statistics using R course: Coding concepts applications
- No knowledge or skills are required for this course
- Coding experience in any language is helpful but not necessary
- Familiarity with basic statistics terms like descriptive, inferential, mean, standard deviation, but not necessary
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: English
Quality: 720p
Changes:
Version 2025/11 compared to 2025/8 has increased by 1 lesson and 6 minutes in duration. English subtitles were also added to the course.
Download link
Downloadly
Rapidgator
File(s) password: www.downloadly.ir
File size
21.4 GB
