Master statistics using R Coding, concepts, applications

Learn R, data analysis, visualization, inference, and regression through real-world statistical practice.
What you'll learn
R Programming & Data Wrangling
R programming for data analysis
Writing clean reproducible R code
Tidyverse data manipulation skills
Data wrangling with dplyr and tidyr
Visualizing data with ggplot2
Handling messy, real-world datasets
Creating clear, professional plots
Organizing projects for reproducibility
GitHub code-along scripts included
Core Statistical Concepts
Understanding sampling variability
Exploring statistical distributions
Central limit theorem in practice
Standard error and confidence intervals
Logic of hypothesis testing
Null vs alternative hypotheses
P-values and significance testing
Comparing statistical tests effectively
Building analytic intuition hands-on
Inferential Statistics & Modeling
Conducting t-tests in R
ANOVA and group comparisons
Chi-square test for categorical data
Linear regression modeling in R
Understanding assumptions of tests
Interpreting effect sizes in R
Practical Data Analysis
Realistic messy data scenarios
Iterative analysis and refinement
Making decisions with uncertainty
Interpreting results like a researcher
Guided exercises for practice
Step-by-step code demonstrations
Building confidence as a data analyst
Applying statistics to real projects
Requirements
No knowledge or skills are required for this course
Coding experience in any language is helpful but not necessary
Familiarity with basic stats terms like descriptive, inferential, mean, standard deviation, but not necessary
Description
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.
Who this course is for
Students & Early-Career Researchers
Psychology students learning statistics
Biology and neuroscience majors using R
Public health data analysis beginners
Social science undergraduates in research methods
Graduate students writing theses with data
Early-career researchers preparing publications
Students needing reproducible R workflows
Professionals Transitioning to Data Roles
Healthcare professionals learning R statistics
Education researchers analyzing student data
Nonprofit staff working with survey data
Policy analysts learning statistical tools
Professionals moving into data science careers
People with stats background new to R
Learners seeking modern tidyverse methods
Self-Taught & Lifelong Learners
Beginners wanting a guided R path
Self-taught coders needing structured learning
Lifelong learners exploring data science
Hobbyists wanting real-world data analysis
Learners preferring clear step-by-step examples
People seeking intuition, not black-box methods
Independent learners practicing hands-on R
machine learning beginners
Homepage

Код:

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Master statistics using R Coding, concepts, applications

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