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Master statistics using R Coding, concepts, applications - OneDDL - 08-20-2025 Free Download Master statistics using R Coding, concepts, applications Published 8/2025 Created by Adam Dede,Mike X Cohen MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 203 Lectures ( 28h 17m ) | Size: 20.7 GB 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 [To see links please register or login] Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [To see links please register or login] No Password - Links are Interchangeable