Statistics with R - Advanced Level
Master advanced statistical analysis with R. Learn powerful techniques for data insights and modeling. Perfect for data science professionals and researchers.
Course Content
Statistics with R - Advanced Level is an advanced Udemy course taught by Bogdan Anastasiei. It runs 4h 14m, is taught in English, and includes a certificate of completion. Learners rate it 4.3 out of 5 from 221 reviews, with 28,245 students enrolled. It is a paid course, last updated December 2020.
Unlock Advanced Statistical Analysis with R
Ready to master sophisticated statistical techniques in R? This course provides a comprehensive, step-by-step visual guide to performing advanced analyses, eliminating the need for endless online searches.
Here’s a detailed look at what you’ll learn:
Advanced Mean Difference Evaluation: Build upon your existing knowledge of t-tests and ANOVA from an intermediate course. Dive into powerful techniques like:
- Analysis of Covariance (ANCOVA): Control for extraneous variables to gain deeper insights.
- Within-Subjects ANOVA: Analyze data collected from the same subjects over time.
- Mixed Analysis of Variance: Handle complex experimental designs with both within- and between-subjects factors.
Predictive Modeling with Logistic Regression: Explore logistic regression for categorical dependent variables. We cover:
- Binomial Logistic Regression: Analyze binary outcomes.
- Ordinal Logistic Regression: Model ordered categorical data.
- Multinomial Logistic Regression: Handle multiple unordered categorical outcomes.
Grouping & Dimensionality Reduction Techniques: Uncover hidden patterns and reduce data complexity with:
- Multidimensional Scaling (MDS): Visualize high-dimensional data in a lower-dimensional space.
- Principal Component Analysis (PCA): Reduce data dimensionality while retaining key information.
- Factor Analysis: Identify underlying factors that explain correlations among variables.
- Correspondence Analysis: Explore relationships between categorical variables. Includes both Simple and Multiple Correspondence Analysis.
- Cluster Analysis: Group similar data points together using:
- K-Means Clustering: Partition data into distinct clusters.
- Hierarchical Clustering: Build a hierarchy of clusters.
- Discriminant Analysis: Classify data into groups based on predictor variables. Includes both Simple and Multiple Discriminant Analysis.
By the end of this course, you'll possess expertise in a wide range of advanced statistical analyses using R. Gain the skills to thoroughly analyze your data and extract maximum insights. Enroll today and elevate your data analysis capabilities!
Want AI in your product? Hire the team behind TutorialSearch.io
LLM features, RAG, agents and automation wired into a real product — built by the devs who shipped this site.