I recently discovered the tidyplots package in R, and it’s impressive how effortlessly it enables you to create beautiful, publication-ready plots.

The example visualizations shown here were created by the package author, Jan Broder Engler, and are featured on the tidyplots website: https://jbengler.github.io/tidyplots/

Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

If you're a Stata user, you should switch to R now!

Thinking about switching to R? Check out my online course for absolute beginners in R programming.

Click this link for detailed information: https://statisticsglobe.com/online-course-r-introduction

Basic boxplots are often not the best way to visualize your data! They can hide important information, such as the distribution of individual data points or group-specific differences.

The attached visual showcases several ways to enhance boxplots.

All of these examples were created using ggplot2 and extensions in R.

Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

In my opinion, R should be your go-to programming language!

Ready to explore the world of R? I've created a comprehensive online course that introduces R for beginners.

More information: https://statisticsglobe.com/online-course-r-introduction

If you're looking to master Deep Learning, following a structured roadmap is key to navigating this advanced and ever-evolving field.

I came across this roadmap on the AIGENTS website, and what really stands out is its interactive format. Each element is clickable, offering AI-powered insights and resources that make it easier to dive deeper into each topic. Check out this link for more details: https://aigents.co/learn/roadmaps/deep-learning-roadmap

Decision trees are a powerful tool in data science for making decisions and predictions based on data. They work by splitting data into branches based on specific criteria, allowing for clear and interpretable decisions. When used correctly, decision trees can significantly enhance the accuracy and interpretability of models.

Learn more: https://statisticsglobe.com/online-course-statistical-methods-r

The Standard Error measures how much a sample statistic, like the mean, is expected to vary from the true population parameter. It helps us understand the precision of our estimates and how much confidence we can place in our results.

Learn more: https://statisticsglobe.com/online-course-statistical-methods-r

Understanding the Law of Large Numbers (LLN) is crucial for anyone working with statistics and probability. The LLN states that as the number of trials in an experiment increases, the average of the results becomes closer to the expected value.

Visualization: https://en.wikipedia.org/wiki/Law_of_large_numbers#/media/File:DiffusionMicroMacro.gif

Click this link for detailed information: https://statisticsglobe.com/law-of-large-numbers

I've recently discovered a really useful R package by Jan Broder Engler called tidyheatmaps.

Full guide: https://jbengler.github.io/tidyheatmaps/

Struggling with too much data? See how Principal Component Analysis (PCA) makes it easy to find the patterns and insights hidden in your data, all in a simple, powerful way!

Join my online course, where we'll dive deep into PCA theory and practical usage in R: https://statisticsglobe.com/online-course-pca-theory-application-r

Principal Component Analysis (PCA) before Linear Regression can greatly enhance your data analysis process.

Learn more in my online course: https://statisticsglobe.com/online-course-pca-theory-application-r

Hey, I've created a video introduction on how to handle missing values in R programming: https://m.youtube.com/watch?v=q8eR2suCyGk&feature=youtu.be

Hey, I've created a tutorial on how to calculate the variance of a NumPy array in the Python programming language: https://statisticsglobe.com/variance-numpy-array-python

Hey, I've created a video tutorial on how to extract the first & last N columns from a pandas DataFrame using the Python programming language: https://m.youtube.com/watch?v=t2lMKhfen9M&feature=youtu.be

Hey, I've created a tutorial on how to draw error bars with connected mean points using the ggplot2 package in the R programming language: https://statisticsglobe.com/draw-error-bars-connected-mean-points-r

Hey, I've created a video tutorial on how to add a column to a DataFrame using the pandas library in the Python programming language: https://m.youtube.com/watch?v=x5JHATL3nis&feature=youtu.be

Hey, I've created an introduction to the normal distribution in the R programming language: https://statisticsglobe.com/normal-distribution-in-r-dnorm-pnorm-qnorm-rnorm

Hey, I've published a tutorial on how to draw an Autoplot of a Principal Component Analysis (PCA) using the Python programming language. The tutorial was created in collaboration with Paula Villasante Soriano & Cansu Kebabci: https://statisticsglobe.com/autoplot-pca-python

Hey, I've published a tutorial on how to apply a merge sort algorithm to a list in the Python programming language. The tutorial was created in collaboration with Ömer Ekiz: https://statisticsglobe.com/merge-sort-list-python