Data Visualization With Ggplot & Machine Learning In R
Published 3/2026
Created by Md Ahshanul Haque
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 150 Lectures ( 9h 52m ) | Size: 4.53 GB
Data Visualization with ggplot, Biostatistical Analysis, and Machine Learning for Data Science in R
What you’ll learn
✓ Create professional data visualizations in R using ggplot for research and publication
✓ Clean, manage, and prepare real-world datasets for statistical and machine learning analysis
✓ Perform regression analysis and correctly interpret model outputs for research
✓ Apply machine learning models in R including decision trees and random forests
✓ Evaluate predictive models using train-test split, confusion matrix, and ROC curve
✓ Create professional data visualizations and publication-ready tables in R for research
Requirements
● Basic familiarity with R and running simple code
● R and RStudio installed on your computer
● No prior machine learning experience required
Description
Data Visualization and Machine Learning in R for Research and Data Analysis
This course offers a practical and structured approach to data visualization, biostatistical analysis, statistical modeling, and machine learning in R. It is designed for students, researchers, and professionals who want to apply data analysis techniques to real-world datasets.
Using a hands-on, learning-by-doing approach, you will work with real data from the beginning. You will learn how to import data in R and RStudio, explore data structures, and create professional visualizations using ggplot. By the end, you will be able to produce clear, publication-ready figures suitable for reports, theses, and research work.
The course then moves into data management and descriptive analysis. You will learn how to clean datasets, handle missing values, create new variables, and perform exploratory data analysis. These steps build a strong foundation for statistical modeling.
You will perform regression analysis in R, including linear and logistic regression, and learn how to interpret model results correctly. Special emphasis is placed on creating clear statistical summaries and publication-ready tables.
After building a solid statistical base, the course introduces machine learning in R. You will learn key concepts such as train-test split, model evaluation, and overfitting. Practical models such as decision trees and random forests are implemented and compared with traditional regression approaches.
You will also evaluate model performance using confusion matrices and ROC curves, helping you understand when machine learning methods are appropriate.
What you will learn
In this course, you will learn how to create professional data visualizations using ggplot in R and how to clean and manage datasets for analysis. You will perform descriptive and statistical analysis, build and interpret regression models, and generate publication-ready tables. You will also apply machine learning techniques in R and evaluate models using confusion matrix, ROC, and AUC. In addition, you will learn how to compare statistical and predictive modeling approaches in practical research settings.
Who this course is for
■ MPH, MSc, and PhD students working with research data
■ Public health and health research professionals
■ Researchers who want to move from regression to machine learning in R
■ Analysts interested in applying machine learning to real-world datasets
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