“Modern Data Science with R” refers to the application of contemporary tools, techniques, and practices in the field of data science using the R programming language. R is a powerful and popular open-source language for statistical computing and data analysis, making it a preferred choice for many data scientists and analysts. Here’s what you can expect in the context of modern data science with R:

1. Data Collection and Import: Data science projects typically start with data collection. In modern data science with R, you’ll learn how to import data from various sources, such as databases, spreadsheets, web APIs, and more, using R packages like readr, readxl, httr, and DBI.

2. Data Cleaning and Preprocessing: Data is often messy and requires cleaning and preprocessing. You’ll use R’s data manipulation packages like dplyr and tidyr to clean, reshape, and transform data for analysis.

3. Exploratory Data Analysis (EDA): EDA is a critical phase where you explore data’s characteristics and uncover insights. Modern R data scientists use visualization libraries like ggplot2 and statistical tools like summary() and ggally for EDA.

4. Statistical Analysis: R has a rich ecosystem of statistical packages and functions, such as lm() for linear regression, glm() for generalized linear models, and various hypothesis testing functions. You’ll perform statistical analyses to test hypotheses and make inferences.

5. Machine Learning: Modern data science often involves machine learning. R provides machine learning libraries like caret, randomForest, xgboost, and glmnet for tasks such as classification, regression, clustering, and feature selection.

6. Data Visualization: Effective data visualization is crucial for conveying insights. You’ll create data visualizations using ggplot2, plotly, shiny, and other R packages to communicate findings.

7. Model Evaluation and Validation: In machine learning, you’ll learn how to evaluate and validate models using techniques like cross-validation and metrics such as accuracy, precision, recall, and ROC curves.

8. Big Data and High-Performance Computing: R can be used for big data analysis through packages like dplyr with database connectors, data.table, and parallel processing with the parallel package.

9. Reproducible Research: Modern data scientists emphasize reproducible research using R Markdown and version control systems like Git and GitHub to document and share analyses.

10. Deep Learning: While traditionally associated with Python and frameworks like TensorFlow and PyTorch, R also has deep learning libraries like keras and MXNet for neural network development.

11. Data Science Tools: You’ll use a range of data science tools and IDEs for R, such as RStudio, to streamline your data analysis workflow.

12. Real-World Applications: Modern data science with R is applied across various industries, including finance, healthcare, marketing, e-commerce, and more, to solve real-world problems and extract actionable insights.

13. Collaboration and Communication: Effective collaboration and communication are essential. You’ll learn how to share your analyses, findings, and visualizations with stakeholders using reports and dashboards.