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R for Data Scientists
9 Courses
4 Labs
11 Hours
Skill IQ
The R for Data Scientists learning path provides a comprehensive toolkit for statistical modeling, machine learning, and data visualization. Learners will explore Bayesian inference, time series analysis, causal inference, and supervised/unsupervised learning using R. The path covers key packages like tidyverse, brms, forecast, caret, and tidymodels, enabling data scientists to analyze, model, and effectively communicate insights with robust, reproducible workflows.
Content in this path
R for Data Scientists
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What You'll Learn
- How to work with Bayesian statistics in R
- How to perform time series analysis and forecasting in R
- How to perform experimental design and causal inference in R
- How to work with advanced data visualization techniques in R
- How to perform statistical modeling and hypothesis testing in R
- How to train supervised machine learning models in R
- How to perform feature engineering and dimensionality reduction in R
- How to perform model validation and hyperparameter tuning in R
- How to work with unsupervised learning and clustering in R
Prerequisites
- Learners interested in this path should have an understanding of R syntax, functions, and data structures and familiarity with core R packages and functions from tidyverse, such as dplyr, ggplot2, and tidyr. Learners will also benefit from knowledge of descriptive statistics and familiarity with hypothesis testing and statistical significance. Learners should also be experienced in core data analysis tasks in R, such as data manipulation and visualization.
Related topics
- R
- Data Science
- Machine Learning
- Statistical Modeling
- Data Analysis
- Data Visualization
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