Published Books on Data Science
Practical, intuition-first guides to statistical analysis and machine learning, written to help you build real, applicable data science skills in R and Python.
Data Science with R
A Practical Guide to Statistical Analysis
Builds a strong foundation in statistical thinking for data science. Whether you're new to programming or looking to develop a solid understanding of data analysis, this practical introduction guides you through the essential steps of working with data, emphasizing intuition over formulas alone.
- Learn how to use R and the tidyverse for data manipulation and analysis
- Understand key statistical concepts, including distributions, inference, and hypothesis testing
- Build and interpret linear regression models
- Create clear and effective data visualizations
- Develop a structured, practical approach to analyzing data
Data Science with R
A Practical Guide to Machine Learning
Builds a strong foundation in machine learning for data science. Whether you're new to programming or looking to develop a solid understanding of predictive modeling, this practical introduction guides you through the essential steps, focusing on why algorithms work rather than just how to apply them.
- Learn how to use R and the tidyverse for data preparation and analysis
- Understand core machine learning concepts, including supervised and unsupervised learning
- Train and evaluate models such as linear regression and tree-based methods
- Apply techniques such as cross-validation, model tuning, and performance metrics
- Build practical machine learning workflows with tidymodels
Data Science with Python
A Practical Guide to Statistical Analysis
Builds a strong foundation in statistical thinking for data science. Whether you're new to programming or looking to develop a solid understanding of data analysis, this practical introduction guides you through the essential steps of working with data, emphasizing intuition over formulas alone.
- Learn how to use Python with NumPy and Pandas for data manipulation and analysis
- Understand key statistical concepts, including distributions, inference, and hypothesis testing
- Build and interpret linear regression models
- Create clear and effective data visualizations with Seaborn
- Develop a structured, practical approach to analyzing data