Data-Centric Statistical Inference Using R and Tidyverse

Dr. Chester Ismay

Experienced data science leader and mentor, co-author of 'Statistical Inference via Data Science.' Expert in R, Python. Developed R packages, holding a PhD in Statistics. Skilled in machine learning, consulting, and software development.

I am an experienced leader and mentor in data science, dedicated to guiding others into the field and enhancing their abilities, particularly in R and Python. As the co-author of 'Statistical Inference via Data Science: A ModernDive into R and the Tidyverse,' available at moderndive.com and through CRC Press, I share my knowledge widely. Leading teams in education and data science, I focus on creating robust, replicable analyses in Python and R, always aiming to refine methodologies based on insights from educational data with a focus on collaboration and compassion. I have also developed a range of R packages, such as 'infer' for statistical inference, 'fivethirtyeight' for datasets used in FiveThirtyEight stories, 'thesisdown' for reproducible theses, and 'moderndive' for tidyverse-friendly linear regression. These tools reflect my commitment to making data science accessible and applicable in various contexts. My academic background includes a PhD in Statistics from Arizona State University, an MS in Statistics from Northern Arizona University, and a BS in Computational & Applied Mathematics from South Dakota School of Mines & Technology. My educational journey has been pivotal in shaping my approach to data science and education. Furthermore, I have extensive experience in using a variety of tools such as Git/GitHub, Jupyter notebooks, Pandas, scikit-learn, NumPy, command line, shiny, R Markdown, tidyverse, and SQL. My skills encompass applied machine learning, statistical modeling, classification methods, regression techniques, automated reporting, technical writing and communication, project management, and unit testing. My career spans roles as a leader, educator, consultant, and data scientist, marked by a profound dedication to teaching, curriculum development, and practical application of data science principles.