Roger D. Peng is an Associate Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a Co-Editor of the Simply Statistics blog. He received his Ph.D. in Statistics from the University of California, Los Angeles and is a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for environmental data. He created the course Statistical Programming at Johns Hopkins as a way to introduce students to the computational tools for data analysis. Dr. Peng is also a national leader in the area of methods and standards for reproducible research and is the Reproducible Research editor for the journal Biostatistics. His research is highly interdisciplinary and his work has been published in major substantive and statistical journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. Dr. Peng is the author of more than a dozen software packages implementing statistical methods for environmental studies, methods for reproducible research, and data distribution tools. He has also given workshops, tutorials, and short courses in statistical computing and data analysis.
More info here.
Coursera
Embark on a journey into the world of data science with 'The Data Scientist's Toolbox'. This introductory course equips you with fundamental tools and ideas that are pivotal for any aspiring data analyst or scientist. Dive into understanding how data is transformed into knowledge, and get hands-on experience with version control, markdown, git, GitHub, R, and RStudio.
Coursera
Discover the fundamentals of R Programming with this beginner-friendly course designed by experts at Coursera. Whether you're new to coding or looking to enhance your data analysis skills, this course will guide you through installing and configuring essential software, while introducing key programming concepts tailored for statistical applications. Learn how to harness the full potential of R for data manipulation, visualization, and analysis.
Coursera
Embark on a journey to master the fundamentals of data acquisition and cleaning with 'Getting and Cleaning Data'. This course is designed for those new to data science or professionals looking to refine their skills in collecting and preparing data for analysis. From web scraping to database management, you'll learn how to obtain data from multiple sources and transform it into a clean, usable format.
Coursera
Discover the secrets to making informed decisions based on data with our Statistical Inference course. Designed for those who want to understand how to draw reliable conclusions from their findings, this course covers a wide range of techniques and theories essential for effective analysis. From statistical modeling to data-oriented strategies, you'll gain valuable skills in interpreting and utilizing your research results.
Coursera
Explore the essential concepts and practical tools of Reproducible Research on Coursera. Master the art of publishing data analyses, ensuring others can verify findings and build upon them. Ideal for researchers, analysts, and anyone working with complex datasets.
Coursera
Discover the key methods of Exploratory Data Analysis (EDA) in this insightful online course offered by Coursera. Designed for beginners and professionals alike, this course will equip you with the skills to summarize data effectively, inform hypothesis development, and lay a strong foundation for advanced statistical modeling. Start your journey into the world of data analysis today!
Coursera
Dive into Regression Models, a crucial course for aspiring data scientists on Coursera. Explore linear assumptions, learn how to relate outcomes to predictors effectively using regression analysis, and understand the principles of least squares and statistical inference. This course is perfect for those looking to deepen their understanding of essential statistical tools.
Coursera
Discover how to transform raw data into valuable insights with Coursera's 'Developing Data Products' course. Designed for those who want to create automated analytical tools or enhance data-driven models, this course equips you with essential skills in utilizing Shiny, R packages, and interactive graphics to build impactful data products.
Coursera
Discover the essential skills needed for data science with our Practical Machine Learning course. Designed for beginners, this course will guide you through the basics of creating and implementing prediction models, focusing on real-world application and avoiding common pitfalls like overfitting and error rates.