About the Course
Dive into the world of data science with our beginner-friendly 4-week online workshop. In this comprehensive course, you'll unravel the basics of R, from data structures and manipulation to fundamental statistics. Best of all? You don’t need any prior programming experience!
Course Schedule
This fully asynchronous session allows you to learn at your own pace and convenience. All course materials and resources will be accessible online, enabling you to engage with the content whenever you like.
Don't miss out on unlocking the full potential of R programming!
Course Highlights:
🖥️ Interactive and hands-on sessions: Get practical experience with real-world applications.
🌍 Real-world applications: See how R applies to everyday data challenges.
🤝 Weekly personalized assistance: Get your questions answered every week.
🏆 Earn a completion certificate: Showcase your newly acquired skills.
What you will learn:
Introduction to R: Navigate RStudio and understand basic data types, vectors, and matrices.
Core Data Structures: Dive deep into lists, data frames, and factors.
Data Manipulation Mastery: Efficiently handle data using the 'dplyr' package.
Data Visualization: Harness the power of 'ggplot2' to craft compelling data visuals.
Statistics in R: From hypothesis testing to regression, explore R's robust statistical toolkit.
Who should attend?
Those at the start of their Machine Learning journey.
Budding Data Science enthusiasts.
Professionals keen on integrating R into their skillset.
Business Analysts pivoting to Machine Learning.
Anyone with a zest for Data Visualization and Analysis.
Your Instructor
Linda Amoafo
Linda is a Principal Data Scientist at Mogital Analytics and recently graduated with her Ph.D in Biostatistics from the University of Utah's Department of Population Health Science. Her research focuses on developing methods to analyze multicollinearity among multiple exposures and causal inference in multi-stage modeling. She has cross-disciplinary expertise in Statistics, Data Science, and Biostatistics.
Previously, Linda was a graduate research assistant at the CCTS Study Design and Biostatistics Center at the University of Utah School of Medicine. She collaborated on observational and experimental studies and employed various statistical modeling techniques there.
Linda holds an M.S. in Statistics from Northern Arizona University.
