Mastering Data Analysis: R-Based Exploratory Techniques
Learn practical data visualization and analysis using R. Master statistical modeling, data cleaning, and graphical summaries through hands-on practice.
Learn practical data visualization and analysis using R. Master statistical modeling, data cleaning, and graphical summaries through hands-on practice.
This comprehensive course from the University of Leeds introduces students to exploratory data analysis and visualization using R software. Starting with fundamental concepts of data types and preparation, learners develop practical skills in creating graphical summaries and statistical models. The course emphasizes hands-on practice with RStudio, covering essential topics like data cleaning, outlier detection, and visualization techniques including box plots, histograms, and kernel density estimation. Students gain real-world experience through practical examples and peer review activities, making it ideal for those seeking to build expertise in data analysis.
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What you'll learn
Understand different data types and their importance in analysis
Master data preparation and cleaning techniques
Create effective graphical summaries using RStudio
Apply visualization methods for data exploration
Develop skills in handling missing data and outliers
Interpret statistical models through data visualization
Skills you'll gain
This course includes:
34 Minutes PreRecorded video
2 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course provides a comprehensive introduction to exploratory data analysis using R software. Students learn about different data types, data preparation methods, and visualization techniques. The curriculum covers essential topics including data cleaning, handling missing data, outlier detection, and creating effective graphical summaries using RStudio. Through hands-on exercises and practical applications, participants develop skills in creating and interpreting various visualization methods such as box plots, histograms, and kernel density estimation. The course emphasizes real-world applications and includes peer review activities to reinforce learning.
Getting to know your data for graphical summaries
Module 1 · 3 Hours to complete
Apply your knowledge: graphical summaries
Module 2 · 2 Hours to complete
Apply your knowledge: make your own graphical summaries and peer review
Module 3 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Senior Lecturer and Expert in Applied Statistics
Dr. Robert G. Aykroyd is a Senior Lecturer with a focus on applied statistics, particularly in Bayesian modeling approaches for image analysis and inverse problems. His research encompasses various fields, including archaeological geophysics, medical imaging (notably PET and SPECT), and electrical tomography. He employs advanced techniques such as Markov Chain Monte Carlo (MCMC) algorithms, finite element methods, and boundary element methods in his work. Additionally, Dr. Aykroyd explores wavelet and kernel density methods for applications in process monitoring, forensic age estimation, and climate reconstruction. He is keen to initiate new research on variational Bayesian methods for inverse problems and welcomes inquiries from potential postgraduate researchers interested in pursuing a PhD in his areas of expertise.
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