Learn data analysis with R. Master basic data visualization, statistical tests, inference, and linear models.
Suggested by: Coursera (What is Coursera?)
No prior knowledge required
No unnecessary risks
In this specialization, you will learn to analyze and predict data in R and create reproducible data analysis reports. You will examine the conceptual understanding of the unified nature of statistical inference, perform statistical inference and Bayesian modeling methods to understand natural phenomena and make data-driven decisions. You will communicate statistical results correctly, effectively, and in context, without relying on statistical jargon. You will critique data-driven claims and evaluate data-driven decisions, and extract and present data using R packages for data analysis.
You will create a portfolio of data analysis projects from the internship, demonstrating mastery of statistical data analysis from initial analysis to inference and modeling, suitable for applications for data analysis or data scientist positions.
This course introduces the concepts of sampling and data exploration, as well as basic probability theory and Bayes’ rules. You will explore different types of sampling methods and discuss how these methods can affect the scope of inference. The course will cover a variety of exploratory data analysis techniques, including numerical summary statistics and basic data visualization. In addition, you will be guided in installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as a foundation for courses in inference and modeling in the specialization.
This course covers common statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is understandable to clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that reflects the uncertainty of the quantity of interest. You will receive guidance in installing and using R and RStudio (free statistical software), and you will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the basic concepts required to interpret and report results for categorical and numerical data.
This course introduces simple and multiple linear regression models. These models allow you to estimate the relationships between variables in a data set and a continuous response variable. Is there a relationship between a professor’s physical attractiveness and his students’ test scores? Can a child’s test score be predicted based on certain traits of his mother? In this course, you will learn the basic theory behind linear regression, and through data examples, you will learn how to fit, test, and exploit regression models to explore relationships between multiple variables, using R and the free RStudio software.
