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Scientists seek to answer questions using rigorous methods and careful observations. These observations—collected from the likes of field notes, surveys, and experiments—form the backbone of a statistical investigation and are called data. Statistics is the study of how best to collect, analyze, and draw conclusions from data. It is helpful to put statistics in the context of a general process of investigation: 1) identify a question or problem; 2) collect relevant data on the topic; 3) analyze the data; and 4) form a conclusion. In this course, you'll focus on the first two steps of the process.
Language of dataFree
This chapter introduces terminology of datasets and data frames in R.Welcome to the course!50 xpLoading data into R100 xpTypes of variables50 xpIdentify variable types100 xpCategorical data in R: factors50 xpFiltering based on a factor100 xpComplete filtering based on a factor100 xpDiscretize a variable50 xpDiscretize a different variable100 xpCombining levels of a different factor100 xpVisualizing numerical data50 xpVisualizing numerical and categorical data100 xp
Study types and cautionary tales
In this chapter, you will learn about observational studies and experiments, scope of inference, and Simpson's paradox.Observational studies and experiments50 xpIdentify type of study: Reading speed and font50 xpIdentify type of study: Countries100 xpRandom sampling and random assignment50 xpRandom sampling or random assignment?50 xpIdentify the scope of inference of study50 xpSimpson's paradox50 xpNumber of males and females admitted100 xpProportion of males admitted overall100 xpProportion of males admitted for each department100 xpAdmission rates for males across departments50 xpRecap: Simpson's paradox50 xpIdentify type of study: Countries [new]50 xp
Sampling strategies and experimental design
This chapter defines various sampling strategies and their benefits/drawbacks as well as principles of experimental design.Sampling strategies50 xpSampling strategies, determine which50 xpSampling strategies, choose worst50 xpSampling in R50 xpSimple random sample in R100 xpStratified sample in R100 xpCompare SRS vs. stratified sample50 xpPrinciples of experimental design50 xpIdentifying components of a study50 xpExperimental design terminology50 xpConnect blocking and stratifying50 xp
Apply terminology, principles, and R code learned in the first three chapters of this course to a case study looking at how the physical appearance of instructors impacts their students' course evaluations.
PrerequisitesIntroduction to the Tidyverse
Associate Professor at Duke University & Data Scientist and Professional Educator at RStudio
Mine is the Director of Undergraduate Studies and an Associate Professor of the Practice in the Department of Statistical Science at Duke University as well as a Professional Educator at RStudio. Her work focuses on innovation in statistics pedagogy, with an emphasis on computation, reproducible research, open-source education, and student-centered learning. She is the author of three open-source introductory statistics textbooks as part of the OpenIntro project and teaches the popular Statistics with R MOOC on Coursera.
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I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
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Decision Science Analytics, USAA