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Data science skills are in much demand today, but it is not just the mathematicians, statisticians, and the computer scientists who can benefit from acquiring them. Data science skills are for everyone!
In this course, I help you to begin using R, one of the most important tools in data science, and the excellent graphics package for R, ggplot2. Along the way, I also show you the basics of simple linear regression.
There are no prerequisites. We begin with installation of R and RStudio, and I introduce R and ggplot skills as they are needed as we progress toward an understanding of linear regression.
Students should be able to complete the course within two weeks, working at an easy pace.
Linear regression is a machine learning technique. I hope to create more courses like this one in the future, teaching machine learning, R, ggplot, dplyr, and programming, all at the same time.
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Section 1: Getting Started  

Lecture 1 
Introduction

02:19  
Lecture 2  02:57  
After this lecture, you should be able to install both R and RStudio on your own computer and start a session in RStudio. 

Lecture 3  02:06  
After this lecture, you will know the functions of the four panels in RStudio. 

Lecture 4  06:32  
After this lecture, you will be able to enter vectors into R, access individual coordinates of vectors, and slice vectors. 

Lecture 5  03:35  
After this lecture, you will be able to create a data frame out of several vectors and individually access column vectors of data frames. 

Section 2: Working with ggplot  
Lecture 6  02:52  
After this lecture, students will be able to install ggplot2 

Lecture 7  08:31  
After this lecture, you will be able to plot a point using ggplot. 

Lecture 8  09:00  
After this lecture, you will be able to set limits and number of ticks on your axes. 

Lecture 9  04:22  
After this lecture, you will have more colors and shapes to choose from when defining your points. 

Lecture 10  05:11  
After this lecture, you will be able to graph lines with ggplot. 

Lecture 11  06:10  
After this video, you will be able to graph lines with ggplot using the equation of a line. 

Section 3: Sampling from populations  
Lecture 12  05:20  
After this lecture, you will be able to generate samples from normal populations in R. 

Lecture 13  06:11  
After this lecture, you will be able to plot a vertical stack of points, with the ycoordinates drawn from a normal population, along with a point which represents the mean of that population. 

Lecture 14  08:47  
After this lecture, you will be able to plot several vertical stacks of points, with the ycoordinates drawn from normal populations, along with points which represent the means of these populations. 

Lecture 15  08:59  
After this lecture, you will be able to plot vertical samples with means lying on a line. 

Lecture 16  03:47  
After this lecture, you will be able to employ the sapply function in R. 

Lecture 17  10:04  
After this lecture, you will be able to generate a cloud of random points based on a true regression line. 

Section 4: Simple Linear Regression in R  
Lecture 18  05:42  
At the end of this lecture, you will be able to load and plot the father/son height data from the R package UsingR. 

Lecture 19  02:19  
At the end of this lecture, you will be able to find the equation of a line given two points on the line. 

Lecture 20  12:09  
After this lecture, you will understand residuals, and you will be able to group with ggplot. 

Lecture 21  04:57  
After this lecture, given any line and set of points, you will be able to find the sum of squared residuals. 

Lecture 22  05:07  
After this lecture, you will be able to find the equation of the least squares line in R. 

Lecture 23  03:02  
After this lecture, you will be able to use the least squares line for prediction. 

Lecture 24  02:35  
After this lecture, you will be able to read in data you have stored in an Excel file. 

Lecture 25  01:08  
This is a review of what we've covered in the course, and I also mention some important concepts that we have not covered, some that I hope to address in future courses. 
Dr. Charles Redmond is a professor in the Tom Ridge School of Intelligence Studies and Information Science at Mercyhurst University. He has been a member of the Department of Mathematics and Computer Systems at Mercyhurst for 21 years and has recently completed a term as chair of the department. Dr. Redmond received his PhD in mathematics from Lehigh University in 1993 and has published in the Annals of Applied Probability, the Journal of Stochastic Processes and Their Applications, Mathematics Magazine, the College Mathematics Journal, and Mathematics Teacher. In his spare time he enjoys making music and computer generated art, reading, and owning a Clumber Spaniel.