The Comprehensive Statistics and Data Science with R Course
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The Comprehensive Statistics and Data Science with R Course

Learn how to use R for data science tasks, all about R data structures, functions and visualizations, and statistics.
4.2 (93 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,620 students enrolled
Last updated 8/2016
English
Current price: $12 Original price: $60 Discount: 80% off
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Includes:
  • 19.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?
  • Students will understand what R is, and how to input and output data files into their R sessions.
  • Students will know how to manipulate numbers and vectors, and will understand objects and classes.
  • Students will understand how to create data structures in R: vectors; arrays and matrices; lists and data frames.
  • Students will know how to use R as a statistical environment following many examples.
  • Students will understand how to create, estimate and interpret ANOVA, regression, GLM and GAM statistical models with many examples of each.
  • Students will learn how to create statistical and other visualizations using both the base and ggplot graphics capabilities in R.
View Curriculum
Requirements
  • Students must install R and RStudio (free software) but ample instructions are provided.
Description

This course, The Comprehensive Statistics and Data Science with R Course, is mostly based on the authoritative documentation in the online "An Introduction to R" manual produced with each new R release by the Comprehensive R Archive Network (CRAN) development core team. These are the people who actually write, test, produce and release the R code to the general public by way of the CRAN mirrors. It is a rich and detailed 10-session course which covers much of the content in the contemporary 105-page CRAN manual. The ten sessions follow the outline in the An Introduction to R online manual and specifically instruct with respect to the following user topics:

1. Introduction to R; Inputting data into R

2. Simple manipulation of numbers and vectors

3. Objects, their modes and attributes

4. Arrays and matrices

5. Lists and data frames

6. Writing user-defined functions

7. Working with R as a statistical environment

8. Statistical models and formulae; ANOVA and regression

9. GLMs and GAMs

10. Creating statistical and other visualizations with R

It is a comprehensive and decidedly "hands-on" course. You are taught how to actually use R and R script to create everything that you see on-screen in the course videos. Everything is included with the course materials: all software; slides; R scripts; data sets; exercises and solutions; in fact, everything that you see utilized in any of the 200+ course videos are included with the downloadable course materials.

The course is structured for both the novice R user, as well as for the more experienced R user who seeks a refresher course in the benefits, tools and capabilities that exist in R as a software suite appropriate for statistical analysis and manipulation. The first half of the course is suited for novice R users and guides one through "hands-on" practice to master the input and output of data, as well as all of the major and important objects and data structures that are used within the R environment. The second half of the course is a detailed "hands-on" transcript for using R for statistical analysis including detailed data-driven examples of ANOVA, regression, and generalized linear and additive models. Finally, the course concludes with a multitude of "hands-on" instructional videos on how to create elegant and elaborate statistical (and other) graphics visualizations using both the base and gglot visualization packages in R.

The course is very useful for any quantitative analysis professional who wishes to "come up to speed" on the use of R quickly. It would also be useful for any graduate student or college or university faculty member who also seeks to master these data analysis skills using the popular R package.

Who is the target audience?
  • This course will benefit anyone wishing to learn R and especially those who seek an in-depth "hands-on" tutorial on performing statistical analyses with R.
  • The course is useful for graduate students, college and university faculty, and working quantitative analysis professionals.
Compare to Other R Courses
Curriculum For This Course
219 Lectures
19:40:46
+
Introduction to R and Inputting Data into R
12 Lectures 01:18:47




Agenda and What is R ? (slides, Part 1)
08:35

What is R ? (slides, part 2)
06:59

What is R ? (slides, part 3)
06:54

What is R ? (slides, part 4)
06:13

What is R ? (slides, part 5)
06:45



Reading in Data (part 3)
09:03
+
Manipulating Numbers and Vectors
17 Lectures 01:21:38
Introduction to Section 2
05:52



Vectors and Assignment (part 3)
05:37

Vector Arithmetic (part 1)
05:26

Vector Arithmetic (part 2)
04:41

Vector Arithmetic (part 3)
05:49

Vector Arithmetic (part 4)
04:02

Vector Arithmetic (part 5)
05:58

Generating Regular Sequences
04:35

Logical Vectors
04:24

More Missing Values; Character Vectors
04:28

Index Vectors (part 1)
05:51

Index Vectors (part 2)
06:57

Index Vectors (part 3)
04:40

Index Vectors (part 4)
02:05

Session 2 Exercises
01:13
+
Objects and Classes: Their Modes and Attributes
17 Lectures 01:24:14
Solutions to Session 2 Exercises (part 1)
05:32

Solutions to Session 2 Exercises (part 2)
06:07

Solutions to Session 2 Exercises (part 3)
04:23



Strings
02:24

Factors
05:53

Logical and Missing
05:07

Vectors
03:40

Vectorization and Recycling
05:05

Basic Data Structures in R (slides, part 1)
04:54

Basic Data Structures (slides, part 2)
05:28

Basic Data Structures (slides, part 3)
05:54

Objects: Script Examples (part 1)
05:22

Objects: Script Examples (part 2)
06:02

Objects: Script Examples (part 3)
05:53

Objects: Script Examples (part 4)
05:17
+
Arrays and Matrices
22 Lectures 01:55:23
Session 4 Exercises
00:54

More on Factors
05:39




Function tapply() and Ragged Arrays
07:20

Arrays
05:43

Arrays and Matrices (part 1)
05:12

Arrays and Matrices (part 2)
05:06

Warpbreaks Data (part 1)
05:28

Warpbreaks Data (part 2)
05:40

More about Matrices (part 1)
05:13

More about Matrices (part 2)
05:31

More about Matrices (part 3)
06:22

More about Matrices (part 4)
05:10

More about Matrices (part 5)
04:35

More about Matrices (part 6)
05:33

Creating Matrices (part 1)
05:41

Creating Matrices (part 2)
04:28

Row Names and Column Names
05:35

More on Array Function
05:13

Outer Product of Two Arrays
05:48
+
List and Data Frame Structures
19 Lectures 01:34:36
Introduction to Lists
05:55



Accessing List Components (part 1)
05:51

Accessing List Components (part 2)
05:41

More List Dissection (part 1)
05:11

More List Dissection (part 2)
04:06

More About Lists
03:35

What are Data Frames
05:17

Characteristics of Data Frames
04:57

A Data Frame is a List
04:48

Data Frames are Lists
03:25



Manipulating Data Frames (part 3)
05:10

Manipulating Data Frames (part 4)
06:13

Manipulating Data Frames (part 5)
04:18

Manipulating Data Frames (part 6)
04:13

Manipulating Data Frames (part 7)
04:27
+
User-Defined Functions
27 Lectures 02:13:31
Exercise Solutions (part 1)
05:10

Exercise Solutions (part 2)
05:20

Exercise Solutions (part 3)
05:57

Exercise Solutions (part 4)
04:58

Exercise Solutions (part 5)
05:20

Exercise Solutions (part 6)
06:00

Exercise Solutions (part 7)
05:09

Exercise Solutions (part 8)
05:38

Introduction to Writing Functions in R
02:27

Writing Functions (slides, part 1)
04:09

Writing Functions (slides, part 2)
04:17

Two Sample t-test (part 1)
04:54

Two Sample t-test (part 2)
05:03

Finish t-test Example; Named Arguments and Defaults
04:59



Many Means and More
04:48

More Functions Examples (part 1)
04:48

More Functions Examples (part 2)
05:18

Superassigment Examples (part 3)
04:57

Superassignment Examples (part 4)
04:47

Optional Arguments Example (part 5)
06:08



More Functions Examples (part 8)
04:31

Still More Examples (part 9)
04:14

Exercises for User Defined Functions Section
04:11
+
Working with R as a Statistical Environment
28 Lectures 02:21:14
User-Defined Functions Exercise 1a. Solution (part 1)
02:58

User-Defined Functions Exercise 1a. Solution (part 2)
05:16

User-Defined Functions Exercise 1b. Solution
06:24

User-Defined Functions Exercise 2 Solution (part 1)
04:22

User-Defined Functions Exercise 2 Solution (part 2)
04:48

Introduction to R as a Statistical Environment
06:58

Basic Operations (part 1)
03:51

Basic Operations (part 2)
04:22

Basic Operations (part 3)
04:45



Prussian Horsekicks and Functions (part 1)
05:28

Prussian Horsekicks and Functions (part 2)
05:19

Functions; Vectors and Matrices (part 1)
05:57

Functions; Vectors and Matrices (part 2)
05:37

Functions; Vectors and Matrices (part 3)
05:48

Data Frames and Histograms (part 1)
05:19

Data Frames and Histograms (part 2)
05:57

Attaching and Working with Data Frames (part 1)
04:33

Attaching and Working with Data Frames (part 2)
04:16

Attaching and Working with Data Frames (part 3)
05:12

Entering Data Manually (part 1)
05:43

Entering Data Manually (part 2)
05:15

Entering Data Manually (part 3)
04:23

Exercises for Working with R as a Statistical Environment
05:26

Exercises Solutions (part 1)
03:48

Exercises Solutions (part 2)
05:00

Exercises Solutions (part 3)
04:41
+
Statistical Models and Formulae, ANOVA and Regression
27 Lectures 02:07:00
Statistical Modeling Operators in R (part 1)
05:33

Statistical Modeling Operators in R (part 2)
05:51

Statistical Modeling Operators in R (part 3)
05:04

Analysis of Variance (ANOVA) (slides, part 1)
04:58

ANOVA (slides, part 2)
05:22

ANOVA (slides, part 3)
04:06

ANOVA Scripts (part 1)
05:03

ANOVA Scripts (part 2)
05:13

ANOVA Scripts (part 3)
05:13

ANOVA Scripts (part 4)
05:21

ANOVA Scripts (part 5)
04:01

ANOVA Scripts (part 6)
03:49



What is Linear Modeling ? (slides, part 3)
02:57

What is Linear Modeling ? (slides, part 4)
04:14

Regression Domains (slides, part 1)
04:23

Regression Domains (slides, part 2)
04:45



Regression Scripts (part 3)
04:56

Regression Scripts (part 4)
06:08

Regression Scripts (part 5)
04:27

Regression Scripts (part 6)
05:00

Regression Scripts (part 7)
03:21

Regression Scripts (part 8)
04:07

Linear Modeling Exercise
01:48
+
Generalized Linear Models (GLMs) and Generalized Additive Models (GAMS)
19 Lectures 01:33:52


What are GLMs ? (slides, part 3)
05:40

What are GLMs ? (slides, part 4)
05:24

What are GLMs ? (slides, part 5)
04:48

What are GLMs ? (slides, part 6)
04:21

GLM: ESR Study (part 1)
06:01

GLM: ESR Study (part 2)
04:47

GLM: ESR Study (part 3)
04:49



GLM: Womens' Role in Society (part 3)
04:15

GLM: Colonic Polyps (part 1)
04:26

GLM: Colonic Polyps (part 2)
05:51

GAMs and Smoothers (slides, part 1)
04:41

GAMs and Smoothers (slides, part 2)
03:44

Smoothers: Olympic Data (part 1)
05:11

Smoothers and GAMs (part 2)
07:12

Smoothers and GAMs (part 3)
05:07
+
Creating Visualizations with R
31 Lectures 03:50:31



Still More Graphics Features
07:16

More on Plotting Characters
07:05

More on Plotting and Features and an Exercise
06:46

Exercise Solution and More on Base Graphics
09:43

More Base Features Compared to ggplot
08:17

Adding Text to Plots (part 1)
08:45

Adding Text to Plots (part 2)
09:31

Adding Shapes to Plots Interactively (part 1)
07:22

Adding Shapes to Plots Interactively (part 2)
07:39

Adding Shapes to Plots Interactively (part 3)
08:05



Adding Nonlinear Fits to Plots (part 3)
05:50

Adding Nonlinear Fits to Plots (part 4)
05:26

Adding Nonlinear Fits to Plots (part 5)
07:21

Boxplots (part 1)
05:55

Boxplots (part 2)
07:25

Boxplots (part 3)
07:23

Boxplots (part 4)
10:21

Histograms
06:54

Time Series and Piechart
06:07

Stripchart and Pairs Plot
06:58

Exercise Solution and Shingles
07:02

Shingles, Coplot, and Interaction Plots
06:35

Box and Whiskers Plot and Design Plot
08:14

Interaction and XYPlots
06:25

Effects Sizes
05:34

Bubble and Sunflower Plots
07:53
About the Instructor
Geoffrey Hubona, Ph.D.
4.0 Average rating
1,497 Reviews
12,707 Students
28 Courses
Associate Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. Currently, he is a visiting associate professor of MIS at Texas A&M International University. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.