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The Comprehensive Statistics and Data Science with R Course

By: Geoffrey Hubona

  • 5
  • (8)
  • 19:42:10
  • 219
  • 25
  • Language: English

Course Summary

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

Read More

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.

Pre-Requisites

  • Students must install R and RStudio (free software) but ample instructions are provided.

Curriculum

  • Introduction
    01:55
  • Another Word about the Course and the Materials
    04:50
  • Introduction to Course Materials
    08:43
  • Session 1 Exercises
    06:45
  • Agenda and What is R ? (slides, Part 1)
    08:35
  • What is R ? (slides, part 2)
    07:00
  • What is R ? (slides, part 3)
    06:54
  • What is R ? (slides, part 4)
    06:14
  • What is R ? (slides, part 5)
    06:45
  • Reading in Data (part 1)
    05:40
  • Reading in Data (part 2)
    06:27
  • Reading in Data (part 3)
    09:03
  • Introduction to Section 2
    05:53
  • Vectors and Assignment (part 1)
    05:37
  • Vectors and Assignment (part 2)
    04:24
  • 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
  • Solutions to Session 2 Exercises (part 1)
    05:33
  • Solutions to Session 2 Exercises (part 2)
    06:07
  • Solutions to Session 2 Exercises (part 3)
    04:23
  • Objects and Classes
    04:45
  • Numeric Types
    02:28
  • Strings
    02:24
  • Factors
    05:54
  • Logical and Missing
    05:08
  • Vectors
    03:41
  • Vectorization and Recycling
    05:06
  • Basic Data Structures in R (slides, part 1)
    04:55
  • Basic Data Structures (slides, part 2)
    05:28
  • Basic Data Structures (slides, part 3)
    05:55
  • Objects: Script Examples (part 1)
    05:22
  • Objects: Script Examples (part 2)
    06:02
  • Objects: Script Examples (part 3)
    05:54
  • Objects: Script Examples (part 4)
    05:17
  • Session 4 Exercises
    00:55
  • More on Factors
    05:39
  • More on Factors and Strings (part 1)
    05:13
  • Factors and Strings (part 2)
    05:03
  • Factors and Strings (part 3)
    04:58
  • Function tapply() and Ragged Arrays
    07:20
  • Arrays
    05:44
  • Arrays and Matrices (part 1)
    05:13
  • Arrays and Matrices (part 2)
    05:06
  • Warpbreaks Data (part 1)
    05:28
  • Warpbreaks Data (part 2)
    05:40
  • More about Matrices (part 1)
    05:14
  • 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)
    04:36
  • More about Matrices (part 6)
    05:33
  • Creating Matrices (part 1)
    05:42
  • Creating Matrices (part 2)
    04:29
  • Row Names and Column Names
    05:36
  • More on Array Function
    05:14
  • Outer Product of Two Arrays
    05:48
  • Introduction to Lists
    05:56
  • List Features and List Slicing (part 1)
    06:05
  • List Features and List Slicing (part 2)
    05:33
  • Accessing List Components (part 1)
    05:51
  • Accessing List Components (part 2)
    05:42
  • More List Dissection (part 1)
    05:12
  • More List Dissection (part 2)
    04:07
  • More About Lists
    03:35
  • What are Data Frames
    05:17
  • Characteristics of Data Frames
    04:58
  • A Data Frame is a List
    04:48
  • Data Frames are Lists
    03:26
  • Manipulating Data Frames (part 1)
    04:52
  • Manipulating Data Frames (part 2)
    05:00
  • Manipulating Data Frames (part 3)
    05:10
  • Manipulating Data Frames (part 4)
    06:13
  • Manipulating Data Frames (part 5)
    04:19
  • Manipulating Data Frames (part 6)
    04:13
  • Manipulating Data Frames (part 7)
    04:27
  • 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
  • Function Examples (part 1)
    04:34
  • "Many Means" Function Example
    05:57
  • 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
  • parmax() and parmin() Functions Examples (part 6)
    05:12
  • parboth() Function Example (part 7)
    04:45
  • More Functions Examples (part 8)
    04:31
  • Still More Examples (part 9)
    04:14
  • Exercises for User Defined Functions Section
    04:11
  • 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
    06:25
  • User-Defined Functions Exercise 2 Solution (part 1)
    04:23
  • User-Defined Functions Exercise 2 Solution (part 2)
    04:48
  • Introduction to R as a Statistical Environment
    06:58
  • Basic Operations (part 1)
    03:52
  • Basic Operations (part 2)
    04:22
  • Basic Operations (part 3)
    04:45
  • Presidential Height and Prussian Horsekicks (part 1)
    05:04
  • Presidential Height and Prussian Horsekicks (part 2)
    04:44
  • Prussian Horsekicks and Functions (part 1)
    05:29
  • Prussian Horsekicks and Functions (part 2)
    05:19
  • Functions; Vectors and Matrices (part 1)
    05:58
  • Functions; Vectors and Matrices (part 2)
    05:37
  • Functions; Vectors and Matrices (part 3)
    05:48
  • Data Frames and Histograms (part 1)
    05:20
  • Data Frames and Histograms (part 2)
    05:57
  • Attaching and Working with Data Frames (part 1)
    04:34
  • Attaching and Working with Data Frames (part 2)
    04:17
  • 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)
    04:24
  • Exercises for Working with R as a Statistical Environment
    05:26
  • Exercises Solutions (part 1)
    03:49
  • Exercises Solutions (part 2)
    05:00
  • Exercises Solutions (part 3)
    04:42
  • 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)
    04:07
  • ANOVA Scripts (part 1)
    05:04
  • ANOVA Scripts (part 2)
    05:13
  • ANOVA Scripts (part 3)
    05:13
  • ANOVA Scripts (part 4)
    05:21
  • ANOVA Scripts (part 5)
    04:02
  • ANOVA Scripts (part 6)
    03:50
  • What is Linear Modeling ? (slides, part 1)
    05:12
  • What is Linear Modeling ? (slides, part 2)
    06:10
  • What is Linear Modeling ? (slides, part 3)
    02:57
  • What is Linear Modeling ? (slides, part 4)
    04:14
  • Regression Domains (slides, part 1)
    04:24
  • Regression Domains (slides, part 2)
    04:46
  • Regression Script (part 1)
    04:50
  • Regression Scripts (part 2)
    05:11
  • Regression Scripts (part 3)
    04:56
  • Regression Scripts (part 4)
    06:09
  • Regression Scripts (part 5)
    04:28
  • Regression Scripts (part 6)
    05:00
  • Regression Scripts (part 7)
    03:21
  • Regression Scripts (part 8)
    04:08
  • Linear Modeling Exercise
    01:49
  • What are Generalized Linear Models (GLMs) ? (slides, part 1)
    04:09
  • What are GLMs ? (slides, part 2)
    04:39
  • What are GLMs ? (slides, part 3)
    05:41
  • What are GLMs ? (slides, part 4)
    05:24
  • What are GLMs ? (slides, part 5)
    04:49
  • 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 1)
    04:18
  • GLM: Womens' Role in Society (part 2)
    04:32
  • GLM: Womens' Role in Society (part 3)
    04:16
  • GLM: Colonic Polyps (part 1)
    04:27
  • GLM: Colonic Polyps (part 2)
    05:52
  • 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)
    07:13
  • Smoothers and GAMs (part 3)
    05:07
  • Begin Base Graphics
    06:30
  • Begin ggplot Graphics as Compared to Base
    11:01
  • More Graphics Features
    07:08
  • Still More Graphics Features
    07:16
  • More on Plotting Characters
    07:06
  • 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
    08:18
  • 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 1)
    07:53
  • Adding Nonlinear Fits to Plots (part 2)
    06:10
  • Adding Nonlinear Fits to Plots (part 3)
    05:51
  • Adding Nonlinear Fits to Plots (part 4)
    05:26
  • Adding Nonlinear Fits to Plots (part 5)
    07:22
  • Boxplots (part 1)
    05:55
  • Boxplots (part 2)
    07:25
  • Boxplots (part 3)
    07:23
  • Boxplots (part 4)
    10:21
  • Histograms
    06:55
  • Time Series and Piechart
    06:07
  • Stripchart and Pairs Plot
    06:59
  • Exercise Solution and Shingles
    07:02
  • Shingles, Coplot, and Interaction Plots
    06:35
  • Box and Whiskers Plot and Design Plot
    08:15
  • Interaction and XYPlots
    06:26
  • Effects Sizes
    05:35
  • Bubble and Sunflower Plots
    07:54

About the Author

Geoffrey Hubona, 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. 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 (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). 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. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

More From Author

Reviews

Ashutosh Singh
5

Very good course for learning R programming with handson tutorials.

The Comprehensive Statistics and Data Science with R Course

  • 19:42:10
  • 219
  • 25
  • Language: English
  • 15 days Money back Gurantee
  • Unlimited Access
  • Android, iPhone and iPad Access
  • Certificate of Completion

Course Summary

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

Read More

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.

Pre-Requisites

  • 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.

About the Author

Geoffrey Hubona, 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. 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 (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). 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. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

More From Author

Review & Rating

Ashutosh Singh 5

Very good course for learning R programming with handson tutorials.