95% off PLS Path Modeling with the Desktop PLS-GUI Application (Coupon)

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PLS Path Modeling with the Desktop PLS-GUI Application - Udemy Coupon

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Coupon & course info

Course Name: PLS Path Modeling with the Desktop PLS-GUI Application

Subtitle: How to conduct PLS path modeling using a desktop application created with R software

Instructor: Taught by Geoffrey Hubona, Ph.D., Professor of Information Systems

Category: Academics

Subcategory: Math & Science

Provided by: Udemy

Price: $30 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of October 3, 2016…

Students: 193 students enrolled

Ratings: 2 reviews

Rank: ranked #97e in Udemy Academics Courses

Brief course description

PLS Path Modeling with the Desktop PLS-GUI Application is a course that both teaches about the practice of variance-based structural equation modeling (SEM), sometimes referred to as PLS Path Modeling, and is a course that provides a beta application, the desktop PLS-GUI, to do so. The PLS-GUI application was developed using the visual RGtk2 programming language in R software. RGtk2 is the R-based GUI language extension derived from the public GTK+ (GIMP Tool Kit) language.

The PLS-GUI desktop application was jointly developed by Dr. Geoffrey Hubona and Dean Lim, both of whom are co-instructors of this course.

Course participants are encouraged to use the included PLS-GUI desktop application with their own data and PLS model files. Please note that this was a beta version application which is no longer directly supported by our staff. So, while we are limited in our ability to provide fixes or solutions to use-related issues which may arise installing or using the application, we are happy to answer any and all questions relevant to the course content per se.

(Read more about this course on the official course page.)

Geoffrey Hubona, Ph.D. bio

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.; I am an avid user and statistical programmer of all things related to SEM especially the newer Partial Least Squares SEM. I believe PLS-SEM will be the future of big data as related to marketing. I am also a data analyst and CMRP marketing researcher.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 63 lessons

Duration: 4.5 hours of video

What you get: This course teaches students about PLS path modeling, including multi-group analysis and prediction-oriented segmentation.

Target audience: Anyone involved with PLS path modeling will benefit from this course, whether they are a novice or more experienced PLS path modeler.

Requirements: Students will need to install R and the desktop PLS-GUI application with the provided instructions.

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Visualization and Imputation of Missing Data (Coupon)

Attention: This post may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission at no extra cost to you. Thanks!

Visualization and Imputation of Missing Data - Udemy Coupon

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Bonus: download a free guide that reveals 11 tricks for getting the biggest discounts on Udemy courses, including this course.

Coupon & course info

Course Name: Visualization and Imputation of Missing Data

Subtitle: Learn to create numerous unique visualizations to better understand patterns of missing data in your data sample.

Instructor: Taught by Geoffrey Hubona, Ph.D., Professor of Information Systems

Category: Academics

Subcategory: Social Science

Provided by: Udemy

Price: $25 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of October 3, 2016…

Students: 562 students enrolled

Ratings: 10 reviews

Rank: ranked #138e in Udemy Academics Courses

Brief course description

There are many problems associated with analyzing data sets that contain missing data. However, there are various techniques to ‘fill in,’ or impute, missing data values with reasonable estimates based on the characteristics of the data itself and on the patterns of ‘missingness.’ Generally, techniques appropriate for imputing missing values in multivariate normal data and not as useful when applied to non-multivariate-normal data. This Visualization and Imputation of Missing Data course focuses on understanding patterns of ‘missingness’ in a data sample, especially non-multivariate-normal data sets, and teaches one to use various appropriate imputation techniques to “fill in” the missing data. Using the VIM and VIMGUI packages in R, the course also teaches how to create dozens of different and unique visualizations to better understand existing patterns of both the missing and imputed data in your samples.

The course teaches both the concepts and provides software to apply the latest non-multivariate-normal-friendly data imputation techniques, including: (1) Hot-Deck imputation: the sequential and random hot-deck algorithm; (2) the distance-based, k-nearest neighbor imputation approach; (3) individual, regression-based imputation; and (4) the iterative, model-based, stepwise regression imputation technique with both standard and robust methods (the IRMI algorithm). Furthermore, the course trains one to recognize the patterns of missingness using many vibrant and varied visualizations of the missing data patterns created by the professional VIMGUI software included in the course materials and made available to all course participants.

This course is useful to anyone who regularly analyzes large or small data sets that may contain missing data. This includes graduate students and faculty engaged in empirical research and working professionals who are engaged in quantitative research and/or data analysis. The visualizations that are taught are especially useful to understand the types of data missingness that may be present in your data and consequently, how best to deal with this missing data using imputation. The course includes the means to apply the appropriate imputation techniques, especially for non-multivariate-normal sets of data which tend to be most problematic to impute.

(Read more about this course on the official course page.)

Geoffrey Hubona, Ph.D. bio

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.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 38 lessons

Duration: 5 hours of video

What you get: Use visualizations created by R software to identify patterns of ‘missingness’ in data sets and to impute reasonable values to replace the missing data.

Target audience: This course is useful for anyone analyzing large or small data sets that may contain missing data.

Requirements: Students will need to install R software but ample instructions for doing so are provided.

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons