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Data Mining with R: Go from Beginner to Advanced!

Learn to use R software for data analysis, visualization, and to perform dozens of popular data mining techniques.
4.5
4.5/5
(395 reviews)
4,440 students
Created by

8.8

CourseMarks Score®

7.9

Freshness

8.5

Feedback

9.5

Content

Platform: Udemy
Video: 11h 54m
Language: English
Next start: On Demand

Top Data Mining courses:

Detailed Analysis

CourseMarks Score®

8.8 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

7.9 / 10
This course was last updated on 8/2020.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.5 / 10
We analyzed factors such as the rating (4.5/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.5 / 10
Video Score: 9.4 / 10
The course includes 11h 54m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 8 hours 34 minutes of 27 Data Mining courses on Udemy.
Detail Score: 9.5 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
17 resources.
0 exercise.
0 test.

Table of contents

Description

This is a “hands-on” business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on “best practices” for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer’s hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can “take them home” and apply them to their own unique data analysis and mining cases. There are also “hands-on” exercises to perform in each course section to reinforce the learning process. The target audience for the course includes undergraduate and graduate students seeking to acquire employable data analytics skills, as well as practicing predictive analytics professionals seeking to expand their repertoire of data analysis and data mining knowledge and capabilities.

You will learn

✓ Use R software for data import and export, data exploration and visualization, and for data analysis tasks, including performing a comprehensive set of data mining operations.
✓ Effectively use a number of popular, contemporary data mining methods and techniques in demand by industry including: (1) Decision, classification and regression trees (CART); (2) Random forests; (3) Linear and logistic regression; and (4) Various cluster analysis techniques.
✓ Apply the dozens of included “hands-on” cases and examples using real data and R scripts to new and unique data analysis and data mining problems.

Requirements

• Download and install no-cost R software (complete, easy-to-follow instructions are provided).
• Download and install no-cost RStudio IDE software (complete, easy-to-follow instructions are provided).

This course is for

• Anyone who wants to learn more about performing data analysis using a variety of popular, contemporary data mining techniques.
• Data Mining beginners and professionals who wish to enhance their data mining knowledge and skill levels
• Individuals seeking to gain more proficiency using the popular R and RStudio software suites.
• Undergraduate students seeking to acquire in-demand analytics skills to enhance employment opportunities.
• Graduate students seeking to acquire a wider repertoire of analytics skills for research data analysis tasks.

How much does the Data Mining with R: Go from Beginner to Advanced! course cost? Is it worth it?

The course costs $17.99. And currently there is a 82% discount on the original price of the course, which was $99.99. So you save $82 if you enroll the course now.
The average price is $15.0 of 27 Data Mining courses. So this course is 20% more expensive than the average Data Mining course on Udemy.

Does the Data Mining with R: Go from Beginner to Advanced! course have a money back guarantee or refund policy?

YES, Data Mining with R: Go from Beginner to Advanced! has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Data Mining with R: Go from Beginner to Advanced! course, but there is a $82 discount from the original price ($99.99). So the current price is just $17.99.

Who is the instructor? Is Geoffrey Hubona, Ph.D. a SCAM or a TRUSTED instructor?

Geoffrey Hubona, Ph.D. has created 27 courses that got 4,030 reviews which are generally positive. Geoffrey Hubona, Ph.D. has taught 30,922 students and received a 4.0 average review out of 4,030 reviews. Depending on the information available, Geoffrey Hubona, Ph.D. is a TRUSTED instructor.
Associate Professor of Information Systems
Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty 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.
Browse all courses by on Coursemarks.

8.8

CourseMarks Score®

7.9

Freshness

8.5

Feedback

9.5

Content

Platform: Udemy
Video: 11h 54m
Language: English
Next start: On Demand

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