Classifying and Clustering Data with R
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Classifying and Clustering Data with R

An all inclusive guide to get well versed with Classifying and Clustering Data with R
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0.0 (0 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.
2 students enrolled
Created by Packt Publishing
Last updated 9/2017
English
English [Auto-generated]
Price: $125
30-Day Money-Back Guarantee
Includes:
  • 2.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Know how to use hierarchical cluster analysis
  • Carry out cluster analysis using visualization methods such as Dendrogram and Silhouette plots
  • Find out how to perform non-hierarchical k-means clustering
  • See how to perform density-based clustering and clustering of tweets
  • Apply discriminant analysis for classification problems
  • Understand time-series decomposition, forecasting, clustering, and classification
  • Develop decision tree model for classification and prediction
View Curriculum
Requirements
  • In depth content balanced with tutorials that put theory into practice. The Video course is a Practical tutorials to help you get beyond the basics of data analysis with R, using real-world data sets and examples.
Description

This video course provides the steps you need to carry out classification and clustering with R/RStudio software. You’ll understand hierarchical clustering, non-hierarchical clustering, density-based clustering, and clustering of tweets. It also provides steps to carry out classification using discriminant analysis and decision tree methods.

In addition, we cover time-series decomposition, forecasting, clustering, and classification. It includes several example sets of data that you can use for the methodologies covered. The approaches are illustrated using practical applications to data belonging to various fields.

By the end the course, you will be well-versed with clustering and classification using Cluster Analysis, Discriminant Analysis, Time-series Analysis, and decision trees.

About The Author :


Dr. Bharatendra Rai is Professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit. His two master's degrees include specializations in quality, reliability, and OR from Indian Statistical Institute and another in statistics from Meerut University, India. He teaches courses on topics such as Analyzing Big Data, Business Analytics and Data Mining, Twitter and Text Analytics, Applied Decision Techniques, Operations Management, and Data Science for Business. He has over twenty years' consulting and training experience, including industries such as automotive, cutting tool, electronics, food, software, chemical, defense, and so on, in the areas of SPC, design of experiments, quality engineering, problem solving tools, Six-Sigma, and QMS. His work experience includes extensive research experience over five years at Ford in the areas of quality, reliability, and six-sigma. His research publications include journals such as IEEE Transactions on Reliability, Reliability Engineering & System Safety, Quality Engineering, International Journal of Product Development, International Journal of Business Excellence, and JSSSE. He has been keynote speaker at conferences and presented his research work at conferences such as SAE World Conference, INFORMS Annual Meetings, Industrial Engineering Research Conference, ASQs Annual Quality Congress, Taguchi's Robust Engineering Symposium, and Canadian RAMS.

Dr. Rai has won awards for Excellence and exemplary teamwork at Ford for his contributions in the area of applied statistics. He also received an Employee Recognition Award by FAIA for his Ph.D. dissertation in support of Ford Motor Company. He is certified as ISO 9000 lead assessor from British Standards Institute, ISO 14000 lead assessor from Marsden Environmental International, and Six Sigma Black Belt from ASQ.

Who is the target audience?
  • This video course is for data scientist or a data analyst who want to Master the art of Data Analysis and Statistics using the R programming language.
Compare to Other Cluster Analysis Courses
Curriculum For This Course
19 Lectures
02:29:43
+
Cluster Analysis
8 Lectures 01:00:02
This video gives an overview of entire course.
Preview 02:29

This video covers data preparation for clustering.
Iris Data
08:29

This video covers clustering using dendrogram.
Hierarchical Clustering Using Dendrogram with R
08:51

In this video, k-means clustering is covered.
Nonhierarchical K-means Clustering with R
11:16

In this video, we will see how to do data preparation for density based clustering.
Preparing Data and Packages for Density-based Clustering
05:39

In this video, we will see how to do density based clustering.
Density-based Clustering with R
07:22

In this video, we will see how to prepare for text data clustering.

Text Data Preparation for Clustering
07:00

In this video, we will see how to do clustering for words or tweets.

Clustering Words or Tweets with R
08:56
+
Discriminant Analysis
4 Lectures 30:20

This video shows how to do discriminant analysis in R.

Preview 07:28

This video shows how to do model interpretation.
Model Interpretation
07:40

This video shows how to do visualizations in discriminant analysis.
Visualization
09:01

This video shows how to do model assessment.
Model Assessment
06:11
+
Time Series Analysis
4 Lectures 36:29
This video shows how to do time series decomposition in R.
Preview 07:55

This video shows how to do time series forecasting in R.
Time Series Forecasting with R
09:23

This video shows how to do time series clustering in R.
Time Series Clustering with R
11:38

This video shows how to do time series classification in R.
Time Series Classification with R
07:33
+
Decision Tree
3 Lectures 22:52

This video shows how to do decision tree in R.

Preview 06:05

This video shows how to do visualize decision tree.

Visualization of Decision Trees
09:02

This video shows how to assess classification performance.

Prediction and Misclassification Errors
07:45
About the Instructor
Packt Publishing
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