Data Science Foundations: Data Mining in R

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Barton Poulson

3:51:30

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01 Introduction

001 R for data mining.mp4

02:35

002 Who should watch this course.mp4

00:49

003 Exercise files.mp4

00:52

02 Preliminaries

001 Tools for data mining.mp4

06:06

002 The CRISP-DM data mining model.mp4

03:20

003 Privacy copyright and bias.mp4

04:26

004 Validating results.mp4

06:15

03 Dimensionality Reduction

001 Dimensionality reduction overview.mp4

06:35

002 Dataset Handwritten digits.mp4

05:57

003 PCA.mp4

05:33

004 LDA.mp4

08:06

005 t-SNE.mp4

03:56

006 Challenge PCA.mp4

01:28

007 Solution PCA.mp4

04:23

04 Clustering

001 Clustering overview.mp4

06:53

002 Dataset Penguins.mp4

02:40

003 Hierarchical clustering.mp4

07:27

004 K-means.mp4

04:16

005 DBSCAN.mp4

06:19

006 Challenge K-means.mp4

01:36

007 Solution K-means.mp4

05:10

05 Classification

001 Classification overview.mp4

06:10

002 Dataset Spambase.mp4

05:00

003 K-nn.mp4

07:20

004 Naive Bayes.mp4

06:12

005 Decision trees.mp4

07:01

006 Challenge K-nn.mp4

04:07

007 Solution K-nn.mp4

03:48

06 Association Analysis

001 Association analysis overview.mp4

06:02

002 Dataset Groceries.mp4

01:59

003 Apriori.mp4

04:53

004 Eclat.mp4

03:42

005 CBA.mp4

06:48

006 Challenge Apriori.mp4

02:08

007 Solution Apriori.mp4

02:26

07 Time-Series Mining

04:23

002 Dataset AirPassengers.mp4

03:04

003 Time-series decomposition.mp4

05:54

004 ARIMA.mp4

06:59

005 MLP.mp4

07:08

006 Challenge Decomposition.mp4

02:29

007 Solution Decomposition.mp4

03:39

08 Text Mining

001 Text mining overview.mp4

04:34

002 Dataset The Iliad.mp4

02:39

003 Sentiment analysis Binary classification.mp4

06:24

004 Sentiment analysis Sentiment scoring.mp4

07:24

005 Visualizing Word pairs.mp4

06:36

006 Challenge Sentiment scoring.mp4

01:10

007 Solution Sentiment scoring.mp4

04:13

09 Conclusion

001 Next steps.mp4

02:36

Description

Data science continues to grow in sophistication and demand at an exponential rate. Data mining is the area of data science that focuses on finding actionable patterns in large and diverse datasets: clusters of similar customers, trends over time that can only be spotted after disentangling seasonal and random effects, and new methods for predicting important outcomes. Instructor Barton Poulson focuses on data mining in R, presents a broad range of algorithms including machine learning methods, and offers important information on laws and policies that affect data mining. Barton gives an overview of dimensionality reduction. He introduces clustering, including hierarchical clustering, then goes into association analysis. He explains time-series mining and decomposition, then concludes with text mining, sentiment analysis, and sentiment scoring.

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Data Science

Data Mining

R Programming

Barton Poulson

Instructor's Courses

Founder of datalab.cc, author for LinkedIn Learning, associate professor of psychology at Utah Valley University. I teach people how to use data to find practical solutions to real-life problems. #DataIsForDoing

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