Data Science Foundations: Data Mining

Offered By: LinkedIn Learning

Tags

Data Mining Courses Python Courses Dimensionality Reduction Courses Clustering Courses K-means Courses CRISP-DM Courses t-SNE Courses DBSCAN Courses PCA Courses

Course Description

Overview

Learn the key concepts and skills behind one of the most important elements of data science: data mining.

Syllabus

Introduction

Python for data mining
What you should know
Exercise files

1. Preliminaries

Tools for data mining
The CRISP-DM data mining model
Privacy, copyright, and bias
Validating results

2. Dimensionality Reduction

Dimensionality reduction overview
Handwritten digits dataset
PCA
LDA
t-SNE
Challenge: PCA
Solution: PCA

3. Clustering

Clustering overview
Penguin dataset
Hierarchical clustering
K-means
DBSCAN
Challenge: K-means
Solution: K-means

4. Classification

Classification overview
Spambase dataset
KNN
Naive Bayes
Decision trees
Challenge: KNN
Solution: KNN

5. Association Analysis

Association analysis overview
Groceries dataset
Apriori
Eclat
FP-Growth
Challenge: Apriori
Solution: Apriori

6. Time-Series Mining

Time-series mining
Air Passengers dataset
Time-Series decomposition
ARIMA
MLP
Challenge: Decomposition
Solution: Decomposition

7. Text Mining

Text mining overview
Iliad dataset
Sentiment analysis: Binary classification
Sentiment analysis: Sentiment scoring
Word pairs
Challenge: Sentiment scoring
Solution: Sentiment scoring

Conclusion

Next steps

Taught by

Barton Poulson

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