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

Related Courses

Cluster Analysis in Data Mining
University of Illinois at Urbana-Champaign via Coursera Explorez vos données avec des algorithmes non supervisés
CentraleSupélec via OpenClassrooms Clustering Geolocation Data Intelligently in Python
Coursera Project Network via Coursera Cluster Analysis using RCmdr
Coursera Project Network via Coursera Applied Machine Learning: Algorithms
LinkedIn Learning