Data Science Foundations: Data Mining in Python

Description

In this course, you will :

• Introduces you to data mining using the Python programming language
• goes over some preliminary topics, such as data mining tools.
• discusses aspects of dimensionality reduction before explaining clustering techniques such as hierarchical clustering, k-Means, DBSCAN, and others.
• includes classification techniques such as kNN and decision trees He introduces you to Apriori, Eclat, and FP-Growth through association analysis.
• It takes you through a time-series decomposition process before concluding with sentiment scoring and other text mining tools.

Syllabus :

1. Preliminaries

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

 

2. Dimensionality Reduction

• Handwritten digits dataset
• PCA
• LDA
• t-SNE

 

3. Clustering

• Penguin dataset
• Hierarchical clustering
• K-means
• DBSCAN

 

4. Classification

• Spambase dataset
• KNN
• Naive Bayes
• Decision trees

 

5. Association Analysis

• Groceries dataset
• Apriori
• Eclat
• FP-Growth

 

6. Time-Series Mining

• Time-series mining
• Air Passengers dataset
• Time-Series decomposition
• ARIMA
• MLP

 

7. Text Mining

• Iliad dataset
• Sentiment analysis: Binary classification
• Sentiment analysis: Sentiment scoring
• Word pairs

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