Data Science Foundations: Data Mining in Python
3h 4mIntermediate2021-03-26
Authors
Barton Poulson
Professor, Designer, Data Analytics Expert
Course details
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. In this course, instructor Barton Poulson introduces you to data mining that uses the programming language Python. Barton goes over some preliminaries, such as the tools you may use for data mining. He discusses aspects of dimensionality reduction, then explains clustering, including hierarchical clustering, k-Means, DBSCAN, and more. Barton covers classification, including kNN and decision trees. He goes into association analysis and introduces you to Apriori, Eclat, and FP-Growth. Barton steps you through a time-series decomposition, then concludes with sentiment scoring and other text mining tools.
Skills covered
PythonFoundationsData AnalysisProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceSoftware Development
Concepts
Introduction
- Python for data mining
- What you should know
- Exercise files
Preliminaries
- Tools for data mining
- The CRISP-DM data mining model
- Privacy copyright and bias
- Validating results
Dimensionality Reduction
- Dimensionality reduction overview
- Handwritten digits dataset
- PCA
- LDA
- t-SNE
- Challenge PCA
- Solution PCA
Clustering
- Clustering overview
- Penguin dataset
- Hierarchical clustering
- K-means
- DBSCAN
- Challenge K-means
- Solution K-means
Classification
- Classification overview
- Spambase dataset
- KNN
- Naive Bayes
- Decision trees
- Challenge KNN
- Solution KNN
Association Analysis
- Association analysis overview
- Groceries dataset
- Apriori
- Eclat
- FP-Growth
- Challenge Apriori
- Solution Apriori
Time-Series Mining
- Time-series mining
- Air Passengers dataset
- Time-Series decomposition
- ARIMA
- MLP
- Challenge Decomposition
- Solution Decomposition
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