Lecture Notes on Fundamental of Data Analysis#
This Jupyter Book contains lecture notes for the Fundamentals of Data Analysis Course, taught at the University of Catania by Prof. Antonino Furnari.
The notes are informal and meant to be used as teaching material - they may contain errors.
To point out any mistake, you can open an issue. Introductory slides to this course are available here.
Theory
- 1. Introduction to Data Analysis
- 2. Main data analysis concepts
- 3. Misure di Frequenze e Rappresentazione Grafica dei Dati
- 4. Misure di Tendenza Centrale, Dispersione e Forma
- 5. Associazione tra Variabili
- 6. Probability for Data Manipulation
- 7. Common Probability Distributions
- 8. Basic Elements of Information Theory
- 9. Statistical Inference
- 10. Linear Regression
- 11. Logistic Regression
- 12. Causal Data Analysis
- 13. Data as N-Dimensional Points
- 14. Clustering
- 15. Density Estimation
- 16. Principal Component Analysis
- 17. Introduction to Predictive Modelling and Regression Models
- 18. Classification Task and Evaluation Measures
- 19. Discriminative Models for Classification
- 20. Generative Models for Classification
Laboratories
- 21. Introduzione ai laboratori e Installazione dell’Ambiente di Lavoro
- 22. Introduzione a Python
- 23. Introduzione a Numpy
- 24. Introduzione a Matplotlib
- 25. Introduzione a Pandas
- 26. Laboratorio su Misure di Frequenze e Rappresentazione Grafica dei Dati
- 27. Laboratorio su Misure di Tendenza Centrale, Dispersione e Forma
- 28. Associazione tra Variabili
- 29. Exploratory Analysis on the Heart Disease Dataset
- 30. Laboratory on Statistical Inference
- 31. Laboratorio su Regressione Lineare
- 32. Laboratorio su regressione logistica
- 33. Linear and Logistic Regression Laboratory
- 34. Clustering, Density Estimation, and Principal Component Analysis
- 35. Customer Segmentation Analysis Example
- 36. Classificazione