Data Science and Analytics with Python

Title: Data Science and Analytics with Python

Citation: Jesús Rogel-Salazar, CRC Press, Taylor & Francis Group, 2017, ISBN-13: 978-1-498-74209-2.

Key Topics:

1. Introduction to Data Science: Definition, roles, and tools in data science.
2. Python Fundamentals: Basic types, control flow, computation, and data manipulation.
3. Machine Learning and Pattern Recognition: Artificial intelligence, learning, predicting, classifying, and feature selection.
4. Regression: Relationships between variables, multivariate linear regression, and polynomial regression.
5. Clustering: k-means clustering, cluster validation, and hierarchical clustering.
6. Classification: Classification techniques, confusion matrices, ROC and AUC, KNN, logistic regression, and naïve Bayes.
7. Decision Trees and Ensemble Techniques: Hierarchical clustering, decision trees, bagging, boosting, and random forests.
8. Dimensionality Reduction: Principal component analysis, singular value decomposition, and recommendation systems.
9. Support Vector Machines: Kernel methods, linear and non-linear SVM, and cross-validation.

Summary:

"Data Science and Analytics with Python" by Jesús Rogel-Salazar is a comprehensive guide for data analysts and budding data scientists. The book covers the basics of data science, Python programming, and various machine learning algorithms including regression, clustering, classification, decision trees, ensemble techniques, dimensionality reduction, and support vector machines. It provides practical examples and uses Python as a tool to implement these algorithms, making it a valuable resource for those looking to delve into data analytics and machine learning.