Data Analytics Demystified using Python

• Theory to Practice: Perfectly blends statistical theory with hands-on Python implementation.

• Flexible Environment: Programs are designed to work seamlessly in both the Spyder IDE and Google Colab, allowing for learning from the desktop or the cloud.

• Diverse Contexts: Features real-world examples and datasets across business, management, and engineering sectors.

• Self-Paced Learning: Includes step-by-step Python programs for each analytical method to facilitate independent study.

• End-to-End Workflow: Supports the entire analytics life-cycle from data preparation and statistical testing to machine learning and advanced visualization.

• Rich Pedagogy: Equipped with extensive worked examples, chapter-end exercises, and practice problems.

• Advanced Topics: Features contemporary content, including Structural Equation Modeling (SEM), Time-series Forecasting, and Natural Language Processing (NLP)

Rights:  World Rights