python data analysis

Page counts in the search results vary, ranging from about 304 pages up to 674 pages.

The listed titles are available in both hardcover and paperback formats, and the language shown for these books is English.

The books target a range of readers: some assume only minimal statistics or basic Python knowledge, while others expect basic Python programming familiarity plus some mathematical foundations. Overall they span beginners through working analysts and data professionals.

Many of the books include working Python code and practical examples. Several provide starter code that readers can adapt, updated code repositories, hands-on case studies, and exercises to practice concepts.

The titles cover exploratory data analysis and basic statistics (frequencies, descriptive summaries, Q–Q plots, box plots) and a range of inferential and multivariate methods such as ANOVA, MANOVA, discriminant analysis, regression, principal component analysis, factor analysis, and cluster analysis.

NLP coverage includes Python basics for text processing, cleaning and feature engineering, classification and clustering, topic modeling and summarization, text similarity and embedding methods, sentiment analysis, named-entity recognition, and deep-learning approaches (including deep transfer learning).

Business analytics material includes applied case studies such as customer churn prediction, fraud detection, and sales forecasting. Finance topics include portfolio construction and optimization, Monte Carlo simulations, rebalancing, tax-loss harvesting, asset-allocation methods, and retirement-planning techniques.

They discuss putting models and pipelines into production via containerized applications and MLOps practices, deploying across major cloud platforms, and using workflow orchestration and real-time/streaming pipeline tools to manage production workloads.