Python Data Analysis: Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering, 4th Edition

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Understand data analysis pipelines using Python Data Analysis, machine learning, pandas, scikit-learn, and data visualization techniques. Build scalable workflows for time series, NLP, image analytics, and big data processing.

Key Features

Prepare, clean, and transform data with Python, pandas, and exploratory data analysis techniques
Apply machine learning with Python using regression, classification, clustering, PCA, and Bayesian methods
Scale analytics workflows using Dask, Ray, Modin, and PySpark
Modern data analysis goes beyond cleaning and visualizing data. Today’s practitioners need to build scalable data pipelines, apply machine learning, work with text and image data, and understand emerging AI techniques such as Generative AI and Large Language Models (LLMs). This guide shows you how to tackle these challenges using Python’s modern data ecosystem.

Unlike books focused on a single library or technique, this book provides an end-to-end approach to Python data analysis. You’ll learn how to move from data preparation and exploratory analysis to machine learning, NLP, image analytics, scalable processing, and AI-powered workflows.

Starting with statistical foundations, you’ll learn how to clean, transform, wrangle, and visualize data. You’ll then explore time series analysis, signal processing, forecasting, and predictive analytics before applying machine learning techniques such as regression, classification, clustering, PCA, probabilistic methods, and Bayesian approaches.

The book also covers graph analytics, sentiment analysis, NLP, image analytics, Generative AI, and LLMs. Finally, you’ll learn to scale analytics workflows using Dask, Modin, Ray, and PySpark.

By the end of the book, you’ll be able to build end-to-end data analysis pipelines and apply modern data science and AI techniques to solve real-world challenges.

What you will learn

Prepare, clean, and transform data for exploratory data analysis and data wrangling
Analyze and visualize data using Python and pandas
Perform time series analysis, forecasting, and signal processing
Apply machine learning with Python using scikit-learn techniques
Use regression, classification, clustering, PCA, and Bayesian methods
Perform sentiment analysis, NLP, graph analytics, and image analytics
Accelerate workflows using Dask, Modin, and Ray
Build scalable big data analytics pipelines with PySpark