Avinash Navlani, Cornellius Yudha Wijaya | Python Data Analysis. Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering. 4th Edition (2026) [PDF, EPUB] [EN]

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
Who this book is for
This book is for data analysts, data scientists, business analysts, statisticians, students, and academic professionals who want to strengthen their Python Data Analysis skills. It is ideal for readers looking to apply data science with Python to real-world problems involving data preparation, visualization, machine learning, NLP, image analytics, and big data processing. A basic understanding of mathematics and working knowledge of Python will help you get the most from this book.

Table of Contents
Getting Started with Python Libraries
NumPy and Pandas
Statistics for Data Insights
Linear Algebra
Data Visualization
Retrieving, Processing, and Storing Data
Cleaning Messy Data
Time-Series Analysis
Supervised Learning: Regression and Classification
Unsupervised Learning: Dimensionality Reduction, Clustering, Anomaly Detection
Ensemble Methods: Bagging and Boosting Methods
Artificial Neural Networks and Deep Learning
Analyzing Text Data
Analyzing Image Data
LLMs and Gen AI
Parallel Computing Using Dask, Modin, and Ray
Big Data Analytics using PySpark