{"product_id":"python-data-analysis-fourth-avinash-navlani-9781806022878","title":"Python Data Analysis - Fourth Edition: Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering","description":"\u003cp\u003e\u003cstrong\u003eUnderstand 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.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Prepare, clean, and transform data with Python, pandas, and exploratory data analysis techniques\u003c\/p\u003e\u003cp\u003e- Apply machine learning with Python using regression, classification, clustering, PCA, and Bayesian methods\u003c\/p\u003e\u003cp\u003e- Scale analytics workflows using Dask, Ray, Modin, and PySpark\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eModern 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.\u003c\/p\u003e\u003cp\u003eUnlike 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.\u003c\/p\u003e\u003cp\u003eStarting 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.\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003eBy 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Prepare, clean, and transform data for exploratory data analysis and data wrangling\u003c\/p\u003e\u003cp\u003e- Analyze and visualize data using Python and pandas\u003c\/p\u003e\u003cp\u003e- Perform time series analysis, forecasting, and signal processing\u003c\/p\u003e\u003cp\u003e- Apply machine learning with Python using scikit-learn techniques\u003c\/p\u003e\u003cp\u003e- Use regression, classification, clustering, PCA, and Bayesian methods\u003c\/p\u003e\u003cp\u003e- Perform sentiment analysis, NLP, graph analytics, and image analytics\u003c\/p\u003e\u003cp\u003e- Accelerate workflows using Dask, Modin, and Ray\u003c\/p\u003e\u003cp\u003e- Build scalable big data analytics pipelines with PySpark\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis 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. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Getting Started with Python Libraries\u003c\/p\u003e\u003cp\u003e- NumPy and Pandas\u003c\/p\u003e\u003cp\u003e- Statistics for Data Insights\u003c\/p\u003e\u003cp\u003e- Linear Algebra\u003c\/p\u003e\u003cp\u003e- Data Visualization\u003c\/p\u003e\u003cp\u003e- Retrieving, Processing, and Storing Data\u003c\/p\u003e\u003cp\u003e- Cleaning Messy Data\u003c\/p\u003e\u003cp\u003e- Time Series Analysis\u003c\/p\u003e\u003cp\u003e- Supervised Learning: Regression and Classification\u003c\/p\u003e\u003cp\u003e- Unsupervised Learning: Dimensionality Reduction, Clustering, Anomaly Detection\u003c\/p\u003e\u003cp\u003e- Ensemble Methods: Bagging and Boosting Methods\u003c\/p\u003e\u003cp\u003e- Artificial Neural Networks and Deep Learning\u003c\/p\u003e\u003cp\u003e- Analyzing Text Data\u003c\/p\u003e\u003cp\u003e- Analyzing Image Data\u003c\/p\u003e\u003cp\u003e- LLMs and Gen AI\u003c\/p\u003e\u003cp\u003e- Parallel Computing Using Dask, Modin, and Ray\u003c\/p\u003e\u003cp\u003e- Big Data Analytics using PySpark\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Avinash Navlani,Cornellius Yudha Wijaya\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 1806022877\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9781806022878\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Packt Publishing\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 06\/26\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 766\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 2.85lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.25h x 7.50w x 1.53d","brand":"Avinash Navlani","offers":[{"title":"Paperback","offer_id":49002062414079,"sku":"9781806022878","price":44.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/python-data-analysis-fourth-avinash-navlani-9781806022878","provider":"WR Book House","version":"1.0","type":"link"}