Python data analysis: perform data collection, data processing, wrangling, visualization, and more using python, third edition

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, youâ#x80;#x99;ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, youâ#x80;#x99;ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. Youâ#x80;#x99;ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, youâ#x80;#x99;ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, youâ#x80;#x99;ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, youâ#x80;#x99;ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.

Python data analysis : perform data collection, data processing, wrangling, visualization, and more using python, third edition -- Contributors -- Table of Contents -- Preface -- 1. Section 1: Foundation for Data Analysis -- 1: Getting Started with Python Libraries -- 2: NumPy and pandas -- 3: Statistics -- 4: Linear Algebra -- 2. Section 2: Exploratory Data Analysis and Data Cleaning -- 5: Data Visualization -- 6: Retrieving, Processing, and Storing Data -- 7: Cleaning Messy Data -- 8: Signal Processing and Time Series -- 3. Section 3: Deep Dive into Machine Learning -- 9: Supervised Learning -- Regression Analysis -- 10: Supervised Learning -- Classification Techniques -- 11: Unsupervised Learning -- PCA and Clustering -- 4. Section 4: NLP, Image Analytics, and Parallel Computing -- 12: Analyzing Textual Data -- 13: Analyzing Image Data -- 14: Parallel Computing Using Dask -- Other Books You May Enjoy -- Index.