Python Data Analysis: An end-to-end guide covering data processing, data manipulation and data visualization

Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide

• Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wrangling
• Discover supervised, unsupervised, probabilistic, and Bayesian machine learning methods
• Get to grips with graph processing and sentiment analysis

Data analysis enables you to generate value from small and big data by discovering new patterns, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases 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, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy—to—follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. You'll also 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, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.

• Prepare, clean, and transform your data for exploratory analysis, manipulation, and wrangling.
• Explore concepts in signal processing, time series analysis, and predictive analytics.
• Understand and apply key machine learning techniques, including supervised, unsupervised, probabilistic, and Bayesian methods.
• Work with graph data and perform sentiment analysis.
• Handle large—scale image and text analytics efficiently.
• Accelerate data manipulation using Dask, Modin, and Ray.
• Perform scalable big data analytics with PySpark.

This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands—on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.