Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

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, you’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, 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. In the concluding chapters, you’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, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Table of Contents (20 chapters)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing


The core fundamentals of statistics will provide the foundation for data analysis, facilitating how data is described and understood. In this chapter, you have learned the basics of statistics such as attributes and their different types such as nominal, ordinal, and numeric. You have also learned about mean, median, and mode for measuring central tendency. Range, IQR, variance, and standard deviation measures are used to estimate variability in the data; skewness and kurtosis are used for understanding data distribution; covariance and correlation are used to understand the relationship between variables. You have also seen inferential statistics topics such as the central limit theorem, collecting samples, and parametric and non-parametric tests. You have also performed hands-on coding on statistics concepts using the pandas and scipy.stats libraries.

The next chapter, Chapter 4, Linear Algebra, will help us to learn how to solve the linear system of equations, find Eigenvalues...