We are in the age of information, where every movement will generate data in a variety of formats, such as text, images, geospatial data, and videos. Smartphones have reached rural areas of the world and people are capturing activities, especially in images and videos, and sharing them on social media platforms. This is how lots of big chunks of data are generated and most of the data is in image and video formats. Industry and research institutes want to analyze image and video datasets to generate value and make automated solutions to reduce costs. Image processing and computer vision are fields that explore and develop image- and video-based solutions. There are lots of opportunities for research, innovation, and start-ups in the area of computer vision. In this chapter, we focus on the basics of image processing to build your fundamental knowledge in the...
Python Data Analysis - Third Edition
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Python Data Analysis - Third Edition
By:
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)
Preface
Section 1: Foundation for Data Analysis
Free Chapter
Getting Started with Python Libraries
NumPy and pandas
Statistics
Linear Algebra
Section 2: Exploratory Data Analysis and Data Cleaning
Data Visualization
Retrieving, Processing, and Storing Data
Cleaning Messy Data
Signal Processing and Time Series
Section 3: Deep Dive into Machine Learning
Supervised Learning - Regression Analysis
Supervised Learning - Classification Techniques
Unsupervised Learning - PCA and Clustering
Section 4: NLP, Image Analytics, and Parallel Computing
Analyzing Textual Data
Analyzing Image Data
Parallel Computing Using Dask
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