Both linear algebra and statistics are the foundation for any kind of data analysis activity. Statistics help us to get an initial descriptive understanding and make inferences from data. In the previous chapter, we have understood descriptive and inferential statistical measures for data analysis. On the other side, linear algebra is one of the fundamental mathematical subjects that is the core foundation for any data professional. Linear algebra is useful for working with vectors and matrices. Most of the data is available in the form of either a vector or a matrix. In-depth knowledge of linear algebra helps data analysts and data scientists understand the workflow of machine learning and deep learning algorithms, giving them the flexibility to design and modify the algorithms as per your business needs. For example, if you want to work with principal component...
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Book Overview & Buying
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Table Of Contents
Python Data Analysis - Third Edition
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Python Data Analysis
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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