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

What this book covers

Chapter 1, Getting Started with Python Libraries, explains the data analyst process and the successful installation of Python libraries and Anaconda. Also, we will discuss Jupyter Notebook and its advanced features.

Chapter 2, NumPy and Pandas, introduces NumPy and Pandas. This chapter provides a basic overview of NumPy arrays, Pandas DataFrames, and their associated functions.

Chapter 3, Statistics, gives a quick overview of descriptive and inferential statistics.

Chapter 4, Linear Algebra, gives a quick overview of linear algebra and its associated NumPy and SciPy functions.

Chapter 5, Data Visualization, introduces us to the matplotlib, seaborn, Pandas plotting, and bokeh visualization libraries.

Chapter 6, Retrieving, Processing, and Storing Data, explains how to read and write various data formats, such as CSV, Excel, JSON, HTML, and Parquet. Also, we will discuss how to acquire data from relational and NoSQL databases.

Chapter 7, Cleaning Messy Data, explains how to preprocess raw data and perform feature engineering.

Chapter 8, Signal Processing and Time Series, contains time series and signal processing examples using sales, beer production, and sunspot cycle dataset. In this chapter, we will mostly use NumPy, SciPy, and statsmodels.

Chapter 9, Supervised Learning – Regression Analysis, explains linear regression and logistic regression in detail with suitable examples using the scikit-learn library.

Chapter 10, Supervised Learning – Classification Techniques, explains various classification techniques, such as naive Bayes, decision tree, K-nearest neighbors, and SVM. Also, we will discuss model performance evaluation measures.

Chapter 11, Unsupervised Learning – PCA and Clustering, gives a detailed discussion on dimensionality reduction and clustering techniques. Also, we will evaluate the clustering performance.

Chapter 12, Analyzing Textual Data, gives a quick overview of text preprocessing, feature engineering, sentiment analysis, and text similarity. This chapter mostly uses the NLTK, SpaCy, and scikit-learn libraries.

Chapter 13, Analyzing Image Data, gives a quick overview of image processing operations using OpenCV. Also, we will discuss face detection.

Chapter 14, Parallel Computing Using Dask, explains how to perform data preprocessing and machine learning modeling in parallel using Dask.