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Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

- 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 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.

- Explore data science and its various process models
- Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values
- Create interactive visualizations using Matplotlib, Seaborn, and Bokeh
- Retrieve, process, and store data in a wide range of formats
- Understand data preprocessing and feature engineering using pandas and scikit-learn
- Perform time series analysis and signal processing using sunspot cycle data
- Analyze textual data and image data to perform advanced analysis
- Get up to speed with parallel computing using Dask

Download this book in **EPUB** and **PDF** formats

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Publication date :
Feb 5, 2021

Length
478 pages

Edition :
3rd Edition

Language :
English

ISBN-13 :
9781789955248

Category :

Languages :

Concepts :

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Preface

1. Section 1: Foundation for Data Analysis

2. Getting Started with Python Libraries

3. NumPy and pandas

4. Statistics

5. Linear Algebra

6. Section 2: Exploratory Data Analysis and Data Cleaning

7. Data Visualization

8. Retrieving, Processing, and Storing Data

9. Cleaning Messy Data

10. Signal Processing and Time Series

11. Section 3: Deep Dive into Machine Learning

12. Supervised Learning - Regression Analysis

13. Supervised Learning - Classification Techniques

14. Unsupervised Learning - PCA and Clustering

15. Section 4: NLP, Image Analytics, and Parallel Computing

16. Analyzing Textual Data

17. Analyzing Image Data

18. Parallel Computing Using Dask

19. Other Books You May Enjoy

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