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Key benefits

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

Description

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.

Who is this book for?

This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.

What you will learn

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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Amazon verified reviews

Sameet Feb 24, 2021

I would definitely recommend this book to anyone who would like to learn machine learning with python it starts right from basics to NLP

Amazon Verified review

Rachel Mae Lademora Jan 03, 2024

This book is very informative and well explained in detailed it helped me a lot.

Rob Jul 19, 2021

Great book. Curated to be easy to read most important concepts and tools.

Nithin Apr 08, 2021

Python Data Analysis is a great book for beginners as well as anyone looking to brush up Python skills. The best thing about this book is, the author does not assume the target audience to have any Python knowledge and hence, all the topics are detailed with theory and workable practical code. The book covers in detail on Data Visualization packages and methods and touches on Machine Learning and Linear Regression towards the end.Happy learning!

ER Feb 24, 2021

If you prefer breadth over depth then this book is for you. It covers a thorough array (no pun intended) of material needed to get started in data science, from setting up your computer all the way to implementing ML algorithms at scale. It provides high level background knowledge about the topics it covers; introduces libraries, techniques, and algorithms commonly used in DS; and includes code examples.That being said, this is not the place to go for deep level explanations or details about potential problems and pitfalls or things to look out for. I like this book as an easy to navigate reference where I can quickly find what I am looking for and get a general idea of what I need to do and what my options are.

Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python , Third Edition

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