Python Data Analysis - Third Edition | Perform data collection, data processing, wrangling, visualization, and model building using Python
Language: English
Published by Packt Publishing, 2021
- Softcover
- New
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£ 47.52
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Python Data Analysis - Third Edition | Perform data collection, data processing, wrangling, visualization, and model building using Python | Avinash Navlani (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Packt Publishing | EAN 9781789955248 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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- Title
- Python Data Analysis - Third Edition | Perform data collection, data processing, wrangling, visualization, and model building using Python
- Author
- Avinash Navlani (u. a.)
- Publisher
- Packt Publishing
- Publication year
- 2021
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1789955246
- ISBN 13
- 9781789955248
- Edition
- 3rd Edition
- Item weight
- 885 grams
- Dimensions
- 235 x 191 x 26 mm
- Seller catalogs
- Bücher
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide
Key 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
Book 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.
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
Who this book is 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.
Table of Contents
- Getting Started with Python Libraries
- NumPy and Pandas
- Statistics
- Linear Algebra
- Data Visualization
- Retrieving, Processing, and Storing Data
- Cleaning Messy Data
- Signal Processing and Time Series
- Supervised Learning - Regression Analysis
- Supervised Learning - Classification Techniques
- Unsupervised Learning - PCA and Clustering
- Analyzing Textual Data
- Analyzing Image Data
- Parallel Computing using Dask
"Synopsis" may belong to another edition of this title.
About the Author
Avinash Navlani has over 8 years of experience working in data science and AI. Currently, he is working as a senior data scientist, improving products and services for customers by using advanced analytics, deploying big data analytical tools, creating and maintaining models, and onboarding compelling new datasets. Previously, he was a university lecturer, where he trained and educated people in data science subjects such as Python for analytics, data mining, machine learning, database management, and NoSQL. Avinash has been involved in research activities in data science and has been a keynote speaker at many conferences in India.
Armando Fandango creates AI-empowered products by leveraging his expertise in deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership roles as the chief data scientist and director at start-ups and large enterprises. He has advised high-tech AI-based start-ups. Armando has authored books such as Python Data Analysis - Second Edition and Mastering TensorFlow, Packt Publishing. He has also published research in international journals and conferences.
Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.
"About the title" may belong to another edition of this title.
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