Getting Started with Python Data Analysis
Phuong Vo.T.H
Venduto da PBShop.store UK, Fairford, GLOS, Regno Unito
Venditore AbeBooks dal 11 giugno 1999
Nuovi - Brossura
Condizione: Nuovo
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Aggiungere al carrelloVenduto da PBShop.store UK, Fairford, GLOS, Regno Unito
Venditore AbeBooks dal 11 giugno 1999
Condizione: Nuovo
Quantità: Più di 20 disponibili
Aggiungere al carrelloNew Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Codice articolo L0-9781785285110
Learn to use powerful Python libraries for effective data processing and analysis
If you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.
Data analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.
With this book, we will get you started with Python data analysis and show you what its advantages are.
The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.
Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.
Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn.
This is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.
Phuong Vo.T.H
Phuong Vo.T.H has a MSc degree in computer science, which is related to machine learning. After graduation, she continued to work in some companies as a data scientist. She has experience in analyzing users' behavior and building recommendation systems based on users' web histories. She loves to read machine learning and mathematics algorithm books, as well as data analysis articles.
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