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Ivan Idris - Python Data Analysis [2014, PDF/EPUB/MOBI, ENG]

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Post 02-May-2016 18:00


Python Data Analysis
Год издания: 2014
Автор: Ivan Idris
Жанр или тематика: Программирование
Издательство: Packt Publishing
ISBN: 9781783553358
Язык: Английский
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 348
Описание: Python is a multi-paradigm programming language well suited for both object-oriented application development as well as functional design patterns. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. It will give you velocity and promote high productivity.
This book will teach novices about data analysis with Python in the broadest sense possible, covering everything from data retrieval, cleaning, manipulation, visualization, and storage to complex analysis and modeling. It focuses on a plethora of open source Python modules such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikit-learn, and NLTK. In later chapters, the book covers topics such as data visualization, signal processing, and time-series analysis, databases, predictive analytics and machine learning. This book will turn you into an ace data analyst in no time.
What You Will Learn
- Install open source Python modules on various platforms
- Get to know about the fundamentals of NumPy including arrays
- Manipulate data with pandas
- Retrieve, process, store, and visualize data
- Understand signal processing and time-series data analysis
- Work with relational and NoSQL databases
- Discover more about data modeling and machine learning
- Get to grips with interoperability and cloud computing

Примеры страниц


Table of Contents
1: Getting Started with Python Libraries
2: NumPy Arrays
3: Statistics and Linear Algebra
4: pandas Primer
5: Retrieving, Processing, and Storing Data
6: Data Visualization
7: Signal Processing and Time Series
8: Working with Databases
9: Analyzing Textual Data and Social Media
10: Predictive Analytics and Machine Learning
11: Environments Outside the Python Ecosystem and Cloud Computing
12: Performance Tuning, Profiling, and Concurrency
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