《Python:数据分析全流程指南》是一本全面介绍如何使用Python进行数据处理和分析的专业书籍。适合希望提升数据分析技能的数据科学家与工程师阅读。
Python: End-to-end Data Analysis by Phuong Vothihong
This book, published on May 31, 2017 and available in AZW3 format with a file size of 27.07 MB, teaches you how to leverage the power of Python for data analysis.
About This Book:
- Clean, format, explore your data using popular libraries like Pandas and NumPy.
- Analyze large datasets; create attractive visualizations; manipulate various data types using SciPy and matplotlib.
- Gain advanced computational skills for analyzing complex data through numerous examples.
Who This Book Is For:
This course is ideal for developers, analysts, and data scientists who are new to the field or want a solid foundation in Python-based data analysis. A basic understanding of Python programming is recommended alongside an eagerness to work with your data.
What You Will Learn:
- Understand the significance of data analysis and master its processing steps.
- Clean and transform your data using advanced statistical techniques for creating visualizations.
- Analyze images, time series data, text, social networks, web scraping, databases (including Hadoop and Spark).
- Use statistical models to discover patterns in your datasets.
- Detect similarities or differences within your dataset through clustering methods.
In Detail:
Data analysis involves applying logical reasoning to study each component of the systems data. Python is a versatile language that has become one of the leading languages for data science due to its extensive range of tools and libraries suitable for all purposes. This course aims at helping you master effective approaches towards solving complex data analysis problems in Python.
The book begins by introducing fundamental concepts along with supported libraries like matplotlib, NumPy, pandas etc., then progresses into creating visualizations using different color maps, shapes, sizes, palettes before moving onto statistical data analysis techniques such as distribution algorithms and correlations. It also covers handling numerical issues alongside Spark and HDFS setup for web mining.
You will be able to perform sorting, reduction, subsequent analyses quickly while appreciating how these methods support business decision-making processes. Advanced topics include performing regression analysis, quantifying cause-effect relationships using Bayesian methods, discovering supervised machine learning techniques in Python’s toolbox.
The course concludes with a comprehensive guide and reference material enabling you to analyze data at varying complexity levels turning it into actionable insights specific to this course but also applicable elsewhere.
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