Python: 提供端到端的数据分析电子书下载。

简介：
Python: End-to-end Data Analysis by Phuong VothihongEnglish | 31 May 2017 | ASIN: B072M6868D | 1321 Pages | AZW3 | 27.07 MB Leverage the capabilities of Python to thoroughly cleanse, extract, analyze, and visually represent your data. This comprehensive resource delves into the intricacies of data examination, offering a robust pathway for both novice and experienced practitioners. About This Book: This book equips readers with the skills to meticulously clean, format, and explore their datasets, utilizing widely adopted Python libraries to unlock valuable insights. It guides users through the handling of substantial data sets, the creation of compelling visualizations, and the manipulation and processing of diverse data types leveraging tools like NumPy, SciPy, and matplotlib. Furthermore, it provides a wealth of easily understandable examples designed to foster advanced computational skills essential for analyzing complex datasets. Who This Book Is For: This course is specifically tailored for developers, analysts, and data scientists who are eager to establish a foundational understanding of data analysis from the ground up. The curriculum is structured to provide a solid base for analyzing data with varying degrees of complexity. A preliminary familiarity with Python programming (along with a genuine enthusiasm for working with data) is recommended as a prerequisite. What You Will Learn: Throughout this course, you will gain a deep appreciation for the significance of data analysis and master its core processing procedures. You’ll become proficient in utilizing Python and its associated data analysis libraries – including Pandas, NumPy, and SciPy – to effectively handle your datasets. The curriculum progresses through techniques such as cleaning and transforming your data while applying sophisticated statistical analysis methods to generate visually appealing representations. Youll also explore methods for analyzing image and time series data alongside mining text information and examining social networks. Furthermore, you will learn how to perform web scraping operations alongside working with various databases like Hadoop and Spark. The course incorporates statistical models designed to reveal patterns within your data; it also covers identifying similarities and differences using clustering techniques. Finally, you’ll utilize Jupyter Notebooks to produce publication-worthy figures suitable for inclusion in reports. In Detail: Data analysis fundamentally involves applying logical reasoning and analytical techniques to systematically investigate each component within a given system’s dataset. Python stands out as a versatile multi-domain programming language that offers an extensive array of tools and libraries suitable for a broad spectrum of applications; it has steadily gained prominence as one of the primary languages utilized within the field of data science. Have you ever considered developing expertise in effectively addressing challenges related to data analysis – resolving these problems efficiently while extracting maximum information from your datasets? If so, this course represents an ideal opportunity for you! Within this courses framework we will initiate your journey into Python-based data analysis by introducing fundamental concepts related to both general data analysis principles along with supported Python libraries such as matplotlib, NumPy, and pandas. You will learn how to create impactful visualizations by strategically selecting color maps—incorporating diverse shapes sizes—and palettes—while simultaneously delving into statistical analyses using distribution algorithms alongside correlations calculations.. Subsequently you will gain proficiency in tackling various numerical problems related to different types of datasets; furthermore youll acquire hands-on experience with Spark and HDFS environments; additionally we will cover setting up migration scripts specifically designed for web mining activities.. Through practical exercises you’ll develop rapid proficiency in performing accurate sorting operations along with dimensionality reduction techniques followed by comprehensive analytical procedures—ultimately fostering a full understanding regarding how effective utilization of established methodologies can significantly support informed business decision-making processes.. Finally we delve into more advanced approaches including performing regression analyses quantifying cause-and-effect relationships employing Bayesian methods—and exploring how Pythons powerful tools can be leveraged within supervised machine learning contexts.. The course provides highly practical content illustrating Pythons role in facilitating effective data analysis – starting from foundational concepts presented within Packt publications such as Getting Started with Python Data Analysis, Python Data Analysis Cookbook, and Mastering Python Data Analysis. By completing this course successfully you will accumulate all necessary knowledge required when analyzing diverse datasets exhibiting varying levels of complexity – ultimately transforming raw information into actionable insights that drive strategic outcomes.. Style & Approach: This course emphasizes learning through engaging examples coupled with interactive exercises designed to foster an enjoyable “learn-by-doing” approach . It offers an accessible method specifically tailored towards analyzing datasets relevant within this particular course but also adaptable across various other contexts . The curriculum is thoughtfully structured combining elements that serve both as guidance along with providing valuable reference material - enabling learners progress beyond basic introductory concepts toward more sophisticated analytical strategies .