Python电子书“End-to-end Data Analysis”已提供azw3格式下载。

简介：
Python: End-to-end Data Analysis by Phuong VothihongEnglish | 31 May 2017 | ASIN: B072M6868D | 1321 Pages | AZW3 | 27.07 MB This resource leverages the capabilities of Python to facilitate a comprehensive approach to data analysis, encompassing tasks such as cleaning, extraction, in-depth analysis, and the creation of insightful visualizations. The book details how to effectively utilize Pythons powerful libraries for these processes. About This Book: This publication guides readers through the meticulous process of refining, formatting, and exploring their datasets, extracting valuable insights through the application of popular Python libraries. It demonstrates how to handle substantial data sets, generate visually compelling representations, manipulate diverse data types utilizing NumPy, SciPy, and matplotlib – and more. The content is replete with readily understandable examples designed to cultivate advanced computational skills essential for analyzing complex datasets. Who This Book Is For: This course is specifically targeted toward developers, analysts, and data scientists who are seeking to establish a foundational understanding of data analysis from its very beginning. It’s designed to provide a robust base upon which to analyze data exhibiting varying degrees of complexity. A preliminary familiarity with Python programming (along with a genuine enthusiasm for working with data) is recommended as a prerequisite for successfully navigating this material. What You Will Learn: Readers will gain a thorough comprehension of the significance of data analysis and master the fundamental steps involved in its processing. They will develop confidence in utilizing Python alongside its associated data analysis libraries – including Pandas, NumPy, and SciPy – effectively. Furthermore, they will learn techniques for cleaning and transforming their data while applying sophisticated statistical analyses to produce aesthetically pleasing visualizations. The curriculum also explores methods for analyzing image and time series data; uncovering patterns within text and social networks; conducting web scraping operations; and working with various databases like Hadoop and Spark. The course further equips participants with the ability to employ statistical models for identifying patterns within data; detecting similarities and differences through clustering techniques; and utilizing Jupyter Notebooks to generate publication-worthy figures suitable for incorporation into reports. In Detail: Data analysis fundamentally involves employing logical reasoning combined with analytical skills to systematically examine each component within a given system’s dataset. Python stands out as a versatile multi-domain programming language offering an extensive array of tools and libraries applicable across numerous domains – it has steadily gained prominence as a primary language in the field of data science. Have you ever considered developing expertise in effectively addressing challenges related to data analysis—resolving them efficiently—and extracting all available information from your datasets? If so, this course represents an ideal resource for achieving that goal! Within this course’s structure, we will initiate your journey into Python-based data analysis by introducing core concepts related to both data analysis itself and the supporting Python libraries such as matplotlib, NumPy, and pandas. Youll learn how to create impactful visualizations by carefully selecting color maps, various shapes and sizes alongside palettes - subsequently delving into statistical data analysis using distribution algorithms alongside correlation analyses. You’ll gain proficiency in tackling diverse problems involving both raw data and numerical information; familiarize yourself with technologies like Spark and HDFS; plus establish migration scripts specifically tailored for web mining endeavors. Participants will swiftly acquire the ability to perform practical sorting operations efficiently; implement dimensionality reduction techniques effectively; then proceed with subsequent analyses – all while gaining a deep appreciation for how established methodologies within the field of data analysis can powerfully support strategic business decision-making processes. Finally ,the course delves into advanced methodologies such as performing regression analyses ,quantifying cause-and-effect relationships using Bayesian methods ,and exploring how Python’s tools can be leveraged for supervised machine learning applications . The course provides access to highly practical content that elucidates various aspects of performing data analysis using Python—drawing upon material presented in several Packt publications including Getting Started with Python Data Analysis, Python Data Analysis Cookbook, and “Mastering Python Data Analysis.” By completing this comprehensive course you will acquire all necessary knowledge enabling you analyze your datasets regardless of their complexity level—transforming them into actionable insights that drive informed decision making . Style & Approach: The course emphasizes learning through engaging examples coupled with enjoyable exercises—employing an approachable style that prioritizes hands-on experience via a “learn-by-doing” methodology . It offers an effective framework specifically tailored towards analyzing datasets relevant within this particular course but also adaptable across any other type of dataset encountered . Designed as both a guide providing direction alongside a valuable reference tool ,this course aims at assisting users in progressing beyond fundamental concepts pertaining directly towards conducting effective analyses .