Hands-On Exploratory Data Analysis with Python
暫譯: 使用 Python 進行實作探索性資料分析

Kumar Mukhiya, Suresh, Ahmed, Usman

Hands-On Exploratory Data Analysis with Python
  • 出版商: Packt Publishing
  • 出版日期: 2020-03-30
  • 售價: $3,050
  • 貴賓價: 9.5 折 $2,898
  • 語言: 英文
  • 頁數: 352
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789537258
  • ISBN-13: 9781789537253
  • 相關分類: Python、程式語言、Data Science

    • 海外代購書籍(需單獨結帳)

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商品描述

Key Features

  • Understand the fundamental concepts of exploratory data analysis using Python
  • Find missing values in your data and identify the correlation between different variables
  • Practice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python package

Book Description

Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization.

You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence.

By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes.

What you will learn

  • Import, clean, and explore data to perform preliminary analysis using powerful Python packages
  • Identify and transform erroneous data using different data wrangling techniques
  • Explore the use of multiple regression to describe non-linear relationships
  • Discover hypothesis testing and explore techniques of time-series analysis
  • Understand and interpret results obtained from graphical analysis
  • Build, train, and optimize predictive models to estimate results
  • Perform complex EDA techniques on open source datasets

Who this book is for

This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.

商品描述(中文翻譯)

#### 主要特點

- 理解使用 Python 進行探索性資料分析的基本概念
- 找出資料中的缺失值並識別不同變數之間的相關性
- 使用 Matplotlib 和 Seaborn Python 套件練習圖形探索性分析技術

#### 書籍描述

探索性資料分析（Exploratory Data Analysis, EDA）是一種資料分析方法，涉及應用多種技術以獲取資料集的見解。本書將幫助您獲得 EDA 的主要支柱的實用知識——資料清理、資料準備、資料探索和資料視覺化。

您將從使用開源資料集進行 EDA 開始，並執行從簡單到進階的分析，將資料轉化為有意義的見解。接著，您將學習各種描述性統計技術，以描述資料的基本特徵，並進一步對時間序列資料進行 EDA。隨著進展，您將學習如何實施 EDA 技術以進行模型開發和評估，並建立預測模型以視覺化結果。使用 Python 進行資料分析，您將處理真實世界的資料集，理解資料，總結其特徵，並為商業智慧進行視覺化。

在本 EDA 書籍結束時，您將具備對任何資料集進行初步調查的技能，獲得資料的見解，使用視覺輔助工具呈現結果，並建立能正確預測未來結果的模型。

#### 您將學到什麼

- 匯入、清理和探索資料，以使用強大的 Python 套件進行初步分析
- 使用不同的資料整理技術識別和轉換錯誤資料
- 探索多元回歸的使用，以描述非線性關係
- 發現假設檢定並探索時間序列分析技術
- 理解和解釋從圖形分析中獲得的結果
- 建立、訓練和優化預測模型以估算結果
- 在開源資料集上執行複雜的 EDA 技術

#### 本書適合誰

本 EDA 書籍適合任何對資料分析感興趣的人，特別是學生、統計學家、資料分析師和資料科學家。本書中呈現的實用概念可以應用於各種學科，以增強決策過程中的資料分析和綜合能力。您只需具備 Python 程式設計和統計概念的基本知識，即可開始使用本書。

作者簡介

Suresh Kumar Mukhiya is a PhD candidate, currently affiliated to the Western Norway University of Applied Sciences (HVL). He is a big data enthusiast, specializing in Information Systems, Model-Driven Software Engineering, Big Data Analysis, Artificial Intelligence and Frontend development. He has completed a Masters in Information Systems from the Norwegian University of Science and Technology (NTNU, Norway) along with a thesis in processing mining. He also holds a bachelor's degree in computer science and information technology (BSc.CSIT) from Tribhuvan University, Nepal, where he was decorated with the Vice-Chancellor's Award for obtaining the highest score. He is a passionate photographer and a resilient traveler.

Usman Ahmed is a data scientist and Ph.D. candidate at Western Norway University of Applied Science (HVL). He has rich experience in building and scaling high-performance systems based on data mining, natural language processing, and machine learning. Usman's research interests are sequential data mining, heterogeneous computing, natural language processing, a recommendation system, and machine learning. He has completed a Master's of Science in computer science from Capital University of Science and Technology, Islamabad, Pakistan. Usman Ahmed was awarded Gold Medal in Bachelor of Computer Science from Heavy Industries Taxila Education City.

作者簡介(中文翻譯)

Suresh Kumar Mukhiya 是一名博士候選人，目前隸屬於西挪威應用科學大學 (HVL)。他是一位大數據愛好者，專注於資訊系統、模型驅動軟體工程、大數據分析、人工智慧和前端開發。他在挪威科技大學 (NTNU, Norway) 完成了資訊系統碩士學位，並撰寫了有關處理挖掘的論文。他還擁有尼泊爾特里布萬大學的計算機科學與資訊技術學士學位 (BSc.CSIT)，並因獲得最高分而獲得副校長獎。他是一位熱情的攝影師和堅韌的旅行者。

Usman Ahmed 是一名數據科學家及西挪威應用科學大學 (HVL) 的博士候選人。他在基於數據挖掘、自然語言處理和機器學習的高效能系統構建和擴展方面擁有豐富的經驗。Usman 的研究興趣包括序列數據挖掘、異構計算、自然語言處理、推薦系統和機器學習。他在巴基斯坦伊斯蘭堡的資本科技大學完成了計算機科學碩士學位。Usman Ahmed 在重工業塔基拉教育城獲得計算機科學學士金獎。

目錄大綱

  1. Exploratory Data Analysis Fundamentals
  2. Visual Aids for EDA
  3. EDA with Personal Email
  4. Data Transformation
  5. Descriptive Statistics
  6. Grouping Dataset
  7. Correlation
  8. Time Series Analysis
  9. Hypothesis Testing and Regression
  10. Model Development and Evaluation
  11. EDA on Wine Quality Data Analysis
  12. Appendix

目錄大綱(中文翻譯)


  1. Exploratory Data Analysis Fundamentals

  2. Visual Aids for EDA

  3. EDA with Personal Email

  4. Data Transformation

  5. Descriptive Statistics

  6. Grouping Dataset

  7. Correlation

  8. Time Series Analysis

  9. Hypothesis Testing and Regression

  10. Model Development and Evaluation

  11. EDA on Wine Quality Data Analysis

  12. Appendix

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