Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data

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Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data
 

  • Author:Nathan George
  • Length: 620 pages
  • Edition: 1
  • Publisher: Packt Publishing
  • Publication Date: 2021-09-30
  • ISBN-10: 1801071977
  • ISBN-13: 9781801071970
  • Sales Rank: #6554959 (See Top 100 Books)
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  • Buy Print:Buy from amazon


    Book Description

    Learn to effectively manage data and execute data science projects from start to finish using Python

    Key Features

    • Understand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modeling
    • Build a strong data science foundation with the best data science tools available in Python
    • Add value to yourself, your organization, and society by extracting actionable insights from raw data

    Book Description

    Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.

    The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You’ll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.

    As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.

    By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.

    What you will learn

    • Use Python data science packages effectively
    • Clean and prepare data for data science work, including feature engineering and feature selection
    • Data modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted models
    • Evaluate model performance
    • Compare and understand different machine learning methods
    • Interact with Excel spreadsheets through Python
    • Create automated data science reports through Python
    • Get to grips with text analytics techniques

    Who this book is for

    The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.

    The book requires basic familiarity with Python. A “getting started with Python” section has been included to get complete novices up to speed.

    Table of Contents

    1. Introduction to Data Science
    2. Getting Started with Python
    3. SQL and Built-in File Handling Modules in Python
    4. Loading and Wrangling Data with Pandas and NumPy
    5. Exploratory Data Analysis and Visualization
    6. Data Wrangling Documents and Spreadsheets
    7. Web Scraping
    8. Probability, Distributions, and Sampling
    9. Statistical Testing for Data Science
    10. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction
    11. Machine Learning for Classification
    12. Evaluating Machine Learning Classification Models and Sampling for Classification
    13. Machine Learning with Regression

    (N.B. Please use the Look Inside option to see further chapters)

    中文:

    书名:Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data

    学习使用Python自始至终有效地管理数据和执行数据科学项目

    Key Features

    • 了解并使用Python语言中的数据科学工具,如专门的机器学习算法和统计建模
    • 使用Python中提供的最佳数据科学工具构建强大的数据科学基础
    • 通过从原始数据中提取可操作的见解,为您自己、您的组织和社会增值

    Book Description

    使用Python的实用数据科学向您传授核心数据科学概念,并提供真实世界和真实的示例,并加强您对数据准备和存储、统计学、概率论、机器学习和Python编程的基本和高级原理的掌握,帮助您为熟练掌握数据科学奠定坚实的基础。

    本书首先概述了基本的Python技能,然后介绍了基本的数据科学技术,然后详细解释了执行这些技术所需的Python代码。您将通过练习这些示例来理解代码。代码被分解成小块(一次几行或一个函数),以便进行深入讨论。

    在您的学习过程中,您将学习如何执行数据分析,同时探索关键的数据科学Python包的功能,包括Pandas、SciPy和SCRICIT-LEARN。最后,这本书涵盖了数据科学中的伦理和隐私问题,并建议了提高数据科学技能的资源,以及保持最新数据科学发展的方法。

    到本书结束时,您应该能够轻松地将Python用于基本的数据科学项目,并且应该具备在任何数据源上执行数据科学过程的技能。

    What you will learn

    • Use Python data science packages effectively
    • 为数据科学工作清理和准备数据,包括功能工程和功能选择
    • 数据建模,包括经典统计模型(如t-检验)和基本机器学习算法,如随机森林和增强模型
    • 评估模型性能
    • 比较和了解不同的机器学习方法
    • 通过Python与Excel电子表格进行交互
    • 通过Python创建自动化数据科学报告
    • Get to grips with text analytics techniques

    这本书是为谁而写的

    本书面向初学者,包括开始或即将开始数据科学、分析或相关课程(如学士、硕士、训练营、在线课程)的学生,想要学习新技能以在就业市场脱颖而出的应届大学毕业生,想要在Python语言中学习实践数据科学技术的专业人士,以及想要转行到数据科学领域的人。

    这本书要求对Python有基本的了解。为了让完全的新手能够快速掌握,我们还包括了一节《Python快速入门》。

    Table of Contents

    1. 数据科学导论
    2. Python快速入门
    3. SQL和Python语言中的内置文件处理模块
    4. 使用Pandas和NumPy加载和争用数据
    5. Exploratory Data Analysis and Visualization
    6. Data Wrangling Documents and Spreadsheets
    7. Web Scraping
    8. Probability, Distributions, and Sampling
    9. Statistical Testing for Data Science
    10. 为机器学习准备数据:特征选择、特征工程和降维
    11. Machine Learning for Classification
    12. 机器学习分类模型评价与分类抽样
    13. 带回归的机器学习

    (N.B. Please use the Look Inside option to see further chapters)

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