Pythonによるデータサイエンスの基礎<br>Foundations of Data Science with Python (Chapman & Hall/crc the Python Series)

個数:

Pythonによるデータサイエンスの基礎
Foundations of Data Science with Python (Chapman & Hall/crc the Python Series)

  • 提携先の海外書籍取次会社に在庫がございます。通常3週間で発送いたします。
    重要ご説明事項
    1. 納期遅延や、ご入手不能となる場合が若干ございます。
    2. 複数冊ご注文の場合は、ご注文数量が揃ってからまとめて発送いたします。
    3. 美品のご指定は承りかねます。

    ●3Dセキュア導入とクレジットカードによるお支払いについて
  • 【入荷遅延について】
    世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
    おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。
  • ◆画像の表紙や帯等は実物とは異なる場合があります。
  • ◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
    また、洋書販売価格は、ご注文確定時点での日本円価格となります。
    ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。
  • 製本 Hardcover:ハードカバー版/ページ数 464 p.
  • 言語 ENG
  • 商品コード 9781032346748
  • DDC分類 006.76

Full Description

Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.

This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.

Key Features:

Applies a modern, computational approach to working with data
Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues
Teaches the fundamentals of some of the most important tools in the Python data-science stack
Provides a basic, but rigorous, introduction to Probability and its application to Statistics
Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material

Contents

1. Introduction 2. First Simulations, Visualizations, and Statistical Tests 3. First Visualizations and Statistical Tests with Real Data 4. Introduction to Probability 5. Null Hypothesis Tests 6. Conditional Probability, Dependence, and Independence 7. Introduction to Bayesian Methods 8. Random Variables 9. Expected Value, Parameter Estimation, and Hypothesis Tests on Sample Means 10. Decision Making with Observations from Continuous Distributions 11. Categorical Data, Tests for Dependence, and Goodness of Fit for Discrete Distributions 12. Multidimensional Data: Vector Moments and Linear Regression 13. Working with Dependent Data in Multiple Dimensions

最近チェックした商品