Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value
暫譯: Python 數據科學基礎：收集 - 組織 - 探索 - 預測 - 價值

Dmitry Zinoviev

Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value

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商品描述

Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python.

Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data.

This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume.

Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option.

What You Need:

You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.

商品描述(中文翻譯)

從儲存在 SQL 和 NoSQL 資料庫中的雜亂無章、無結構的資料，轉變為整齊、組織良好的資料集，這本快速參考手冊專為忙碌的資料科學家而設。了解文本挖掘、機器學習和網路分析；使用 NumPy 和 Pandas 模組處理數值資料；使用統計和網路理論方法描述和分析資料；並查看實際的資料分析範例。這個一站式解決方案涵蓋了您在 Python 中所需的基本資料科學知識。

資料科學是學術研究、學生註冊和就業中增長最快的學科之一。Python 以其靈活性和可擴展性，迅速超越 R 語言，成為資料科學專案的首選。透過這本模組化的快速參考手冊，隨時掌握 Python 資料科學的概念，了解用於獲取、清理、分析和儲存資料的工具。

這個一站式解決方案涵蓋了基本的 Python、資料庫、網路分析、自然語言處理、機器學習的要素以及視覺化。從本地檔案、資料庫和互聯網訪問結構化和非結構化的文本和數值資料。整理、重新排列和清理資料。處理關聯和非關聯資料庫、資料視覺化以及簡單的預測分析（回歸、聚類和決策樹）。了解典型的資料分析問題是如何處理的。並嘗試自己解決各種中型專案，這些專案既有趣又能增添您的履歷。

無論您是學生、從 R 轉向 Python 的初級資料科學專業人士，還是希望不必記住每個函數和選項的資深 Python 開發者，都可以隨時攜帶這本方便的快速指南。

您需要的：

您需要一個合適的 Python 3.3 或以上版本的發行版，至少包含 NLTK、Pandas、NumPy、Matplotlib、Networkx、SciKit-Learn 和 BeautifulSoup。滿足這些要求的優秀發行版是 Anaconda，您可以從 www.continuum.io 免費獲得。如果您計劃設置自己的資料庫伺服器，還需要 MySQL (www.mysql.com) 和 MongoDB (www.mongodb.com)。這兩個套件都是免費的，並且可以在 Windows、Linux 和 Mac OS 上運行。

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