Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python (Paperback)

As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.

Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.

• Refine a question of interest to one that can be studied with data
• Pursue data collection that may involve text processing, web scraping, etc.
• Glean valuable insights about data through data cleaning, exploration, and visualization
• Learn how to use modeling to describe the data
• Generalize findings beyond the data

作為一名有抱負的資料科學家，您了解組織在重要決策中依賴數據的原因 - 無論是為了設計網站的公司、決定如何改進服務的城市，還是科學家發現如何阻止疾病傳播。您希望擁有將一堆雜亂的數據轉化為可行動洞察的技能。我們稱之為資料科學生命週期：從數據收集、整理、分析到得出結論的過程。

《學習資料科學》是第一本涵蓋編程和統計基礎技能的書籍，涵蓋了整個資料科學生命週期。它針對那些希望成為資料科學家或已經與資料科學家合作的人，以及希望跨越“技術/非技術”鴻溝的資料分析師。如果您具備基本的Python編程知識，您將學習如何使用像pandas這樣的行業標準工具來處理數據。

- 將感興趣的問題細化為可以用數據研究的問題
- 進行可能涉及文本處理、網絡抓取等的數據收集
- 通過數據清理、探索和可視化獲取有價值的洞察
- 學習如何使用建模來描述數據
- 將結果推廣到數據之外