Principles of Data Science

Sinan Ozdemir

Principles of Data Science

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商品描述

Key Features

  • Enhance your knowledge of coding with data science theory for practical insight into data science and analysis
  • More than just a math class, learn how to perform real-world data science tasks with R and Python
  • Create actionable insights and transform raw data into tangible value

Book Description

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking―and answering―complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas.

With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.

What you will learn

  • Get to know the five most important steps of data science
  • Use your data intelligently and learn how to handle it with care
  • Bridge the gap between mathematics and programming
  • Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results
  • Build and evaluate baseline machine learning models
  • Explore the most effective metrics to determine the success of your machine learning models
  • Create data visualizations that communicate actionable insights
  • Read and apply machine learning concepts to your problems and make actual predictions

About the Author

Sinan Ozdemir is a data scientist, startup founder, and educator living in the San Francisco Bay Area with his dog, Charlie; cat, Euclid; and bearded dragon, Fiero. He spent his academic career studying pure mathematics at Johns Hopkins University before transitioning to education. He spent several years conducting lectures on data science at Johns Hopkins University and at the General Assembly before founding his own start-up, Legion Analytics, which uses artificial intelligence and data science to power enterprise sales teams.

After completing the Fellowship at the Y Combinator accelerator, Sinan has spent most of his days working on his fast-growing company, while creating educational material for data science.

Table of Contents

  1. How to Sound Like a Data Scientist
  2. Types of Data
  3. The Five Steps of Data Science
  4. Basic Mathematics
  5. Impossible or Improbable – A Gentle Introduction to Probability
  6. Advanced Probability
  7. Basic Statistics
  8. Advanced Statistics
  9. Communicating Data
  10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
  11. Predictions Don't Grow on Trees – or Do They?
  12. Beyond the Essentials
  13. Case Studies

商品描述(中文翻譯)

主要特點



  • 通過數據科學理論提升編碼知識，以實際洞察力進行數據科學和分析

  • 不僅僅是數學課程，還學習如何使用R和Python執行真實世界的數據科學任務

  • 創建可行的見解，將原始數據轉化為有形價值

書籍描述


需要將編程技能轉化為有效的數據科學技能嗎？《數據科學原理》旨在幫助您將數學、編程和業務分析相結合。通過這本書，您將對於將抽象和原始統計數據轉化為可行想法感到自信，並能夠提出並回答複雜而複雜的問題。


這本書以一種獨特的方法將數學和計算機科學相結合，帶您穿越整個數據科學流程。從清理和準備數據，到有效的數據挖掘策略和技術，您將逐步建立一個全面的數據科學拼圖。學習計算數學和統計學的基礎知識，以及當今數據科學家和分析師正在使用的一些偽代碼。您將熟悉機器學習，了解幫助您掌握和處理即使是最複雜數據集的統計模型，並了解如何創建能夠傳達數據含義的強大可視化效果。

您將學到什麼



  • 了解數據科學的五個最重要步驟

  • 聰明地使用您的數據，並學習如何小心處理它

  • 彌補數學和編程之間的差距

  • 了解概率、微積分以及如何使用統計模型來控制和清理數據並獲得可行結果

  • 構建和評估基準機器學習模型

  • 探索最有效的指標來確定機器學習模型的成功

  • 創建能夠傳達可行見解的數據可視化效果

  • 閱讀並應用機器學習概念解決問題並進行實際預測

關於作者


Sinan Ozdemir 是一位數據科學家、初創公司創始人和教育家，與他的狗Charlie、貓Euclid和有鬍子的蜥蜴Fiero一起生活在舊金山灣區。他在約翰霍普金斯大學攻讀純數學，然後轉向教育。在創辦自己的初創公司Legion Analytics之前，他在約翰霍普金斯大學和General Assembly進行了數年的數據科學講座，該公司利用人工智能和數據科學為企業銷售團隊提供動力。


在完成Y Combinator加速器的研究生課程後，Sinan大部分時間都在忙於發展自己快速增長的公司，同時為數據科學創建教育材料。

目錄



  1. 如何像數據科學家一樣說話

  2. 數據類型

  3. 數據科學的五個步驟

  4. 基礎數學

  5. 不可能或不太可能-概率的簡介

  6. 高級概率

  7. 基礎統計學

  8. 高級統計學

  9. 傳達數據

  10. 如何判斷您的烤麵包機是否在學習-機器學習基礎知識

  11. 預測不是從樹上長出來的-還是長出來了？

  12. 超越基礎知識

  13. 案例研究

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