想真正學會數(shù)據(jù)科學,你不僅要掌握工具——數(shù)據(jù)科學庫、框架、模塊和工具包——還要理解它們背后的思想和原理。更新的《數(shù)據(jù)科學入門》第2版為你展示了這些工具和算法是如何從零開始實現(xiàn)的。
如果你具備數(shù)學能力和一些編程技能,作者Joel Grus將會幫你熟悉數(shù)據(jù)科學相關的核心數(shù)學和統(tǒng)計學知識,以及作為一名數(shù)據(jù)科學家所需的黑客技巧。這本更新的書還包含了關于深度學習、統(tǒng)計學和自然語言處理的新資料,為你展示了如何在日常繁雜冗余的數(shù)據(jù)中找到寶石。
快速入門Python
學習線性代數(shù)、統(tǒng)計學和概率的基礎知識——以及它們在數(shù)據(jù)科學中的使用場景
收集、探索、清理、管理和操作數(shù)據(jù)
深入研究機器學習的基礎知識
實現(xiàn)k近鄰、樸素貝葉斯、線性回歸、邏輯回歸、決策樹、神經(jīng)網(wǎng)絡和聚類等模型
探索推薦系統(tǒng)、自然語言處理、網(wǎng)絡分析、MapReduce和數(shù)據(jù)庫知識
Preface to the Second Edition
Preface to the First Edition
1. Introduction
The Ascendance of Data
What Is Data Science?
Motivating Hypothetical: DataSciencester
Finding Key Connectors
Data Scientists You May Know
Salaries and Experience
Paid Accounts
Topics of Interest
Onward
2. A Crash Course in Python
The Zen of Python
Getting Python
Virtual Environments
Whitespace Formatting
Modules
Functions
Strings
Exceptions
Lists
Tuples
Dictionaries
defauhdict
Counters
Sets
Control Flow
Truthiness
Sorting
List Comprehensions
Automated Testing and assert
Object-Oriented Programming
Iterables and Generators
Randomness
Regular Expressions
Functional Programming
zip and Argument Unpacking
args and kwargs
Type Annotations
How to Write Type Annotations
Welcome to DataSciencester!
For Further Exploration
3. Visualizing Data
matploflib
Bar Charts
Line Charts
Scatterplots
For Further Exploration
4. Linear Algebra
……