数据科学原理（影印版 英文版）

Preface
Chapter 1： How to Sound Like a Data Scientist
What is data science?
Basic terminology
Why data science?
Example - Sigma Technologies
The data science Venn diagram
The math
Example - spawner-recruit models
Computer programming
Why Python?
Python practices
Example of basic Python
Domain knowledge
Some more terminology
Data science case studies
Case study - automating government paper pushing
Fire all humans， right?
Case study - marketing dollars
Case study - what's in a job description?
Summary
Chapter 2： Types of Data
Flavors of data
Why look at these distinctions?
Structured versus unstructured data
Example of data preprocessing
Word/phrase counts
Presence of certain special characters
Relative length of text
Picking out topics
Quantitative versus qualitative data
Example - coffee shop data
Example - world alcohol consumption data
Digging deeper
The road thus far
The four levels of data
The nominal level
Mathematical operations allowed
Measures of center
What data is like at the nominal level
The ordinal level
Examples
Mathematical operations allowed
Measures of center
Quick recap and check
The interval level
Example
Mathematical operations allowed
Measures of center
Measures of variation
The ratio level
Examples
Measures of center
Problems with the ratio level
Data is in the eye of the beholder
Summary
Chapter 3： The Five Steps of Data Science
Introduction to Data Science
Overview of the five steps
Ask an interesting question
Obtain the data
Explore the data
Model the data
Communicate and visualize the results
Explore the data
Basic questions for data exploration
Dataset 1 - Yelp
Dataframes
Series
Exploration tips for qualitative data
Dataset 2 - titanic
Summary
Chapter 4： Basic Mathematics
Mathematics as a discipline
Basic symbols and terminology
Vectors and matrices
Quick exercises
Answers
Arithmetic symbols
Summation
Proportional
Dot product
Graphs
Logarithms/exponents
Set theory
Linear algebra
Matrix multiplication
How to multiply matrices
Summary
Chapter 5： Impossible or Improbable - A Gentle Introduction to Probability
Basic definitions
Probability
Bayesian versus Frequentist
Frequentist approach
The law of large numbers
Compound events
Conditional probability
The rules of probability
The addition rule
Mutual exclusivity
The multiplication rule
Independence
Complementary events
A bit deeper
Summary
Chapter 6： Advanced Probability
Collectively exhaustive events
Bayesian ideas revisited
Bayes theorem
More applications of Bayes theorem
Example - Titanic
Example - medical studies
Random variables
Discrete random variables
Types of discrete random variables
Summary
Chapter 7： Basic Statistics
What are statistics?
How do we obtain and sample data?
Obtaining data
Observational
Experimental
Sampling data
Probability sampling
Random sampling
Unequal probability sampling
How do we measure statistics?
Measures of center
Measures of variation
Definition
Example - employee salaries
Measures of relative standing
The insightful part - correlations in data
The Empirical rule
Summary
Chapter 8： Advanced Statistics
Point estimates
Sampling distributions
Confidence intervals
Hypothesis tests
Conducting a hypothesis test
One sample t-tests
Example of a one sample t-tests
Assumptions of the one sample t-tests
Type I and type II errors
Hypothesis test for categorical variables
Chi-square goodness of fit test
Chi-square test for association/independence
Summary
Chapter 9： Communicating Data
Why does communication matter?
Identifying effective and ineffective visualizations
Scatter plots
Line graphs
Bar charts
Histograms
Box plots
When graphs and statistics lie
Correlation versus causation
Simpson's paradox
If correlation doesn't imply causation， then what does?
Verbal communication
It's about telling a story
On the more formal side of things
The whylhowlwhat strategy of presenting
Summary
Chapter 10： How to Tell If Your Toaster Is Learning - Machine Learning Essentials
What is machine learning?
Machine learning isn't perfect
How does machine learning work?
Types of machine learning
Supervised learning
It's not only about predictions
Types of supervised learning
Data is in the eyes of the beholder
Unsupervised learning
Reinforcement learning
Overview of the types of machine learning
How does statistical modeling fit into all of this?
Linear regression
Adding more predictors
Regression metrics
Logistic regression
Probability， odds， and log odds
The math of logistic regression
Dummy variables
Summary
Chapter 11： Predictions Don't Grow on Trees - or Do They?
Na'fve Bayes classification
Decision trees
How does a computer build a regression tree?
How does a computer fit a classification tree?
Unsupervised learning
When to use unsupervised learning
K-means clustering
Illustrative example - data points
Illustrative example - beer！
Choosing an optimal number for K and cluster validation
The Silhouette Coefficient
Feature extraction and principal component analysis
Summary
Chapter 12： Beyond the Essentials
The bias variance tradeoff
Error due to bias
Error due to variance
Two extreme cases of bias/variance tradeoff
Underfitting
Overfitting
How bias/variance play into error functions
K folds cross-validation
Grid searching
Visualizing training error versus cross-validation error
Ensembling techniques
Random forests
Comparing Random forests with decision trees
Neural networks
Basic structure
Summary
Chapter 13： Case Studies
Case study 1 - predicting stock prices based on social media
Text sentiment analysis
Exploratory data analysis
Regression route
Classification route
Going beyond with this example
Case study 2 - why do some people cheat on their spouses?
Case study 3 - using tensorflow
Tensorflow and neural networks
Summary
Index