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目录如下，值得阅读！！
Introduction
Part I - Data Science Recipes
1. New random number generator: simple, strong and fast
2. Lifetime value of an e-mail blast: much longer than you think
3. Two great ideas to create a much better search engine
4. Identifying the number of clusters: finally a solution
5. Online advertising: a solution to optimize ad relevancy
6. Example of architecture for AaaS (Analytics as a Service)
7. Why and how to build a data dictionary for big data sets
8. Hidden decision trees: a modern scoring methodology
9. Scorecards: Logistic, Ridge and Logic Regression
10. Iterative Algorithm for Linear Regression
11. Approximate Solutions to Linear Regression Problems
12. Theorems for Traders
13. Preserving metric and score consistency over time and across clients
14. Advertising: reach and frequency mathematical formulas
15. Real Life Example of Text Mining to Detect Fraudulent Buyers
16. Discount optimization problem in retail analytics
17. Sales forecasts: how to improve accuracy while simplifying models?
18. How could Amazon increase sales by redefining relevancy?
19. How to build simple, accurate, data-driven, model-free confidence intervals
20. Comprehensive list of Excel errors, inaccuracies and use of non-standard statistical definitions
21. 10+ Great Metrics and Strategies for Email Campaign Optimization
22. 10+ Great Metrics and Strategies for Fraud Detection
23. Case Study: Four different ways to solve a data science problem
24. Case Study: Email marketing - analytic tips to boost performance by 300%
25. Optimize keyword campaigns on Google in 7 days: an 11-step procedure
26. How do you estimate the proportion of bogus accounts on Facebook?
Part II - Data Science Discussions
1. Statisticians Have Large Role to Play in Web Analytics (AMSTAT interview)
2. Future of Web Analytics: Interview with Dr. Vincent Granville
3. Connecting with the Social Analytics Experts
4. Interesting note and questions on mathematical patents
5. Big data versus smart data: who will win?
6. Creativity vs. Analytics: Are These Two Skills Incompatible?
7. Barriers to hiring analytic people
8. Salary report for selected analytical job titles
9. Are we detailed-oriented or do we think "big picture", or both?
10. Why you should stay away from the stock market
11. Gartner Executive Programs' Worldwide Survey of More Than 2,300 CIOs
12. Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally...
13. One Third of Organizations Plan to Use Cloud Offerings to Augment BI Capabilities
14. Twenty Questions about Big Data and Data Sciences3
15. Interview with Drew Rockwell, CEO of Lavastorm
16. Can we use data science to measure distances to stars?
17. Eighteen questions about real time analytics
18. Can any data structure be represented by one-dimensional arrays?
19. Data visualization: example of a great, interactive chart
20. Data science jobs not requiring human interactions
21. Featured Data Scientist: Vincent Granville, Analytic Entrepreneur
22. Healthcare fraud detection still uses cave-man data mining techniques
23. Why are spam detection algorithms so terrible?
24. What is a Data Scientist?
25. Twenty seven types of data scientists: where do you fit?
Part III - Data Science Resources
1. Vincent’s list
2. History of 24 analytic companies over the last 30 years
3. Fifteen great data science articles from influential news outlets
4. List of publicly traded analytic companies
5. Thirty unusual applications of data sciences, analytics and big data
6. 50 unusual ways analytics are used to make our lives better
7. Berkeley course on Data Science
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