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Data Science Book

The Data Science eBook is free for DataScienceCentral members. Click here to get your copy. Membership is free.

 

Updated content as of June 6, 2012. See below for an outdated HTML version; the PDF version is of much higher quality, proofread and with the most recent content.  Click here to preview 2nd Edition.

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 i...
  20. Comprehensive list of Excel errors, inaccuracies and use of non-sta...
  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 b...
  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 inte...
  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. Analysts Explore Cloud Analytics at Gartner Business Intelligence Summit 2012
  13. One Third of Organizations Plan to Use Cloud Offerings to Augment BI Capabilities
  14. Twenty Questions about Big Data and Data Sciences
  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

 

While the book is not yet finished, we wanted to share with you the 86 pages that we have written so far. The reasons are as follows:

  • We want your feedback about the style and content.
  • We want to attract sponsors and affiliates to submit contributions. 

Affiliates submit an 2-3 pages article, in exchange for making the book available for download on their website. Contributing to the book will drive traffic to your blog or website, as clickable links are available throughout the book, and can be added in your article as well.We are looking for authors to submit contributions to Part I or Part II.

So far, most articles are from Vincent Granville or Analyticbridge staff for the following reasons:

  • We own copyright for our own articles
  • We don't have to spend much time to select and review our own articles
  • We know that the links associated with our contributions are permanent, making our book more robust

However, we would love to include contributions from external authors in Part I and II, and contributions from sponsors (e.g. vendors) and affiliates in Part I, II and III. So feel free to send articles for possible inclusion. You can check and download the "book in progress" by clicking on the link below: 

About the book:

Our Data Science e-Book provides recipes, intriguing discussions and resources for data scientists and executives or decision makers. You don't need an advanced degree to understand the concepts. Most of the material is written in simple English, however it offers simple, better and patentable solutions to many modern business problems, especially about how to leverage big data.

Emphasis is on providing high-level information that executives can easily understand, while being detailed enough so that data scientists can easily implement our proposed solutions. Unlike most other data science books, we do not favor any specific analytic method nor any particular programming language: we stay one level above practical implementations. But we do provide recommendations about which methods to use when necessary.

Most of the material is original, and  can be used to develop better systems, derive patents or write scientific articles. We also provide several rules of the thumbs and details about craftsmanship used to avoid traditional pitfalls when working with data sets. The book also contains interviews with analytic leaders, and material about what should be included in a business analytics curriculum, or about how to efficiently optimize a search to fill an analytic position.

Among the more technical contributions, you will find notes on

  • How to determine the number of clusters
  • How to implement a system to detect plagiarism
  • How to build an ad relevancy algorithm
  • What is a data dictionary, and how to use it
  • Tutorial on how to design successful stock trading strategies
  • New fast and efficient random number generator
  • How to detect patterns vs. randomness

The book has three parts:

  • Part I: Data science recipes
  • Part II: Data science discussions
  • Part III: Data science resources

Part I and II mostly consist of the best Analyticbridge posts by Dr. Vincent Granville, founder of Analyticbridge. Part III consists of sponsored vendor contributions as well as contributions by organizations (affiliates offering software, conferences, training, books, etc.) who make our free e-book available for download on their web site. To become a sponsor or affiliate, please contact us at [email protected]

 

The Data Science eBook is free for DataScienceCentral members. Click here to get your copy. Membership is free.

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