 |
|
Data Science for Business: What you need to know about data mining and data-analytic thinking
Foster Provost
Paperback. O'Reilly Media 2013-08-19.
ISBN 9781449361327
|
|
|
|
Hitta bokens lägsta pris
|
Förlagets beskrivning
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization-and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you're to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates
Fler böcker av Foster Provost
Liknande böcker
Recensioner
Den här boken har tyvärr inte några recensioner ännu. Om du redan läst boken, skriv en recension!
Recensera boken
Skriv en recension och dela dina åsikter med andra. Försök att fokusera på bokens innehåll. Läs våra instruktioner för mer information.
Data Science for Business: What you need to know about data mining and data-analytic thinking
Bokrecensioner » Data Science for Business: What you need to know about data mining and data-analytic thinking
|
|
 |
|
|
|
|
