9 Data Mining Challenges From Data Scientists Like You

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9 data mining challenges from data scientists like you and how to overcome them with predictive modeling software

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9 Data Mining Challenges From Data Scientists Like You

  1. 1. 9 Data Mining Challenges from Data Scientists Like You
  2. 2. 1. Poor quality data
  3. 3. • • • • D r t y dat a i M ssi ng val ues i I nadequat e dat a si ze Poor r epr esent at i on i n dat a sam i ng pl
  4. 4. 2. Lack of understanding
  5. 5. Lack of understanding/lack of diffusion of data mining techniques in academic arenas
  6. 6. 3. Lack of good literature
  7. 7. Lack of good literature on important data mining topics and techniques
  8. 8. 4. (Academic) access to commercial-grade software.
  9. 9. (Academic institutions) have trouble accessing commercial-grade software at reasonable costs.
  10. 10. 5. Data variety
  11. 11. Data variety - trying to accommodate data that comes from different sources and in a variety of different forms (images, geo
  12. 12. 6. Data velocity
  13. 13. Data velocity - online machine learning requires models to be constantly updated with new, incoming data.
  14. 14. 7. Dealing with huge datasets
  15. 15. Dealing with huge datasets, or 'Big Data,' that require distributed approaches.
  16. 16. 8. Coming up with the right question
  17. 17. "More data beats the better algorithm, but smarter questions beat more data,“- Gregory Piatetsky, www.kdnuggets.com
  18. 18. 9. Remaining objective and allowing the data to lead you, not the opposite.
  19. 19. Remaining objective and allowing the data to lead you, not the opposite. Preconceived notions can be dangerous, but luckily it is in our power to resist them...
  20. 20. Interested in other articles on data mining topics and techniques? Check out the Salford Systems blog: http://1.salford-systems.com/blog

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