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Data Science Problems We Solve

  • ESTIMATION—Assessing the values and distributions of uncertain variables:
    • Where is the communications satellite right now?
    • How far is the user from the nearest branch when they deposit a check via a mobile app?
  • NATURAL LANGUAGE PROCESSING—Giving context to unstructured text
    • Is the term "woot" positive or negative the way it is used in a particular sentence?
    • Is the term "crash" being used as a noun or a verb?
  • RANKING—Ordering items by preference:
    • Which battery vendor do I go to first, second, third?
    • Which users should get a “try this new feature” offer?
  • CHARACTERIZATION—Drawing conclusions from data:
    • Why did the brake system fail?
    • Why are users uninstalling the app?
  • CLUSTERING—Grouping items by similarity/discovering similarities:
    • Do different knee replacement patients fall into certain recovery classes?
    • Do users with similar app-interaction patterns also share demographic patterns?
  • RELATIONSHIP DISCOVERY—Finding correlations and cause-effect relations:
    • Did the recent feature release increase the user interaction time with the app?
    • What is the relationship between annual rainfall and bushels of soybeans per acre?
  • VISUALIZATION—Exposing the salient aspects of complex data in simple pictures:
    • How do we show the key features of networks that have a half million links?
    • How do we picture the uncertainty in the aggregate data about user attrition?
  • PLANNING AND SCHEDULING—Determining the best actions to take:
    • How to distribute trolley cars to get tourists to the big comic book convention?
    • What steps to take in what order and at what times to minimize user attrition?
  • OPTIMIZATION—Determining the best choice among a set of possibilities:
    • How do we maximize satisfaction with the user experience?
    • What fuel mixture minimizes the overall cost of fleet vehicle operation?

S3 Data Science
S3 Data Science, copyright 2015.