Why L-3 Data Tactics Data Science?


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Why L-3 Data Tactics Data Science?

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Why L-3 Data Tactics Data Science?

  1. 1. L-3 Data Tactics Data Science: Why DT Data Science? ! asymptotically approaching perfect
  2. 2. Data Science Team The Team: (Geoffrey B., Nathan D., Rich H., Keegan H., David P., Ted P., Shrayes R., Robert R., Jonathan T., Adam VE., Max W.) ! Graduates from top universities… …many of whom are EMC Data Science Certified. ! Advanced degrees include: mathematics, computer science, astrophysics, electrical engineering, mechanical engineering, statistics, social sciences. ! Base competencies (horizontals): clustering, association rules, regression, naive bayesian classifier, decision trees, time-series, text analysis. ! Going beyond the base (verticals)...
  3. 3. Horizontals and Verticals Clustering || Regression || Decision Trees || Text Analysis Association Rules || Naive Bayesian Classifier || Time Series Analysis econom etrics spatialeconom etrics graph theory algorithm s astrophysicaltim e-series analysis path planning algorithm s bayesian statistics constrained optim izations num ericalintegration techniques PCA bagging/boosting hierarchicalm odels IRT space-tim e latentclass analysis structuralequation m odeling m ixture m odels SVM m axent CART autoregressive m odels ICA factoranalysis random forest dim ensionalreduction topic m odels sentim entanalysis frequency dom ain patterns unsupervised by supervised change-pointm odels LUBAP DLISA
  4. 4. Team Design Centralized Structure: Decentralized Structure: Hybrid Structure (L-3 DT DS Team): + 1 Standardized processes + 1 Strategic goals met - 1 costumer goals not met + 1 costumers goals met - 1 NO Strategic goals met - 1 Inconsistent & redundant + 1 standardized processes + 1 Strategic goals met + 1 costumers goals met
  5. 5. Tool-makers Hierarchy of Data Scientists
  6. 6. Data Science = “Order from Chaos”
  7. 7. Data Science = “Order from Chaos”
  8. 8. Why Data Science [Business]??? Why are analytics important? (Business, Analytics, Practical) ! ! "We need to stop reinventing the cloud and start using it!" (Dave Boyd) ! Using the cloud = doing data science ! !
  9. 9. Why are analytics important? (Business, Analytics, Practical) Analytics: ! No Free Lunch (NFL) theorems: no algorithm performs better than any other when their performance is averaged uniformly over all possible problems of a particular type. Algorithms must be designed for a particular domain or style of problem, and that there is no such thing as a general purpose algorithm. ! Meaning you need tool-makers! Not tool users! ! ! Why Data Science [Data Science]???
  10. 10. If this guy doesn’t scale - none of us do. We need data science. Data Scales Web Scales Academic Publications Scale IC Scales N t t Why Data Science [Practical]??? N=Amount of data; t=time
  11. 11. Big Data needs Data Science, but Data Science does not need Big Data. We excel with Big and Small Data. ! BIG DATA, small data - it doesn’t really matter. Big P vs. Big N vs. small n vs. small p N: records P: columns (variables) ! ...it doesn’t matter cause data size alone is not enough to find vagaries in data: Generalization = Data + Knowledge. Data = rough + smooth Philosophy:
  12. 12. DT Data Science Ethos: “We are Data Dogmatic!” ! We are NOT “Data Agnostic” ...this should represent an early warning system about any corporate culture claiming to “do” data science. ! The IT notion of data is dead.
  13. 13. Data Science Perspective... http://datatactics.blogspot.com/2013/07/analytics-in-perspective-inquiry-into.html Analytics in Perspective: An Inquiry into Modes of Inquiry
  14. 14. “Analytics in Perspective” reflects how people arrive at decisions. ! GOOD: Induction, Abduction, Circumscription, Counterfactuals. ! BAD: Deduction, Speculation, Justification, Groupthink ! ! ! Data Science Perspective...
  15. 15. What can dogs teach us about data science? Dogs and Data Science: Just as there are odors that dogs can smell and we cannot, as well as sounds that dogs can hear and we cannot, so too there are wavelengths of light we cannot see and flavors we cannot taste. Why then, given our brains wired the way they are, does the remark "Perhaps there are thoughts we cannot think," surprise you? Evolution, so far, may possibly have blocked us from being able to think in some directions; there could be unthinkable thoughts. ! The point is; analysts have biases and self- schemas that may preclude them from asking certain questions of data and thinking in certain directions. Data Science is about allowing data to speak and communicate in novel ways.
  16. 16. Data Science for Government (DS4G) DS4G 4 Everyone! - Train everyone! ! Created and delivered by practitioners of Data Science! ! FREE! ! July 28th @ 11am - 3:30pm; followed by L-3 Data Tactics Quarterly Data Science Brown Bag (4pm - 5:30pm).
  17. 17. Data Science for Government (DS4G) Data Science for Government An L-3 Event July 28, 2014 ! Introduction by Will Grannis Vice President and Chief Technology Officer, L-3 National Security Solutions ! Organized by Richard Heimann Chief Data Scientist, L-3 National Security Solutions ! ! Speakers: Nathan Danneman: Nathan’s background is in political science, with specializations in applied statistics and international conflict. He finished his PhD in June of 2013, and joined Data Tactics in May of that same year. He recently co-authored Social Media Mining with R, is active in the local Data Science community and currently supports DARPA. Nathan is also EMC Data Science Certified.   Richard Heimann: Richard’s background is in quantitative geography with specializations in spatial statistics and spatially explicit theory. He currently leads the Data Science Team at L-3 NSS and is adjunct faculty at UMBC and an instructor at GMU teaching related topics. He recently co-authored Social Media Mining with R and formerly supported DARPA. Richard is also EMC Data Science Certified.  ! Theodore Procita: Ted is an information technologist with ten years experience embracing open-source technology to build large-scale parallel processing systems for data manipulation and analysis. He's supported government customers in research at NRL and DARPA along with members of the IC. Ted is also EMC Data Science Certified.  ! Shrayes Ramesh: Shrayes’s background is in economics and statistics. Shrayes completed his undergraduate degree at University of Virginia in cognitive science and his PhD at University of Maryland, in 2012. Shrayes joined the Data Tactics team in July 2013 and currently supports DARPA. He is a former instructor of the EMC Data Science course and is himself EMC Data Science Certified.  ! Max Watson: Max’s background is in physics and applied mathematics. Max completed his undergraduate degree at University of California, Berkeley and completed his PhD at University of California, Santa Barbara in 2012. Max specializes in large-scale simulations, signal analysis and statistical physics - he joined the Data Tactics team in January 2014 and has supported DHS. Max is also EMC Data Science Certified. 
  18. 18. Thank you... Questions? Email us! Homepage: http://www.data-tactics.com Blog: http://datatactics.blogspot.com Twitter: https://twitter.com/DataTactics Or, me (Rich Heimann) at rheimann@data-tactics-corp.com