Analytics Brownbag


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Analytics Brownbag

  1. 1. DT Brown Bag: A Primer in Analytics WELCOME! R2 = 500; p<marty’s 1mile time asymptotically approaching perfect Thursday, August 22, 13
  2. 2. Outline •EAT, Guten Appetit, Bon appetit, Buen apetito, Buon appetito! •Words from the VP •Why this brown-bag? •Analytics Services: •Team Introduction; About YOU! •Why Analytics!? •Philosophy... •Case Studies: •Case Study (Nathan D.) •Localview (Marty A.) •Case Study (me) •Core Values: Analytical Insights •On the horizon... Thursday, August 22, 13
  3. 3. Why this brown bag?? Learning [close] at a pace similar to the pace at which we learn. Learning and Educating from/to PMs, SWE, and OPs. PM: Provide insights from FRIs/RFPs. PM: Atmospherics from our costumers. SWE: Accessing data spaces. SWE: Integrating algorithms. OP: How do you best consume the outputs of models? OP: What models are best to present to OPs? PM: Program Managers, SWE: Software Engineers, OP: Operators Thursday, August 22, 13
  4. 4. Why this brown bag?? ISW %...% USMA Thursday, August 22, 13 DARPA
  5. 5. Data Tactics Analytics Practice The Team: (Nathan D., Shrayes R., David P., Adam VE., Andrew T., Geoffrey B., Rich H.) Graduates from top universities... Degrees include: mathematics, computer science, aeronautical engineering, astrophysics, electrical engineering, mechanical engineering, statistics, social science(s). Base competencies (horizontals): Clustering, Association Rules, Regression, Naive Bayesian Classifier, Decision Trees, Time-Series, Text Analysis. Going beyond the base (verticals)... Thursday, August 22, 13
  6. 6. Data Tactics Analytics Practice ABOUT YOU: 28 confirmed, 18 webex, 14 tentative (n:60 represent > 25% of the company) 21 confirmed within the first 60 minutes.... Monsee Wood & Steve Moccio 1st Charles Fuller & Lenesto Page Last Chris Zilligen: 3,120 (Longest resume) Catherine Schymanski: 284 (shortest resume) Linguistic Standard: Jack Gustafson (FK: -126) Shrayes Ramesh (FK: -38) team below the company average!! :) Thursday, August 22, 13
  7. 7. th ni n an pl al ur ct ru st eq RT CA ua g ra alg tio in n or ed m ith op od ec m tim eli s on ng iza om tio et n ric sp PC s s at A ial ec n DL on aiv IS om e b A ay et ba e hi rics s c ye er las sia ar sifi n ch st er ica as at tro ist l ics mo ph de ICA ys ls lat ica gr en lt ap im tc h elas th se s eo rie an ry s aly alg an nu sis or aly m ith sis er m ica s l in m te IR ixt SV gr T ur M at e io m n G LM tec ode m ax ls hn en iq t ue s ns t co pa Horizontals & Verticals Clustering || Regression || Decision Trees || Text Analysis Association Rules || Naive Bayesian Classifier || Time Series Analysis Thursday, August 22, 13
  8. 8. Sc ics at rip m tin g he at Sk M il s Data Tactics Analytics Practice & & ing m m gr a ~s Da DT Analytics l na itio rch ad a Tr ese R ta nge tis r tic Zo ul ne at ! io n Pr o ics ist at St ML Domain Expertise [1] Statisticulation “How to Lie with Statistics” Darrell Huff [2] [3] Thursday, August 22, 13
  9. 9. Why Analytics [Business]??? Why are analytics important? (Business, Analytics, Practical) "We need to stop reinventing the cloud and start using it!" (Dave Boyd) Thursday, August 22, 13
  10. 10. Why Analytics [Analytics]??? 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. Thursday, August 22, 13
  11. 11. Why Analytics [Practical]??? Academic Publications Scale Data Scales N Web Scales IC Scales t Marty doesn’t scale - none of us do. Thursday, August 22, 13 t
  12. 12. Why Analytics [Practical]??? Why are analytics important? (Business, Analytics, Practical) “…the alternative to good statistics is not “no statistics,” it’s bad statistics. People who argue against statistical reasoning often end up backing up their arguments with whatever numbers they have at their command, over- or under-adjusting in their eagerness to avoid anything systematic” Bill James Thursday, August 22, 13
  13. 13. Philosophy: "companies that have massive amounts of data without massive amounts of clue are going to be displaced by startups that have less data but more clue" (Tim O’Reilly) Thursday, August 22, 13
  14. 14. Philosophy: We are NOT “Data Agnostic” ...this should represent an early warning system about our culture. The IT notion of data is dead. Thursday, August 22, 13
  15. 15. Analytics in Perspective... Analytics in Perspective: An Inquiry into Modes of Inquiry Thursday, August 22, 13
  16. 16. Analytics in Perspective... “Analytics in Perspective” reflects how people arrive at decisions. GOOD: Induction, Abduction, Circumscription, Counterfactuals. BAD: Deduction, Speculation, Justification, Groupthink Thursday, August 22, 13
  17. 17. Identifying Smugglers Leveraging Big Spatio-Temporal Data Thursday, August 22, 13
  18. 18. Background: The Strait of Hormuz Importance: • Oil • Embargo • Smuggling Thursday, August 22, 13
  19. 19. How to Catch Smugglers In order to stop smugglers, we must identify: 1. 2. 3. Which boats are undertaking illicit activities Where illicit activities are taking place Points of departure/arrival of suspicious ships Thursday, August 22, 13
  20. 20. A Difficult Task: Too Much Data AIS (transponder) provides ship-level data: • Ship location (lat-long) • Ship speed • Ship bearing • Ship “purpose” • Time stamp About 0.5M pings from 1,300 boats between March 2012 and January 2013. Thursday, August 22, 13
  21. 21. A Difficult Task: Too Much Data Thursday, August 22, 13
  22. 22. A Difficult Task: Too Little Data Individual pings or tracks not useful: no point of comparison Similarly, small duration plots are too thin to provide analytic leverage. Thursday, August 22, 13
  23. 23. . A Difficult Task: Too Little Data A single boat: Thursday, August 22, 13
  24. 24. . A Difficult Task: Too Little Data A single day: Thursday, August 22, 13
  25. 25. A Difficult Task: Many Types of Boats Thursday, August 22, 13
  26. 26. Solution: Analytics Use a statistical model to discover patterns in the data… …then identify observations (boat-times) that do not fit those patterns. Goal: Identify boats, place, and times that exhibit or house discrepant behavior. Thursday, August 22, 13
  27. 27. Characteristics of a Good Model A good model for this data should: • Leverage all of the available data • Take advantage of local information (not global patterns) • Be able to accommodate a variety of patterns (shipping, fishing, etc) • Be able to identify ships that are only occasionally deviant • Identify place-times where deviant activity occurs • Be estimable with reasonable computational resources Thursday, August 22, 13
  28. 28. The Model A local, unsupervised-as-supervised learning, bagged, probability model. A LUBaP model? Thursday, August 22, 13
  29. 29. The Model A local, unsupervised-as-supervised learning, bagged, probability model. We want to compare apples-to-apples; that is, treat nearby (spatio-temporally) boats the same, don't compare them to far-flung ones. Assign each observation to a geographically constrained grid square. Thursday, August 22, 13
  30. 30. The Model A local, unsupervised-as-supervised learning, bagged, probability model. Thursday, August 22, 13
  31. 31. The Model A local, unsupervised-as-supervised learning, bagged, probability model. Let m denote the number of observations in a particular grid square. Then, in each square, add m additional observations with the following characteristics: •position, drawn from bivariate uniform distribution •speed, drawn with replacement from empirical distribution •time of observation, drawn from a uniform distribution Now, the task is no longer unsupervised, but supervised. ->Model the probability of a boat being a ``real'' boat. Thursday, August 22, 13
  32. 32. The Model Thursday, August 22, 13
  33. 33. The Model Thursday, August 22, 13
  34. 34. The Model A local, unsupervised-as-supervised learning, bagged, probability model. •Turned outlier detection, a poorly structured problem, into modeling a binary target, a very well-understood problem •Now, simply model the probability that each boat is “real” •Apply logistic regression to each grid square •Allow the flexibility (order) of the model fit (splines, interactions) to depend on the data density in each square (more data, richer model). •logit(“real”) = f(speed, location, time) Thursday, August 22, 13
  35. 35. The Model A local, unsupervised-as-supervised learning, bagged, probability model. Problem: Predictions may be arbitrary due to random assignment and grid coarseness. Thursday, August 22, 13
  36. 36. The Model A local, unsupervised-as-supervised learning, bagged, probability model. Problem: Predictions may be arbitrary due to random assignment and grid coarseness. Solution: 1. Create multiple grids with different positions. 2. Re-run the local model in each square, for each different grid. 3. Aggregate the predicted probabilities for each observation, in each grid, by averaging. Thursday, August 22, 13
  37. 37. Computational Efficiency Estimating a flexible model in each of ~300 grid squares, for each of 6 grids, means estimating ~1,800 logistic models! Not a problem, because: • each one has limited amounts of data (most algorithms take exponentially longer as a function of data size) • each local model is separate, allowing for parallel processing Computation on my laptop takes ~4 minutes after simple parallelization across cores. Thursday, August 22, 13
  38. 38. What is the Output from this Model? •Predicted probability of each boat-time (i.e. observation) being a real boat. •High probabilities indicate observations doing something “normal” or “predictable.” •Low probabilities indicate observations doing something “discrepant.” Ship ID Lat Long Speed Timestamp Pr 623432 24.546 55.005 9.8 1203221230 0.78 874627 24.716 55.108 12.4 1209242230 0.08 523881 25.128 54.807 4.2 1206120947 0.64 Thursday, August 22, 13
  39. 39. Value I: Location of Illicit Activities Thursday, August 22, 13
  40. 40. Value II: Identify Devious Boats Thursday, August 22, 13
  41. 41. Value III: Prioritized List of Suspect Boats •Model generates probabilities on an interval scale •Facilitates efficient use of scarce enforcement resources Thursday, August 22, 13
  42. 42. Lessons Learned Analytics is a powerful tool for identifying patterns in big data. Identifying outliers is predicated on identifying patterns. LUBaP models are a powerful tool for outlier detection. This model utilizes no subject matter expertise and a simple probability model (implications: portable across domains; fast) Thursday, August 22, 13
  43. 43. What’s the Next Hot Thing? Unsupervised Scaling of Text Data Thursday, August 22, 13
  44. 44. Analyzing Text is Important The preponderance of data created today is free text, not structured numerical data. One thing people want to do with text is “scale” it; that is, rank order it according to an underlying continuum. Examples: -put a numerical value on what each product reviewer thinks of a particular product -generate a measure of the extremism of Iranian clerics based on their writings Thursday, August 22, 13
  45. 45. Analyzing Text is Difficult Text data is unstructured, and messy. “I thought I would love the iPhone, but it’s actually not that great.” Standard approaches: 1. Dictionary: Create a numeric value for many content-laden words; compare texts to the dictionary. 2. Estimation: Hand-score many texts; use the scores as a basis for training a statistical model for other texts. Thursday, August 22, 13
  46. 46. A New Approach Each author’s use of a word implies they “support” that word, as opposed to words they don’t use. The model, developed for scaling ideological positions of legislators from votes, can be applied to word use. Benefits: 1: No dictionary! 2: Language invariant! Thursday, August 22, 13
  47. 47. Preliminary Example Pulled down 2000 tweets, 1000 each with the hashtags #prolife and #prochoice. Drop the hashtags (no cheating!), pre-process the text data, and run the model. Thursday, August 22, 13
  48. 48. Output Thursday, August 22, 13
  49. 49. Output Thursday, August 22, 13
  50. 50. Output Thursday, August 22, 13
  51. 51. Local Events, Worldwide Impact Thursday, August 22, 13
  52. 52. Localview © Localview also known as “Lv”, is a Cloud/Web based proprietary Dashboard with an advanced analytics framework – the desired end state is an integrated data mining, knowledge discovery and pattern recognition of social and spatial pattering. Lv will provide end-users with globally and locally available historical information as well as globally and locally available real-time social media data feed. This service includes; news, on the spot statistics using a proprietary Data Tactics Tool called “ZoomStat”, historical facts, social media, economics, security, military, infrastructure, health, aid, natural disasters, war, entertainment, weather, transportation, and travel. All results will be analyzed, ingested, normalized, and then plotted on a dynamic and interactive global map. Thursday, August 22, 13
  53. 53. the numbers   7 volunteered & part time team members (NO OVERHEAD) first DEMO delivered in  832 86 days hours of research & development time Thursday, August 22, 13
  54. 54. The Team: The Team Marty A backend development frontend development data analysis development Joe A Joon K Annie W Dave P Rich H Shenoa H Thursday, August 22, 13
  55. 55. Evolution: Thursday, August 22, 13
  56. 56. Evolution: Thursday, August 22, 13
  57. 57. Development Process Lv Development Process Thursday, August 22, 13
  58. 58. End-Users: Law Enforcement IC & DoD Thursday, August 22, 13 Commercial
  59. 59. Directional Space Time Analytics Base-Rate Fallacy Thursday, August 22, 13
  60. 60. Directional Space Time Analytics Data Tactics has been working on a set of problems that require considered solutions. The following method compares distributions at two points in time, with a particular focus on changes in the overall morphology of the distribution as well as mobility of individual observations within the distribution over that same period of time and contextually accounting for neighborhood effects. These dynamics are illuminating and communicate time and explicitly account for underlying spatial dimension (Wy). Based on the integration of a dynamic local space-time together with direction statistics these methods provide insights on the role of spatial dependence and uncontrolled variance over time and space. Thursday, August 22, 13
  61. 61. Directional Space Time Analytics This analysis demonstrates the utility of directional space time analytics on regional stability distribution dynamics. Drawing on recent advances in geovisualization [1], we suggest a spatially explicit view of mobility. Based on the integration of a dynamic local indicator of spatial association together with directional statistics and mapped data points to each observation, this framework provides new insights on the role of spatial dependence in regional stability and change. These approaches have been illustrated with state level incomes in the U.S. (1969-2008), Gross Domestic Product (1960 - 2011) Failed State Index (2010 - 2012), and GMTI data (t0, t1). [1] Murray, A. T., Liu, Y., Rey, S. J., and Anselin, L. (2010). Exploring movement object patterns. Thursday, August 22, 13
  62. 62. Gross Domestic Product Per Capita Gross Domestic Product A measure of the total output of a country that takes the gross domestic product (GDP) and divides it by the number of people in the country. The per capita GDP is especially useful when comparing one country to another because it shows the relative performance of the countries. A rise in per capita GDP signals growth in the economy and tends to translate as an increase in productivity. GDP is widely used by economists to gauge economic recession and recovery and an economy's general monetary ability to address externalities. It is not meant to measure externalities. It serves as a general metric for a nominal monetary standard of living and is not adjusted for costs of living within a region. GDP = private consumption + gross investment + government spending + (exports − imports), or Thursday, August 22, 13
  63. 63. GDP per. Capita Time Span: 1960 to 2011 (51 temporal bin(s), 1 year intervals): 2000 to 2011 (12 temporal bin(s), 1 year intervals); Spatial Area: Global; Original Sample: 202 obs; Data processing: imputation; Pruned Sample: 145 observations; Method: Directional Local Indicator of Spatial Autocorrelation (Moran’s I) with space-time classifications of High-high (Hh), high-High, Low-Low (LL), High Low (HL), Low-High (LH); Spatial Weights: knn4; Thursday, August 22, 13
  64. 64. Directional Space Time Analytics > describe(dlisa$yr2000) > describe(dlisa$yr2011) V. Name n mean sd median mad min max range skew kurtosis yr2000 145 5759 9534 1491 1831 87 46453 46366 2.12 3.72 yr2011 145 13292 20621 4666 5841 231 114232 114001 2.46 6.54 Thursday, August 22, 13
  65. 65. Directional Space Time Analytics Thursday, August 22, 13
  66. 66. Directional Space Time Analytics 2000:2011 (12 temporal bin(s), 1 year intervals); Thursday, August 22, 13
  67. 67. Directional Space Time Analytics What is wrong with Vermont ? [1] - Seemingly nothing! - Lies within head of approximately normal distribution - Not an outlier in a classical statistical sense - Vermont remains below the US average but is closing the gap. [1] State Median Income Thursday, August 22, 13
  68. 68. State Median Income Time Span: 1969 to 2008 (40 temporal bin(s), 1 year intervals) Spatial Area: Contiguous United States; Original Sample: 48 obs; Method: Directional Local Indicator of Spatial Autocorrelation (Moran’s I) with space-time classifications of High-high (Hh), high-High, Low-Low (LL), High Low (HL), Low-High (LH); Spatial Weights: Rook Contiguity; Thursday, August 22, 13
  69. 69. Directional Space Time Analytics 1969:2008 (40 temporal bin(s), 1 year intervals) Thursday, August 22, 13
  70. 70. Directional Space Time Analytics 1969:2008 (40 temporal bin(s), 1 year intervals) Thursday, August 22, 13
  71. 71. Directional Space Time Analytics 1969:2008 (40 temporal bin(s), 1 year intervals) Thursday, August 22, 13
  72. 72. Directional Space Time Analytics Thursday, August 22, 13
  73. 73. Core Values: Localview as an ecosystem: Most existing big data analyses of social media are confined to a single platform. However, most of the topics of interest to such studies, such as influence or information flow can rarely be confined to the Internet, let alone to a single platform. Understandable difficulty in obtaining high-quality multi-platform data does not mean that we can treat a single platform as a closed and insular system, as if human information flows were all gases in a chamber. “Shapes of stories into computers...” Kurt Vonnegut Nate Silver - Cognition2; Small Multiples; Tukey vs. Tufte Thursday, August 22, 13
  74. 74. Core Values: Open-source software where possible.  -Bigger data means bigger cost. -Scientific Python and R Computing Language reached maturity years ago. Data = Rough + Smooth Qualities Rough = impulsive, spiky signal: outliers; Smooth = pervasive Leverage analytics to help understand patterns in data as well as outliers - so called rough and smooth elements of data. The “smooth” and the “rough” patterns in data are informative, depending on the specific questions customers have. Local, as opposed to global or whole-map statistics: We believe that micro-level, local patterns are often of key interest, and can be obscured or distorted by attempts to fit global models to local data.  Analytical Pluralism: Mutli-method approaches dominate single-method approaches.  Rather than craft a single statistical model to answer a customer question, we attack problems from several angles simultaneously, deriving insights from areas of overlap and divergence in the pattern of findings. Methodological pathways: Blend nomothetic and idiographic approaches. Thursday, August 22, 13
  75. 75. Core Values: Thursday, August 22, 13
  76. 76. Analytical Resources: Thursday, August 22, 13
  77. 77. Analytical Resources: Thursday, August 22, 13
  78. 78. Analytical Resources: Thursday, August 22, 13
  79. 79. ...on the horizon. ...On the Horizon: DT & USMA Department of Systems Engineering partner together and leverage the Advanced Individual Academic Development Program. Rstudio:; PostgreSQL: Port: 5432 Thursday, August 22, 13
  80. 80. Data Tactics & US Military Academy: A Prime in Microfinance using KIVA Understanding the complex nature of microfinance more completely: The US military is directly involved in microfinance (Iraq & Afghanistan), working primarily through Provincial Reconstruction Teams (PRTs).  Funded by the DoD and DoS; the operational requirements of these agencies create  a need to demonstrate quick impact on economic recovery and therefore the goal is to report high numbers of loans.  Technical complexities separate this data from other datasets: Heterogeneous forms: structured/unstructured/nominal,ordinal, quantitative/temporal/ geographic/multi-lingual/multiple relationships(lenders to recipients) - multiple sectors/ missing data. Data cleansing is hard! Big Data(ish): $420M (USD), 1.1 million lenders, 580,000 loans, 250 partners, 4.1M transactions, 3 WHOLE GBs. ( Broad appeal: ...government to defense to finance to banking to non-profit organizations to THE POOR. Rstudio:; PostgreSQL: Port: 5432 Thursday, August 22, 13
  81. 81. ...on the horizon. ...On the Horizon: DT & The Institute for the Study of War will collaborate in a balanced but largely quantitative approach to analyzing revolutions and the role social media plays with particular focus on the Iraq Spring. Thursday, August 22, 13
  82. 82. ...on the horizon. ...on the Horizon: Data Science for Program Managers (late September / early October) Analytics Brown Bag Volume II (October / Early November) Thursday, August 22, 13
  83. 83. Thank you... Questions? Homepage: Blog: Twitter: Or, me (Rich Heimann) at 83 Thursday, August 22, 13