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- 1. The Artful Business of Data Mining Computational Statistics with Open Source ToolWednesday 20 March 13
- 2. David Coallier @davidcoallierWednesday 20 March 13
- 3. Data Scientist At Engine Yard (.com)Wednesday 20 March 13
- 4. Find DataWednesday 20 March 13
- 5. Clean DataWednesday 20 March 13
- 6. Analyse Data?Wednesday 20 March 13
- 7. Analyse DataWednesday 20 March 13
- 8. Question DataWednesday 20 March 13
- 9. Report FindingsWednesday 20 March 13
- 10. Data ScientistWednesday 20 March 13
- 11. Data JanitorWednesday 20 March 13
- 12. Actual TasksWednesday 20 March 13
- 13. “If your model is elegant, it’s probably wrong”Wednesday 20 March 13
- 14. “The Times they are a-Changing” — Bob DylanWednesday 20 March 13
- 15. Python & RWednesday 20 March 13
- 16. SciPy http://www.scipy.orgWednesday 20 March 13
- 17. scipy.statsWednesday 20 March 13
- 18. scipy.stats Descriptive StatisticsWednesday 20 March 13
- 19. from scipy.stats import describe s = [1,2,1,3,4,5] print describe(s)Wednesday 20 March 13
- 20. scipy.stats Probability DistributionsWednesday 20 March 13
- 21. Example Poisson DistributionWednesday 20 March 13
- 22. λ e k −k f (k; λ ) = k! for k >= 0Wednesday 20 March 13
- 23. import scipy.stats.poisson p = poisson.pmf([1,2,3,4,1,2,3], 2)Wednesday 20 March 13
- 24. print p.mean() print p.sum() ...Wednesday 20 March 13
- 25. NumPy http://www.numpy.org/Wednesday 20 March 13
- 26. NumPy Linear AlgebraWednesday 20 March 13
- 27. ⎛ 1 0 ⎞ ⎜ 0 1 ⎟ ⎝ ⎠Wednesday 20 March 13
- 28. import numpy as np x = np.array([ [1, 0], [0, 1] ]) vec, val = np.linalg.eig(x) np.linalg.eigvals(x)Wednesday 20 March 13
- 29. >>> np.linalg.eig(x) ( array([ 1., 1.]), array([ [ 1., 0.], [ 0., 1.] ]) )Wednesday 20 March 13
- 30. Matplotlib Python PlottingWednesday 20 March 13
- 31. statsmodels Advanced Statistics ModelingWednesday 20 March 13
- 32. NLTK Natural Language Tool KitWednesday 20 March 13
- 33. scikit-learn Machine LearningWednesday 20 March 13
- 34. from sklearn import tree X = [[0, 0], [1, 1]] Y = [0, 1] clf = tree.DecisionTreeClassifier() clf = clf.fit(X, Y) clf.predict([[2., 2.]]) >>> array([1])Wednesday 20 March 13
- 35. PyBrain ... Machine LearningWednesday 20 March 13
- 36. PyMC Bayesian InferenceWednesday 20 March 13
- 37. Pattern Web Mining for PythonWednesday 20 March 13
- 38. NetworkX Study NetworksWednesday 20 March 13
- 39. MILK MOAR machine LEARNING!Wednesday 20 March 13
- 40. Pandas easy-to-use data structuresWednesday 20 March 13
- 41. from pandas import * x = DataFrame([ {"age": 26}, {"age": 19}, {"age": 21}, {"age": 18} ]) print x[x[age] > 20].count() print x[x[age] > 20].mean()Wednesday 20 March 13
- 42. RWednesday 20 March 13
- 43. RStudio The IDEWednesday 20 March 13
- 44. lubridate and zoo Dealing with Dates...Wednesday 20 March 13
- 45. yy/mm/dd mm/dd/yy YYYY-mm-dd HH:MM:ss TZ yy-mm-dd 1363784094.513425 yy/mm different timezoneWednesday 20 March 13
- 46. reshape2 Reshape your DataWednesday 20 March 13
- 47. ggplot2 Visualise your DataWednesday 20 March 13
- 48. RCurl, RJSONIO Find more DataWednesday 20 March 13
- 49. HMisc Miscellaneous useful functionsWednesday 20 March 13
- 50. forecast Can you guess?Wednesday 20 March 13
- 51. garch And ruGarchWednesday 20 March 13
- 52. quantmod Statistical Financial TradingWednesday 20 March 13
- 53. xts Extensible Time SeriesWednesday 20 March 13
- 54. igraph Study NetworksWednesday 20 March 13
- 55. maptools Read & View MapsWednesday 20 March 13
- 56. map(state, region = c(row.names(USArrests)), col=cm.colors(16, 1)[ﬂoor(USArrests$Rape/max(USArrests$Rape)*28)], ﬁll=T)Wednesday 20 March 13
- 57. StorageWednesday 20 March 13
- 58. Oppose “big” DataWednesday 20 March 13
- 59. “Learn how to sample”Wednesday 20 March 13
- 60. ExperimentsWednesday 20 March 13
- 61. What Do You Want to Answer?Wednesday 20 March 13
- 62. Understand Your AudienceWednesday 20 March 13
- 63. Scientific ReportingWednesday 20 March 13
- 64. Busy-ness Time is moneyWednesday 20 March 13
- 65. Public VisualisationWednesday 20 March 13
- 66. Best Visualisation, Bad DataWednesday 20 March 13
- 67. Best Forecasting models... Bad VisualisationWednesday 20 March 13
- 68. Wednesday 20 March 13
- 69. Wednesday 20 March 13
- 70. SeanchaíWednesday 20 March 13
- 71. Wednesday 20 March 13
- 72. FeelitWednesday 20 March 13
- 73. Wednesday 20 March 13
- 74. Wednesday 20 March 13
- 75. Wednesday 20 March 13
- 76. “Don’t be scared of bar charts.”Wednesday 20 March 13
- 77. Mathematical Statistics Engineering Business Economics CuriosityWednesday 20 March 13
- 78. davidcoallier.github.com @davidcoallier on TwitterWednesday 20 March 13

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