Mastering the 80% of Analytics: What Data Scientists Really Do
•
0 likes•236 views
A brief overview of the tasks and workflow that take up as much as 80% of typical efforts by Data Scientists. Intended for an introductory audience.
Recommended
More Related Content
Viewers also liked
Similar to Mastering the 80% of Analytics: What Data Scientists Really Do
Similar to Mastering the 80% of Analytics: What Data Scientists Really Do (20)
Recently uploaded
Recently uploaded (20)
Mastering the 80% of Analytics: What Data Scientists Really Do
- 1. Mastering the 80% of Analytics What Data Scientists Really Do Mik, PhD @AvrioAnalytics
- 4. Caveat •Data Science is very broad •This is a particular perspective •Mathematician •Predictive algorithm developer •Very brief
- 5. A Day in the Life Wrangling Modeling Features Results
- 7. What is “Wrangling”? •Data: •Getting •Formatting •Cleaning Data Janitorial Work
- 8. Getting the Data •Myriad of sources
- 9. Getting the Data •Myriad of sources •Varying collection, storage and maintenance
- 10. Getting the Data •Myriad of sources •Varying collection, storage and maintenance •Most people just don’t care
- 11. Getting the Data •Myriad of sources •Varying collection, storage and maintenance •Most people just don’t care •At least not soon enough
- 12. Got it. Now what? •Structured: in a consistent and defined format
- 13. Got it. Now what? •Structured: in a consistent and defined format •Unstructured: no consistent format
- 14. Got it. Now what? •Structured: in a consistent and defined format •Unstructured: no consistent format •Text data
- 15. Got it. Now what? •Structured: in a consistent and defined format •Unstructured: no consistent format •Text data Movie Rating Star Wars 5 Stars I loved the new Star Wars, definitely 5/5 stars!
- 19. Formatting Time Username Views 12:30 jsmith 32 12:45 mik 27 1:00 dmartin 8 1:15 jsmith 46 Time Username Views 12:20 gwarren 12 12:30 lpeabody 53 12:40 dmartin 20 12:50 hjohnson 5
- 21. Data is Dirty Business •Duplicates
- 22. Data is Dirty Business •Duplicates •Missing values
- 23. Data is Dirty Business •Duplicates •Missing values •Ill-formed values
- 24. Data is Dirty Business •Duplicates •Missing values •Ill-formed values •Wrong values
- 25. Data is Dirty Business •Duplicates •Missing values •Ill-formed values •Wrong values Similar in effect
- 26. Data is Dirty Business •Duplicates •Missing values •Ill-formed values •Wrong values
- 27. Types of Missing-ness •MCAR: Missing Completely at Random
- 28. Types of Missing-ness •MCAR: Missing Completely at Random •MAR: Missing at Random
- 29. Types of Missing-ness •MCAR: Missing Completely at Random •MAR: Missing at Random •MNAR: Missing Not at Random
- 30. Types of Missing-ness •MCAR: Missing Completely at Random •MAR: Missing at Random •MNAR: Missing Not at Random Bad Worse
- 31. Dealing with Missing Data X Y Z 129 1 40 110 3 210 32 98 9 65
- 32. Dealing with Missing Data •Deletion X Y Z 129 1 40 110 3 210 32 98 9 65
- 33. Dealing with Missing Data •Deletion •Pairwise •Listwise X Y Z 129 1 40 110 3 210 32 98 9 65
- 34. Dealing with Missing Data •Deletion •Pairwise •Listwise X Y Z 129 1 40 110 3 210 32 98 9 65 X Z 129 40 210 32 Pairwise X Y Z 129 40 Listwise
- 35. Dealing with Missing Data •Imputation
- 36. Dealing with Missing Data •Imputation •Mean substitution •Regression
- 37. Dealing with Missing Data •Multiple Imputation
- 38. Dealing with Missing Data •Multiple Imputation •Stochastic simulation
- 39. Dealing with Missing Data •Multiple Imputation •Stochastic simulation •Must know distribution
- 42. Gotchas •Sampling Error •Statistical Power •Population Parameters
- 43. Gotchas •Sampling Error •Statistical Power •Population Parameters •Propagation
- 44. So what do I do? •Approaches vary quite a lot
- 45. So what do I do? •Approaches vary quite a lot •MCAR, MAR hard to prove
- 46. So what do I do? •Approaches vary quite a lot •MCAR, MAR hard to prove •Principle of Least Harm
- 47. 60% - 80% of Work
- 48. Cleaning Done! Now the fun! •Almost…
- 49. Cleaning Done! Now the fun! •Almost… •Clean data is still “raw”
- 50. Cleaning Done! Now the fun! •Almost… •Clean data is still “raw” •Features: pre-processed for modeling
- 51. Feature Engineering •A lot of data is useless
- 52. Feature Engineering •A lot of data is useless •Filter, slice, transform
- 53. Feature Engineering •A lot of data is useless •Filter, slice, transform •Singular idea: What’s the main driver?
- 55. Feature Engineering •Considerations •Relevance •Redundancy •Curse of Dimensionality
- 56. Feature Engineering Methods •PCA •Edge Detection •Blob Detection •Auto encoding •Kernel PCA •Partial Least Squares •Generalized Least Squares •Direct Modeling •Isomapping • Mutual Information Theory • Information Entropy Theory • ICA • MDR • Latent Factors • MPCA • LSA • Statistical Moments • Random Projections •De-Noising •Weighting •Patch Extraction •Functional Mapping •Discretization •Filtering •FFT •Smoothing •Density Mapping
- 58. Feature Engineering •It’s hard •Analysis + Domain knowledge
- 59. Feature Engineering •It’s hard •Analysis + Domain knowledge •…Deserves a presentation on its own
- 60. Feature Engineering •It’s hard •Analysis + Domain knowledge •…Deserves a presentation on its own •Features are input to machine learning
- 61. Now the fun stuff (finally) •ML: computer acts without explicit program
- 62. Now the fun stuff (finally) •ML: computer acts without explicit program •Utilizes empirical data to “teach” a process
- 63. Now the fun stuff (finally) •ML: computer acts without explicit program •Utilizes empirical data to “teach” a process •Pattern Rec. -> ML -> Deep Learning
- 64. Now the fun stuff (finally) •ML: computer acts without explicit program •Utilizes empirical data to “teach” a process •Pattern Rec. -> ML -> Deep Learning •Buzzwords abound
- 65. Now the fun stuff (finally) •ML: computer acts without explicit program •Utilizes empirical data to “teach” a process •Pattern Rec. -> ML -> Deep Learning •Buzzwords abound •Fairly simple, lots of libraries
- 66. ML Approaches •Classes of problems
- 67. ML Approaches •Classes of problems •Continuous (regression)
- 68. ML Approaches •Classes of problems •Continuous (regression) •Discrete (classification)
- 69. ML Approaches •Classes of problems •Continuous (regression) •Discrete (classification) •Classes of solutions
- 70. ML Approaches •Classes of problems •Continuous (regression) •Discrete (classification) •Classes of solutions •Supervised
- 71. ML Approaches •Classes of problems •Continuous (regression) •Discrete (classification) •Classes of solutions •Supervised •Unsupervised
- 73. ML Algorithms •Neural Networks •Genetic Algorithms
- 74. ML Algorithms •Neural Networks •Genetic Algorithms •Bayesian Classification
- 75. ML Algorithms •Neural Networks •Genetic Algorithms •Bayesian Classification •Support Vector Machines
- 76. ML Algorithms •Neural Networks •Genetic Algorithms •Bayesian Classification •Support Vector Machines •Many used as type of feature extraction
- 77. Neural Networks •Motivated by brain function •Neurons fire, activate paths •Non-linear •Simplest: Perceptron X1 X2 Logic Layer w1 w2
- 78. Neural Networks •Inputs feed neuron with weight
- 79. Neural Networks •Inputs feed neuron with weight •Logic Layer: activation function
- 80. Neural Networks •Inputs feed neuron with weight •Logic Layer: activation function •Fires (or not) based on inputs
- 81. Neural Networks •Inputs feed neuron with weight •Logic Layer: activation function •Fires (or not) based on inputs •Weights from minimizing cost function
- 82. Neural Networks •Inputs feed neuron with weight •Logic Layer: activation function •Fires (or not) based on inputs •Weights from minimizing cost function •Backpropagation
- 83. Sigmoid Logic Layer 0 0.25 0.5 0.75 1 -10 -8 -6 -4 -2 0 2 4 6 8 10 w = 1 w = 2 1 1 + e wT x
- 84. Neural Networks •Most networks are bigger X1 X2 A1 AM Y1 YK
- 85. Machine Learning •Got data, features and algorithm
- 86. Machine Learning •Got data, features and algorithm •Just plug in and profit!
- 87. Machine Learning •Got data, features and algorithm •Just plug in and profit! •Not quite
- 88. Machine Learning •Got data, features and algorithm •Just plug in and profit! •Not quite •Tuning and training
- 89. Tuning •What about N, M and K? X1 X2 A1 AM Y1 YK
- 90. Tuning •What about N, M and K? •Hyper-parameters X1 X2 A1 AM Y1 YK
- 91. Tuning •What about N, M and K? •Hyper-parameters •Size of layers, thresholds, etc. X1 X2 A1 AM Y1 YK
- 92. Tuning •What about N, M and K? •Hyper-parameters •Size of layers, thresholds, etc. •Static specifics of the algorithm X1 X2 A1 AM Y1 YK
- 93. Training •It’s all about the teaching
- 94. Training •It’s all about the teaching •Representative data set
- 95. Training •It’s all about the teaching •Representative data set •Large, clean
- 96. Training •Don’t teach to the test
- 97. Training •Don’t teach to the test •Causes overfitting
- 98. Training •Don’t teach to the test •Causes overfitting •Training (80%) and Testing (20%) data
- 99. Training •Don’t teach to the test •Causes overfitting •Training (80%) and Testing (20%) data •Cross-validation
- 100. With all the open source libraries, isn’t machine learning easy now?
- 101. I got results! •Why doesn’t anyone care?
- 102. I got results! •Why doesn’t anyone care? •Kaggle vs. Real Life Syndrome
- 103. I got results! •Why doesn’t anyone care? •Kaggle vs. Real Life Syndrome •It’s all in the presentation
- 104. It’s all in the presentation •Complex topic
- 105. It’s all in the presentation •Complex topic •Non-technical audience
- 106. It’s all in the presentation •Complex topic •Non-technical audience •Several stakeholders
- 107. It’s all in the presentation •Complex topic •Non-technical audience •Several stakeholders •Many likely skeptics
- 108. It’s all in the presentation •Avoid buzzwords
- 109. It’s all in the presentation •Avoid buzzwords •Focus on a business problem
- 110. It’s all in the presentation •Avoid buzzwords •Focus on a business problem •Show value
- 111. It’s all in the presentation •Avoid buzzwords •Focus on a business problem •Show value •Keep in mind cost
- 112. Is it actually science? •Sometimes
- 113. Is it actually science? •Sometimes •…but often not
- 114. Is it actually science? •Sometimes •…but often not •Data Sciences vs. Data Engineering
- 115. Is it actually science? •Sometimes •…but often not •Data Sciences vs. Data Engineering •It should be — focus on why
- 116. Is it actually science? Applied Math Computer Science Domain Expertise
- 117. Is it actually science? Applied Math Computer Science Domain Expertise Applied Math Computer Science Physics Physicist
- 118. Why Data Science? •Big problems, fun challenges
- 119. Why Data Science? •Big problems, fun challenges •Both science and business
- 120. Why Data Science? •Big problems, fun challenges •Both science and business •Consistently awesome
- 121. 2012: Sexiest Job of the Century 2016: Best Job of the Year 2016: Hottest Job of the Year 2016: Best Career Opportunity
- 123. So want to get started? •Theano
- 124. So want to get started? •Theano •TensorFlow
- 125. So want to get started? •Theano •TensorFlow •Torch
- 126. So want to get started? •Theano •TensorFlow •Torch •Pandas