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Statistics
Computer
Science
Domain
Expertise
Data
Science
Machine
Learnin
g
Drew Conway
Data Science
Machine Learning: Neural Network
4
Social models:
Segregation and peer
effects
Aggregation of numbers,
preferences, ?
Randomness, random
processes ,random walks
Cellular automata for social
and biological systems
Convergence and optimality
Probability
Social models:
Segregation and peer
effects
Decision making: rational
actor, behavioral, rule based
Numerical and categorical
data
Linear and nonlinear
Regression and
classification models
Emergence, tipping points,
contagion, SIS, diffusion,
percolation
Growth models, exponential
growth, Solow growth model
Problem solving,
perspectives and innovation
Heuristics
Problem solving and teams
Markov processes
Decision models
Decision models
Coordination and culture
Emergence of culture
Path dependence
Chaos
Increasing returns to scale
Network, structure, logic,
formation
Skills and luck
Game theory and Colonel
Blotto game
Decision models
Competition
Cooperation and collective
action
Prediction and diversity
The Many Model Thinker

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Topic0 f

  • 2.
  • 4. 4 Social models: Segregation and peer effects Aggregation of numbers, preferences, ? Randomness, random processes ,random walks Cellular automata for social and biological systems Convergence and optimality Probability Social models: Segregation and peer effects Decision making: rational actor, behavioral, rule based Numerical and categorical data Linear and nonlinear Regression and classification models Emergence, tipping points, contagion, SIS, diffusion, percolation Growth models, exponential growth, Solow growth model Problem solving, perspectives and innovation Heuristics Problem solving and teams Markov processes Decision models Decision models Coordination and culture Emergence of culture Path dependence Chaos Increasing returns to scale Network, structure, logic, formation Skills and luck Game theory and Colonel Blotto game Decision models Competition Cooperation and collective action Prediction and diversity The Many Model Thinker

Editor's Notes

  1. Data science: learn insights, understanding from data, not traditional data to models to science