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MIS Reading Notes 1-14-16

by: Rebecca Jackson

MIS Reading Notes 1-14-16 MIS 0855-004

Rebecca Jackson
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About this Document

Notes on the two readings assigned for January 14, 2016.
Data Science
Laurel Miller
Class Notes
MIS, data science, laurel miller




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This 3 page Class Notes was uploaded by Rebecca Jackson on Saturday February 6, 2016. The Class Notes belongs to MIS 0855-004 at Temple University taught by Laurel Miller in Spring 2016. Since its upload, it has received 8 views. For similar materials see Data Science in Business, management at Temple University.

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Date Created: 02/06/16
Data Science and Prediction  Predictive Modeling  Science: a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions (data science is not statistics)  Huge amounts of unstructured raw data, tagged for computers to interpret and make decisions of  Looking for predictive patterns in massive amounts of data, rather than looking for data to satisfy a pattern  Predictively actionable  Simple theories are useless but common b/c they are likely to have predictive accuracy; explanations are weak, bold but falsifiable better.  Implications o Storage almost free: everything is stored in case it is useful (videos, social media messages) o 1980s/90s became popular, software developed to use data for prediction o Machine learning picks up subtle structure in data, but also “noise”; no assumptions made on relationship o Skips the assumptive theories; gathers patterns without guessing an explanation o Helpful when there are many possibilities (is it x, is it y?) o Aggregated: gathered. To find patterns aggregate data even if it is exaggerating similarities o From data, learning algorithm finds pattern o But would that pattern hold up predictively? o Oddly specific patterns helpful (health industry warnings) o In large varied data, difficult to imagine patterns. Simpler is better if its more predictive o Computers can test for predictive accuracy across large amounts of data o Causation can be found but background of data must be known and all variables involved should be included  Skills o Machine learning, test processing/mining, XML tagging (unstructured data) o 3 Basic Skills  Statistics, econometrics, despite their basis on fitting data to linear models  Computer science, systems skills, computer languages, despite limited to moderate data  Correlation and Causation, causal desirable but tricky to get o Ability to formulate problems in a way that leads to efective solutions; isomorphism, identical underlying structure o “Unbalanced target classes” – dependent variable is interesting only sometimes, difficult to predict o “computational thinking” – problem formulation skills o Shift to data-driven decision making o Computer>Human: cost, scalability and accuracy  Knowledge Discovery o No longer need to settle for models or theories; let machine learning get it all right  Can science & models exist without theories?  “concept drift: - models get worse over time, predictive data is updated  Biases unless intended for finding causality  Good for where we can be inaccurate; NOT PHYSICS. Earth/social science  Social very inaccurate, incomplete, come up with causality & gather data but same data can = two diferent theories easily  Even if cause unknown, predictive accuracy can save lives (health), find cause later  Model errors: misspecification (linear model on nonlinear data), sample size, heterogeneous data  Big data = more data to test models w/reliable error bounds, good proxy for pop  No “clean data”, no controls or ability to know anything but passive data  Space to test some theories w/controlled experiments w/internet  Predictive models = how everyone will vote and how to change them  Social science theories ^ due to big interaction on net  Conclusion o Big data can look at situations that wouldn’t occur in controlled experiments o Machine asks questions humans won’t think of, large & varied wealth of data o Computer science, statistics, causal modeling… o Machine learning & predictive modeling; machine learning + human knowledge o Orgs: Test on internet & change based on data Three Science Words  Hypothesis : testable prediction from an idea  Theory : supported by evidence  Scientific Law : generally true  Model : replace them all, remaking of something else o Physical Model o Mathematical Model o Conceptual Model


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