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Thinking differently in data science

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Recent talk of mine at University of Chicago :) Includes questions from the interactive portion, in which the audience was able to brainstorm about interdisciplinary approaches.

Published in: Data & Analytics
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Thinking differently in data science

  1. 1. Thinking Differently in Data Science: The Interdisciplinary Approach Colleen M. Farrelly
  2. 2. About Me  Former MD/PhD student  Background outside of math/stat including sociology, biochemistry, molecular biology, psychology, genomics, epidemiology.  Work history including academic medical studies, government, military, biotech, and education.  Areas of expertise include topological data analysis, measurement models, Bayesian designs, geometry in machine learning.
  3. 3. Overview  Multidisciplinary approaches often needed to solve data science problems effectively.  Can draw on many different areas depending on the problem:  Sociology  Industrial psychology  Marketing  Genomics  Finance  Medicine  Individuals with a broad knowledge base are well-equipped for a career in data science.
  4. 4. Example Problems
  5. 5. Problem 1: Health Risk Modeling  Problem: Obesity and related problems are costing a healthcare system a lot of money. How do we flag patients at risk and try to intervene on patients who are sick?  What disciplines might be needed? What causes might we consider? Anything to take into account when designing the data mining and a possible trial? What sorts of expertise might we need on this project and the implementation of results?
  6. 6. Problem 1: Health Risk Modeling  Food deserts  Jobs with little opportunity to be active  Genetic component  Lack of understanding around nutrition  Stress
  7. 7. Problem 2: Market Forecasting  Problem: How can we get a better model of future valuation of a company or sector to find good investment opportunities?  What disciplines might be needed? What outside influences might we need to account for? How might we set up the analysis?
  8. 8. Problem 2: Market Forecasting  Election results  Natural disasters (like Irma)  Other market fluctuations  Breakthrough inventions  Employment, GDP… fluctuations
  9. 9. Problem 3: Predicting Disease from Genetic Data  Problem: Given a sequence of genetic data and patient case history information, provide a short list of differential diagnoses with a high probability of matching the underlying disease.  What might complicate this analysis? Could the patient have more than one underlying disease? Do you think the data is structured or unstructured? What might be some technical challenges? Which disciplines could be helpful on this project?
  10. 10. Problem 3: Predicting Disease from Genetic Data  Epigenetic factors (environment)  Comorbidity  Doctor error  Incorrect spelling or unreadable shorthand  Computational challenges of data storage and analysis requirements  Statistical test problems (p>>n)
  11. 11. Conclusions  Domain knowledge is important in data science.  Interdisciplinary backgrounds or team compositions can help understand a given project from multiple angles.  This avoids potential bias or unreasonable assumptions.  Technical expertise + domain knowledge creates value in data science projects.

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