BIOSTAT 202: Opportunities and Challenges of Complex Biomedical Data: Introduction to the Science of "Big Data" (Summer 2020

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    Opportunities and Challenges Biomedical Data: Introduction to the Science of "Big Data"  (Click for Course Descriptions) BIOSTAT 202 Summer 2020  (3 units). Course Director: Aaron Wolfe Scheffler, PhD. Assistant Professor, Department of Epidemiology and Biostatistics OFFICE HOURS: Monday 2:00 - 3:30 PM by appointment

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• Select section Thursday, July 30, 2020; 1:00 PM to 2:30 PM

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  Lecture 1: Introduction to the course
  
  What is data science/big data?  What makes it different from non-big data?  What big data can and cannot do.  Phases of data science:  getting data, merging and cleaning data, storing and accessing data, visualizing or telling stories with data, drawing conclusions from data.

  Faculty:  Aaron Wolfe Scheffler
  

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    Optional Reading:

    1)      An introduction to statistical learning. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. New York: Springer, 2013 and is available for free via http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf . Largely a data mining book. The book is technical in places, but strives to explain things in text. The book provides accompanying code in R to illustrate concepts, but the R material is restricted to lab sections at the end of each chapter; understanding R is not necessary for following the general concepts in the book.

• Select section Thursday, July 30, 2020; 2:45 PM - 4:15 PM

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  Lab 1: Introduction to software
  

  Live online session in which students have access to course faculty for questions on current or prior curriculum, assignments and software implementation
  
  

  Faculty:  Aaron Wolfe Scheffler
  
  Location: https://ucsf.zoom.us/j/99789000780?pwd=b3JVLzd2cWI5VTJNWkJEOEVDYnQ5UT09

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• Select section Monday, August 3, 2020; 1:00 PM - 2:30 PM

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  Lecture 2: Getting data from large databases

  Public use data (NHANES, NIS).  Electronic health records. 

  Faculty:  Aaron Wolfe Scheffler
  

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• Select section Thursday, August 6, 2020; 1:00 PM - 2:30 PM

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  Lecture 3: Managing data and data storage

  Data management, cleaning, and storage.

  Faculty: Aaron Wolfe Scheffler
  

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• Select section Thursday, August 6, 2020; 2:45 PM - 4:15 PM

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  Lab 2: Managing data and data storage
  

  Live online session in which students have access to course faculty for questions on current or prior curriculum, assignments and software implementation
  
  

  Faculty:  Aaron Wolfe Scheffler
  
  Location: Zoom URL

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• Select section Monday, August 10, 2020; 1:00 PM - 2:30 PM

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  Lecture 4: Machine Learning

  Introduction and supervised learning [regression]

  Faculty:  Aaron Wolfe Scheffler
  

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• Select section Thursday, August 13, 2020; 1:00 PM - 2:30 PM

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  Lecture 5: Machine Learning 2

  Supervised learning [classification]

  Faculty:  Aaron Wolfe Scheffler
  

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  Lab 3: Machine Learning
  

  Live online session in which students have access to course faculty for questions on current or prior curriculum, assignments and software implementation
  
  

  Faculty:  Aaron Wolfe Scheffler
  
  Location: Zoom URL

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• Select section Monday, August 17, 2020; 1:00 PM - 2:30 PM

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  Lecture 6: Machine Learning 3

  Supervised learning, cont. [classification]

  Faculty:  Aaron Wolfe Scheffler
  

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• Select section Thursday, August 20, 2020; 1:00 PM - 2:30 PM

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  Lecture 7: Machine Learning 4

  Cross-validation, ensemble methods, and feature importance

  Faculty:  Aaron Wolfe Scheffler
  

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• Select section Thursday, August 20, 2020; 2:45 PM - 4:15 PM

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  Lab 4: Machine Learning Competition
  

  Live online session in which students have access to course faculty for questions on current or prior curriculum, assignments and software implementation
  
  

  Faculty:  Aaron Wolfe Scheffler
  
  Location: Zoom URL

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• Select section Monday, August 24, 2020; 1:00 PM - 2:30 PM

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  Lecture 8: Unsupervised Learning

  Unsupervised learning, clustering, and dimension reduction

  Faculty:  Aaron Wolfe Scheffler
  

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• Select section Thursday, August 27, 2020; 1:00 PM - 2:30 PM

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  Lecture 9: Causal Inference From Big Data

  Issues of bias and how to minimize. Selection bias. Methods to minimize bias in observational studies.

  Faculty:  Aaron Wolfe Scheffler
  

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• Select section Thursday, August 27, 2020; 2:45 PM - 4:15 PM

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  Lab 5: Unsupervised Learning
  

  Cover clustering and dimension reduction.
  
  

  Faculty:  Aaron Wolfe Scheffler
  
  Location: Zoom URL

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• Select section Monday, August 31, 2020; 1:30 PM - 3:00 PM

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  Lecture 10: Data visualization/storytelling

  Graphical and tabular methods for displaying data to uncover/understand associations.

  Faculty:  Aaron Wolfe Scheffler
  

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• Select section Thursday, September 3 2020; 1:00 PM - 2:30 PM

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  Lecture 11: Case studies in "Big Data"

  Taking a step back, we will review what we learned and run through several case studies in "Big Data" and identify the possible ways to analyze the data.

  Faculty:  Aaron Wolfe Scheffler
  

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• Select section Thursday, September 3, 2020; 3:15 PM - 4:15 PM

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  Lab 6: Data Visualization
  

  Live online session in which students have access to course faculty for questions on current or prior curriculum, assignments and software implementation
  
  

  Faculty:  Aaron Wolfe Scheffler
  
  Location: Zoom URL

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• Select section Thursday, September 10, 2020; 1:00 PM - 2:30 PM

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  Lecture 12: Project Office Hours

  Faculty:  Aaron Wolfe Scheffler
  

  Ethics in Big Data

  
  

  Ethical application of machine learning to big data is an important topic and I would like to provide some resources for you to archive and peruse as your own leisure. As clinical researchers, the approaches and considerations we take when analyzing data have implications on treatment and health outcomes. Even the most well intended applications of machine learning to big data can have unanticipated consequences that produce bias or even discrimination. Therefore, please keep these topics in mind as you develop your future research projects.

  
  

  Resources:

  
  

  Online courses: 

  • Practical data ethics: an online short course on data ethics by https://ethics.fast.ai/. Very organized structure with recorded video lectures.

  Books:

  • Cathy O'Neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishers, 2016.
  • Broussard, M. (2018). Artificial Unintelligence: How Computers Misunderstand the World. MIT Press.

  Journal articles with discussion of bias in machine learning with clinical applications:

  • Vyas DA, Eisenstein LG, Jones DS. Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms. N Engl J Med. 2020;383(9):874-882. doi:10.1056/NEJMms2004740
    
  • Ziad Obermeyer and Sendhil Mullainathan. 2019. Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). Association for Computing Machinery, New York, NY, USA, 89. DOI:https://ucsf.idm.oclc.org/login?url=https://doi.org/10.1145/3287560.3287593

  
  

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