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

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    Opportunities and Challenges Biomedical Data: Introduction to the Science of "Big Data" BIOSTAT 202 Summer 2016  (3 units). Course Director: John Kornak, PhD. Associate Professor, Department of Epidemiology and Biostatistics

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• Select section Thursday, July 28, 2016; 1:00 PM - 2:30 PM

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  Thursday, July 28, 2016; 1:00 PM - 2:30 PM

  Lecture: 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:  Charles McCulloch
  
  Location:  Mission Hall 1400

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    1)      Data mining with SPSS modeler. Theory, Exercises and Solutions. Tilo Wendler and Sören Gröttrup. Springer, Switzerland, 2016. Available for free electronically within the UCSF network via http://link.springer.com/book/10.1007%2F978-3-319-28709-6. This book is focused on the IBM SPSS Data Modeler software, though is still useful for learning about methods. The book provides background on using IBM SPSS Data Modeler for data cleaning and manipulation and then onto the application of simple statistical methods and visualization, before going on to cover regression, data-reduction methods, clustering and classification. The book gives IBM SPSS Data Modeler examples and screenshots throughout.

    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 www-bcf.usc.edu/~gareth/ISL/. Largely a machine learning 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.

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• Select section Thursday, July 28, 2016; 2:45 PM - 4:15 PM

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  Computer Lab: Introduction to software
  

  Faculty:  Elaine Allen
  
  Location: Mission Hall 1400

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• Select section Monday, August 1, 2016; 1:00 PM - 2:30 PM

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  Lecture: Getting data from large databases
  Public use data (NHANES, NIS, CMS, national death index, etc, large cohort studies).  Electronic health records.  Social media.

  Faculty:  Charles McCulloch

  Location:  Mission Hall 1400

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• Select section Thursday, August 4, 2016; 1:00 PM - 2:30 PM

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  Lecture: Managing data and data storage
  Data harmonization and curation.  Issues with poor quality data. 

  Faculty:  Elaine Allen
  
  Location:  Mission Hall 1400

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• Select section Thursday, August 4, 2016; 2:45 PM - 4:15 PM

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  Computer Lab 2
  

  Faculty:  Charles McCulloch
  

  
  Location:  Mission Hall 1400

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• Select section Monday, August 8, 2016; 1:00 PM - 2:30 PM

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  Monday, August 8, 2016; 1:00 PM - 2:30 PM

  Lecture: Data visualization/story-telling
  Graphical and tabular methods for displaying data to uncover/understand associations.

  Faculty:  Elaine Allen
  
  Location: Mission Hall 1400

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• Select section Thursday, August 11, 2016; 1:00 PM - 2:30 PM

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  Lecture: Digital Health
  Electronic data sensors.  Issues with data storage and processing.

  Faculty:  Mark Pletcher

  
  Location: Mission Hall 1400

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• Select section Thursday, August 11, 2016; 2:45 PM - 4:15 PM

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  Computer Lab 3

  Faculty:  Elaine Allen
  

  Location:  Mission Hall 1400

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• Select section Monday, August 15, 2016; 1:00 PM - 2:30 PM

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  Lecture: Prediction algorithms 1
  Statistical regression models.  Machine learning. Supervised learning, part 1

  Faculty:  John Kornak
  
  Location:  Mission Hall 1400

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• Select section Thursday, August 18, 2016; 1:00 PM - 2:30 PM

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  Lecture: Predication algorithms 2
  Statistical regression models.  Machine learning. Supervised learning, part 2

  Faculty:  John Kornak

  Location: Mission Hall 1400

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• Select section Thursday, August 18, 2016; 2:45 PM - 4:15 PM

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  Computer Lab 4
  

  Faculty:  John Kornak
  
  Location:  Mission Hall 1400

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• Select section Monday, August 22, 2016; 1:00 PM - 2:30 PM

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  Lecture: Clustering algorithms
  K means clustering. Unsupervised learning

  Faculty:  John Kornak
  
  Location:  Mission Hall 1400

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• Select section Thursday, August 25, 2016; 1:00 PM - 2:30 PM

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  Lecture: Causal inference from big data
  Issues of bias and how to minimize.  Selection bias.  Methods to minimize bias in observational studies.

  Faculty:  Charles McCulloch
  
  Location:  Mission Hall 1400

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• Select section Thursday, August 25, 2016; 2:45 PM - 4:15 PM

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  Computer Lab 5

  Faculty:  John Kornak
  
  Location:  Mission Hall 1400

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• Select section Monday, August 29, 2016; 1:00 PM - 2:30 PM

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  Lecture: Large pragmatic trials
  Conducting RCTs using big data methods. 

  Faculty:  Mark Pletcher
  
  Location:  Mission Hall 1400

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• Select section Thursday, September 1 2016; 1:00 PM - 2:30 PM

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  Lecture: Case study in big data
  Rotating

  Faculty:  John Kornak and Howie Rosen
  
  Location:  Mission Hall 1400

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