Section outline

  • 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

    • Lecture Slides:

    • Biostat 202 Lecture 1 - in class File
    • Biostat 202 Lecture 1 - in class File
    • Biostat 202 Lecture 1: Introduction File
    • Biostat 202 Lecture 1: Introduction File
    • Biostat 202: Orange Data Mining Download Guide File
    • Biostat 202 Syllabus File

    • Pre-recorded lecture - watch before live Zoom class session (Access restricted to registered students):

    • Prerecorded Lecture 1 - module 1 Media Resource
      Not available unless: You belong to Registered Students Only
    • Prerecorded Lecture 1 - module 2 Media Resource
      Not available unless: You belong to Registered Students Only
    • Prerecorded Lecture 1 - module 3 Media Resource
      Not available unless: You belong to Registered Students Only
    • Prerecorded Lecture 1 - module 4 Media Resource
      Not available unless: You belong to Registered Students Only
    • Prerecorded Lecture 1 - module 5 Media Resource
      Not available unless: You belong to Registered Students Only
    • Prerecorded Lecture 1 - module 6 Media Resource
      Not available unless: You belong to Registered Students Only

    • Large Group Discussion (Access restricted to registered students): Brief formal review of lecture followed by question and answer discussion. Recorded lecture should be viewed prior to this session.

    • Biostat 202 Lecture 1 - RECORDED Zoom Session URL
      Not available unless: You belong to Registered Students Only
    • Biostat 202 Lecture 1 - RECORDED Zoom Session Media Resource
      Not available unless: You belong to Registered Students Only

    • Final Project

    • Final Project Folder
    • Component 1 - upload here (Due Sunday, Aug 8 @ 11:55 PM) Assignment
    • Component 2 - upload here (Due Sunday, August 15 @ 11:55 PM) Assignment
    • Component 3 - upload here (Due Sunday, August 22 @ 11:55 PM) Assignment
    • Final Project - upload here (Due Tuesday, Sep 2 @ 11:55 PM) Assignment

    • Optional Reading:

      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.