Data Science Toolbox

Course page: https://www.coursera.org/learn/data-scientists-tools By Jeff Leek, PhD, Roger D. Peng, PhD, Brian Caffo, PhD

• Get help from R:

  ?rnorm
  help.search("rnorm")
  
  # get the function arguments:
  args("rnorm")
  
  # See the code:
  rnorm

• R reference card downloaded.
• To ask for help on a problem with R:
  1. What steps to reproduce the problem ?
  2. What did we expect to see ?
  3. What did we see instead ?
  4. What version of R and packages ?
  5. What operating system ?
• To ask for help on data analysis question:
  1. What is the question we try to answer ?
  2. What steps were used to answer the question ?
  3. What did we expect to see ?
  4. What did we see instead ?
  5. What other solutions did we think about ?
• Places to get info on data science questions:
  • stackoverflow
  • R mailing list
  • CrossValidated
  • Google: “[data type] data analysis” or “[data type] R package”

• To checkout a local copy of a github repo:

  cd my_folder
  git init
  git remote add origin https://github.com/roche-emmanue/my_repo.git

• Help on git and github from:
  • http://git-scm.com/doc
  • https://help.github.com/
  • Google/stackoverflow

• Basic Markdown:
  • Headings with:

    ## This is a secondary heading
    ### This is tertiary heading

  • Unordered lists:

    * Item 1
    * Item 2
    * Item 3

• Types of data science questions:
  1. Descriptive:
     • Just trying to describe the data
  2. Exploratory:
     • Trying to find relationships (but not really trying to confirm)
  3. Inferential:
     • Take small dataset and try to generalize that to a larger population.
  4. Predictive:
     • X predicts Y, doesn't mean that X causes Y
  5. Causal
     • What happen if we change the value of one variable…
  6. Mechanistic

• What is Data:
  • In a “set of items” = population
  • Variables = measurement of characteristics.
  • qualitative (discret scale) or quantitative (continuous scale)
  • Could be Raw file / API / Video / Audio
  • Is the second most important thing: (The question is the most important thing)

• To share large amount of data: http://figshare.com

• To avoid confounder we can fix the confounding variable, stratify variables, or randomize them if they cannot be fixed.