Data Science Course

directories=folders
root directory: directory at the top of the tree (/) home directory: contains personal files (~)
navigate directories: windows search Git Bash, Mac search terminal

• pwd: to search the path to your working directory
  command: is the CLI command which does a specific task
  flags: are options we give to the command to trigger certain behaviors, preceded by a -
  arguments : can be what the command is going to modify, or other options for the command
  (pwd is a command that requires no flags or arguments)
• clear: will clear out the commands in current CLI windows
• ls: lists files and folders in current directory
  ls -a: lists hidden and unhidden files and folders
  ls -al: lists details for hidden and unhidden files and folders
  (-a and -l are flags, they can be combined into flag -al)
• cd: stands for "change directory"
  cd takes as an argument the directory you want to visit
  cd with no argument takes you to your home directory
  cd.. allows you to change directory to one level above your current directory
• mkdir: stands for "make new directory"
  mkdir takes as an argument the name of the directory you're creating
• touch: creates an empty file
• cp: stands for "copy"
  cp takes as its first argument a file, and as its second argument the path to where you want the file to be copied
  cp can also be used for copying the contents of directories, but you must use the "-r" flag
  the line: cp -r Documents More_docs copies the contents of Documents into More_docs
• rm: stands for "remove" or "delete"
  rm take sthe name of a file you wish to remove as its argument
  you can use rm to delete entire directories and their contents by using the "-r" flag
  be very careful when you do this, there is no undo an rm
• mv: stands for "move"
  with mv you can move files between directories
  you can use mv to rename files (mv oldname_file newname_file)
• echo: will print whatever arguments you provide
• date: will print today's date

have 2 "#": ## This is a secondary heading
have 3 "#": ### This is a tertiary heading

goal: describe a set of data
the first kind of data analysis performed
most commonly applied to census data
the description and interpretation are different steps
descriptions can usually not be generalized without additional statistical modeling

goal: find relationahips you didn't know about
exploratory models are good for discovering new connections
they're useful for defining future data science projects
exploratory analyses are usually not the final say
exploratory analyses alone should not be used for generalizing/predicting
correlation does not imply causation

goal: use a relatively small sample of data to say something about a bigger population
inferential is commonly the goal of statistical models
inference involves both estimating quantity you care about and your uncertainty about your estimate
inference depends heavily both on the population and the sampling scheme

goal: to use the data on some objects to predict values for another object
if X predicts Y it does not mean that X causes Y
accurate prediction depends heavily on measuring the right variables
although there are better and worse prediction models, more data and a simple model works really well
prediction is very hard, especially about the future references

goal: to find out what happens to one variable when you make another variable change
usually randmized studies are required to identify causation
there are approaches to inferring causation in non-randomized studies, but they are complicated and sensitive to assumptions
causal relationship are usually identified as average effects, but may not apply to every individual
causal models are usually the "gold standard" for data analysis

goal: understand the exact changes in variables that lead to changes in other variables for individual objects
incredibly hard to infer, except in aimple situations
usually modeled by a deterministic set of euqations (science/engineering)
generally the random component of the data is measurement error
if the equations are known but the parameters are not, they may be inferred with data analysis

Data are values of qualitative or quantitative variables, belonging to a set of items.

data sharing at: https://github.com/
http://figshare.com/
http://github.com/jtleek/datasharing