lecture 1: intro to data science, statistical thinking, and course logistics

what is data science

extracting interpretations from jumbled collection of numbers

what is statistical thinking

measuring uncertainity of quantitative information

what is domain knowledge

know subject context of numbers

CSV

comma separated value

QMD

quarto marked document

logistics of pacing

• 1st half — data science → midterm

• 2nd half — statistical thinking → midterm

• final over it all

• each = 20%

• midterm

  • in class = concepts

  • out of class = application

• final

  • out of class

  • can replace in class portion of a midterm

• project group within the lab = 20%

workload logistics

• prepare = priming → learn material ahead of class

  • most of the time, lecture slides will be ready

• homework = lab ( 1 week, guided in lab)

  • lowest lab score is dropped

• in class = application exercises 5%

  • only have to do 70%

• labs = 15%

AI/stackoverflow uses

• code

  • must have a direct URL link for chat gpt convo

  • stack overflow link

  • if you do not cite, it is counted as plagarism

• narrative

  • write your own words!