Lecture 1 Notes

Goals

• Have a high-level view of what Data Science is about and what data scientists do

• Understand what is meant by a few properties of data:

  • Structure vs unstructured
  • Numerical vs categorical
  • Big vs small

Intro and Logistics

• [slides] Welcome; about me

• [slides] Plan for today

• [slides / webpage] Logistics

  • Webpage/syllabus

    • Schedule table

    • Major assessment items

    • Policies

      • Read them
      • Recommended textbook

    • Safe and inclusive learning environment

      • We're all coming from different places

        • example: different experience levels, math background
        • this is an opportunity!

      • More generally, this extends to many aspects of background and identity; gender, orientation, race/ethnicity, etc.

      • We need to be careful to avoid being exclusionary, even by accident

        • Ties into course material; we'll talk some about data science and society/ethics

  • Flexibility

    • 2nd run of the course (I don't know everything!)
    • 160 students, 1 baby, 1 more coming
    • Flexibility all around

  • Feedback

    • Solicited: Fill out the surveys I ask (first one out Friday due Monday, in place of quiz)

    • Unsolicited:

      • Send me email
      • Talk to me in office hours
      • Fill out the anonymous google form linked from the syllabus
      • Yell at me

What is data science? What is data?

• [colab] What is data science? Student suggestions

  • my take: The process and/or study of extracting insights from data.
  • premise: "Data is not information is not unsderstanding is not wisdom" - Clifford Stoll
  • Tools: CS, Stats, Domain expertise
  • Practitioners: CS, Stats, Business

• [colab] What do data scientists do? Student suggestions

  • answer questions
  • discover patterns, trends, insights
  • make predictions (classification, regression)

• [colab] What is data? Student suggestions

  • Properties of data

    • structured/unstructured
    • numerical/categorical
    • big/small

  • Types of Structured data

    • table/matrix (this is our jam in this class)

      • example: course roster / gradebook

    • tree

      • who works for whom at WWU? (Org chart)

    • graph

      • who's friends with whom? (Facebook)

    • stream/time series

      • stock values; median home prices; consumer price index; hourly temperature