Foundation · Week 01

Course launch + analytics thinking

Begin with the course roadmap, then learn to turn decisions into focused questions and evaluate evidence through sports analytics and business intelligence applications.

Aug 26–28Two class meetings

Focus

  • Course expectations and systems
  • Decision → question → evidence → action
  • Sports analytics and business intelligence

Prepare

  • Read the syllabus
  • Open Brightspace and bookmark this website
  • Locate your section’s R repository

Practice

  • Frame an answerable analytical question
  • Identify the unit, outcome, comparison, and context
  • Interpret evidence without overstating it

Learning targets

By the end of the week, you should be able to:

  • describe data analytics as a process connecting questions, data, analysis, insight, and decisions;
  • translate a broad sports or business decision into a focused, answerable question;
  • distinguish descriptive, diagnostic, predictive, and prescriptive questions;
  • identify the unit of observation, outcome, comparisons, context, and evidence needed to address a question;
  • explain how sports analytics and business intelligence support different decisions without eliminating uncertainty; and
  • locate the syllabus, weekly roadmap, course materials, Brightspace, and your section’s R repository.

Class plan

Move What we will do
Orient Review the course purpose, expectations, grading, policies, learning systems, and semester roadmap.
Frame Use a café-location decision to distinguish forecast accuracy from decision usefulness.
Compare Distinguish descriptive, diagnostic, predictive, and prescriptive questions and the evidence each requires.
Apply Interpret season-ticket, football, and player-performance evidence without overstating what it shows.
Monitor Connect recurring questions, key performance indicators (KPIs), and dashboards to business intelligence decisions.
Investigate Use the NYC311 dashboard to compare community districts, propose a tentative next step, and state an important limitation.

Lecture slides

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Lecture 1 · SyllabusView slides in new tab
Lecture 2 · Data Analytics ThinkingView slides in new tab

This week’s materials

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