Introduction to the Course

Data Literacy

Daniel Winkler

Institute for Retailing & Data Science

Welcome to Data Literacy!

Who am I?

• I’m Daniel 👋
• Masters in Economics with major in Mathematics
• Data-driven research in various fields
  • Dissertation: What shapes demand in the music industry?
    • Artist misconduct (#MeToo) increases demand
    • Identification of playlists important for artist success
  • Organ donation
    • Switching from opt-in to opt-out does not increase available organs
  • Statistical software (e.g., shrinkDSM)
  • Biomarker analysis for cancer research (e.g., FGF8)
• Fraud detection for one of the top 5 music markets world-wide
  • analysis of \(\sim 200\) billion datapoints

My Rabbit Georgie

Why am I here?

High-level Goals

• Have fun
• Gain confidence in analysis
• Build a solid foundation for current and future data-based projects
• Learn to transform raw data into (business/economic) insights
• Provide a safe space for exploration

Explicit Goals

• Set up a development environment for data-based projects
  • Use extensible and future-proof tools
• Get familiar with R, Quarto, and Git
  • Create a flexible, documented, reproducible, and collaborative workflow
• Get familiar with concepts to process, visualize, and analyze data
  • Make conscious decisions about each step
  • Confidently choose models and methods appropriate for a given situation
• Gain practical experience through conducting a short data analysis
• Grading based on engagement

Non-goals

• Learn a “recipe” for data analysis
  • Great for class becaus it’s easy
  • Not useful in practice
• Provide a formal introduction to statistics/data-science
  • Requires it’s own course
  • Focus here on intuition
• Harsh grading
  • This is an elective :)
  • Discourages exploration

Why are you here?

10 minutes

• Create a name-tag for yourself (preferably A4 so I can read it)
• Find someone in class you have not yet met
• Discuss the following questions and take notes of your neighbors answers
  • Who are you?
  • What are you interested in?
  • What constitutes a good learning environment for me?
  • Have you used R or another statistical software before?
  • How can I measure my success in the course?
    • Formulate 3 measures (e.g., I learned how to run linear regression in R for my thesis)
  • What can Daniel do in each lecture for me to be successful in the course?
    • Formulate 3 suggestions (e.g., Provide multiple examples for each concept)
  • What can I do in each lecture to be successful in the course?
    • Formulate 3 review questions for after the lecture (e.g., Did I ask questions if something was unclear?, Did I party yesterday and showed up hung-over?)

Introduce your neigbor to the class

What’s next?

Grading

25% class participation

• Today: upload 3 suggestions to make your neighbor successful
• Every lecture: either participate in class directly or upload a short review to Canvas after class
• 10% for each Saturday lecture, 5% for Wednesday

30% project plan

• Discussion next Wednesday (2024.03.13)
• Due next Friday (2024.03.15)
• Upload to Canvas
• Includes:
  • Research question
  • A dataset that can be used to answer the research question
  • A target audience
  • Optionally: a first idea on how to answer the research question

45% project presentation

• Presentations on Wednesday 11 days from today (2023.03.20)
• \(\sim 10 min\) per presentation
• \(\sim 5 min\) discussion for paired groups
• Upload slides to Canvas
• Includes:
  • Introduction to the research question
  • Short literature review
  • Introduction to the data
  • Model-free evidence (e.g., visualizations)
  • Model-based evidence (e.g., regression analysis)
  • Conclustion & Recommendation
  • Optional: Short discussion of solutions to challenges faced