It is crucial that the reader (and your advisor) understands the format of your data.

You need to distinguish between your raw data, and your final data set.

Your raw data is how the data is stored at the company, or how you gathered the data yourself (e.g., using web scraping or APIs)

You need to know and explain:

• What’s the “primary key” of this data? ( -> what identifies a unique row in this data set?)
  • For example, data may be stored per video_id - day (e.g., the number of YouTube views per video per day), or per shop - user - day (recording sales of a user for an online shop per day)
  • Make explicit the frequency of your data (e.g., per month, week, day, hour, second…)
• What are the “value” columns?
  • Value columns is data that is recorded per primary key (e.g., video views for YouTube, sales for the online shop).
• Typically, you may encounter different tables with different primary keys and value columns
  • E.g., a table with user demographics, a table with sales data, a table with clickstream data, etc.

Create some summary statistics of this data

• Always have a table of mean, SD, min, max per variable (“descriptive statistics”)

• Try to be creative

  • E.g., for sales data of a shop, create a summary of how many users buy per shop, or
  • E.g., for panel data (i.e., users/brands/artists observed over time): some time series plots (e.g., line graphs, for each user a line over time)