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Sports clothing is a booming sector!
In this notebook, you will use your SQL skills to analyze product data for an online sports retail company.
You will work with numeric, string, and timestamp data on pricing and revenue, ratings, reviews, descriptions, and website traffic.
You will use techniques such as aggregation, cleaning, labeling, Common Table Expressions, and correlation to produce recommendations on how the company can maximize revenue!
- 1Counting missing values
- 2Nike vs Adidas pricing
- 3Labeling price ranges
- 4Average discount by brand
- 5Correlation between revenue and reviews
- 6Ratings and reviews by product description length
- 7Reviews by month and brand
- 8Footwear product performance
- 9Clothing product performance
Analytics and Data Science Curriculum Manager, DataCamp
George is an Analytics and Data Science Curriculum Manager at DataCamp. He holds a PGDip in Exercise for Health and BSc (Hons) in Sports Science and has experience in project management across public health, applied research, and not-for-profit sectors. George is passionate about sports, tech for good, and all things data science.