SQL-DataCamp-Analyzing Business Data in SQL
作者：互联网
SQL-DataCamp-Analyzing Business Data in SQL
1. Revenue, Cost, and Profit
1.1 Revenue
1.2 Revenue per customer
1.3 Revenue per week
1.4 Cost and Common Table Expressions (CTEs)
1.5 Total cost
1.6 Top meals by cost
1.7 Using CTEs
1.8 Profit
1.9 Profit per eatery
1.10 Profit per month
2. User-centric KPIs
2.1 Registrations and active users
2.2 Registrations by month
2.3 Monthly active users (MAU)
2.4 Window functions
2.5 Registrations running total
2.6 MAU monitor (1)
2.7 Growth
2.8 MAU monitor (2)
2.9 MAU monitor (3)
2.10 Order growth rate
2.11 Retention
2.12 New, retained, and resurrected users
2.13 Retention rate
3. ARPU, Histograms, and Percentiles
3.1 Unit economics
3.2 Average revenue per user
3.3 ARPU per week
3.4 Average orders per user
3.5 Histograms
3.6 Histograms of revenue
3.7 Histograms of orders
3.8 Bucketing
3.9 Bucketing users by revenue
3.10 Bucketing users by orders
3.11 Percentiles
3.12 Revenue quartiles
3.13 Interquartile range
4. Generating an Executive Report
4.1 Survey of useful functions
4.2 Formatting dates
4.3 Rank users by their count of orders
4.4 Pivoting
4.5 Pivoting user revenues by month
4.6 Costs
4.6 Producing executive reports
4.7 Report readability
4.8 Executive report
4.9 Course recap
agoldminer
发布了14 篇原创文章 · 获赞 1 · 访问量 191
私信
关注
标签：MAU,users,Revenue,Profit,per,SQL,Data,DataCamp 来源： https://blog.csdn.net/agoldminer/article/details/103648863