1. the worst post-booking experience
2. increase top-line
3. Pick a music/podcast app
4.the worst post-booking experience on the platform
5.How would you improve it
6. worst post booking experience
7.insert your favorite music app
8.OpenTable's
9.Analyze performance of subscription services
10.Investigate the drop of new customers
11.Investigate the decrease of subscription of dashpass
12.Measure the success of dashpass
13.objective of dashpass
14.Problem of search results
15.Reasons of why no differences in ab tesing
16.Metrics of adding search bar
17.Investigate the increase of dasher waiting time in restaurants
18.Root cause of not working
19.Compare adopters and non-adopters using causal inference
20.Seasonal effect
21.Catering service complain
22.Cooperation with restaurants
23.Percentage calculation
24.Sending coupons
25.Influence of server down
26.Grocery delivery
27.Growth
28.餐馆引入促销功能
29.Increase radius to 10 miles from 5 miles
30.Super late的order
31.Dasher extra pay incentive program
32.超过100刀的订单
33.Grocery store add search bar
34.DashPass
35.Roll out product
36.Data collection
37.SQL filtering and aggregation functions
38.Experiement design
39.Experiement design
40.Experiement design
41.SQL aggregation function
42.Metrics analysis
43.AB Test
44.SQL aggregation function
45.SQL window function
46.SQL aggregation function
47.Ncrease dasher response rate
48.SQL aggregation function
49.SQL aggregation function
50.Metrics evaluation
51.异动分析以及原因
52.AB Test
53.SQL filtering and aggregation functions
54.Explain launched feature only used once
55.Experimental design
56.What metrics will you use to evaluate the impact
57.两个product合并
58.新的城市sign on新的餐厅
59.SQL Query - Bottom Quartile Restaurant Orders
60.SQL Query - Month-over-Month Sales Change
61.SQL Query - Most Frequent Customers
62.SQL Query - High-frequency Customers
63.Buddy Strings and K-Anagrams
64.Determining Promotion Discount Rates
65.Designing an A/B Testing Experiment
66.SQL Monthly Data Correction
67.SQL Lag Window Function
68.SQL Reverse Order Query
69.SQL Monthly High Frequency Users
70.Investigating Low Consumer Retention Rate
71.Identify High-Frequency Customers and the Top Customer by Order Count
72.Analyzing Bottom 25% Restaurant Orders and Customers Percentage
73.Month-over-Month Sales Change for a Given Restaurant
74.Identifying Top Customers Excluding High Frequency Ones
75.Calculating High Frequency Customer Percentage
76.eBike Case Study
77.SQL Questions
1. the worst post-booking experience
How would you improve the worst post-booking experience for an Airline?
2. increase top-line
How would you run a promotion to increase top-line, in-store revenue for Target? How would you decide what to promote? How would you run the experiment?
3. Pick a music/podcast app
Pick a music/podcast app. Why do you like it? On the flip side, what do you not like? What improvements would you suggest?
4. the worst post-booking experience on the platform
The CEO of Open Table comes to you and is worried about the worst post-booking experience on the platform. What do you do?
5. How would you improve it
What do you like about Spotify? How would you improve it?
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