1.The Standard error of mean and median
2.Too many features
3.Normalize the matrix
4.Sample mean和median
5.一些模型问题
6.design a bike sharing product
7.Design a bike program
8.Build a valet service for hotels
9. improve YouTube for emerging markets
10.Design a grocery store for a dense, urban area
11.Improve the airport security experience
12.What is your favorite product?
13.a new consumer facing music subscription service
14.the PM at Target
15. Food Delivery space
16.Choose a product
17.3 products you use
18.Talk about a favorite google product
19.the app on your mobile that you hate
20.make a product
21.How would you improve your iPhone
22.How would you improve Youtube for kids?
23.Improve the theme park experience
24.What will you do
25.which you liked
26. improve the restaurant discovery
27.change about Google Accounts (auth)
28.how would you solve it
29.Imagine
30.a product you have to use every day that you hate
31.favorite product
32.create a playlist for a user
33.favorite Google produc
34.improve driving experience
35.3 products
36.avorite product
37.Launch or not
38.Setting KPI
39.Hypothesis testing
40.Hypothesis testing
41.Measure impact of sending coupon
42.SQL
43.Sampling regression VS. population regression
44.Compare regressions
45.Mean VS. Median
46.Making shoes for dogs
47.Mean VS. Median
48.Type I error
49.Explain p-value
50.Causal effect
51.Sum of even and odd locations
52.Sample sizes
53.Interpret results to different audiences
54.CTR
55.General statistics questions
56.Estimate population
57.Measure level of segragation
58.If it is good to survey whether education leads to higher salary in certain areas
59.test statistics
60. google map餐厅
61.Data structure
62.Probability
63.Saving time period which can insert and search
64.Delete odd index node
65.Relationship between education background and income
66.Questions about Standard error
67.Sum of even and odd
68.Counting two sums
69.Writing a function to pick up balls from basket without replacement
70.Assess data quality and data performance
71.Experiment design
72.String substitution
73.Experiment design
74.Experiment design
75.LeetCode 849
76.Causal inference
77.Modeling
78.LeetCode 253
79.Minimum time to travel in matrix
80.Order of delete
81.Embarrased vampires
82.K largest values with at most m labels
83.Build logistic regression
84.Difference between independent and uncorrelated
85.How to approximate retention rate with insufficient data
86.Estimate arrival time of shuttle bus
87.Return array from a list
88.Legal squares
89.Replace word by dictionary
90.Record temperature
91.Build restaurant waiting list
92.Valid directions
93.Check timeout
94.Design prediction system
95.Intern apartment arrangement
96.Overlapping schedule
97.What is standard error
98.Estimate percent of violent videos
99.Election forecasting
100.Linear regression and overfitting
101.Statistical basics
102.How to get unbiased ratings
103.Sampling
104.Estimate mean and median in bootstraping
105.Estimate waiting time
106.Writing functions
107.Find the shortest path
108.Give an integer and find the length
109.Input list and return valide
110.System file processing
111.Youtube views
112.贝叶斯
113.Product metrics
114.Product metrics
115.Youtube product sense
116.SQL window function
117.SQL aggregation
118.SQL aggregation
119.SQL window function
120.SQL window function
121.Business case % revenue
122.数学coding
123.假设检验问题
124.Simulation
125.OLS的assumption
126.参数和方差问题
127.概率随机问题
128.Bootstrap
129.Distribution check
130.Sampling bias
131.Logistic regression vs OLS
132.Imbalance data
133.Hypothesis testing
134.Failure rate investigation
135.Linear regression线性回归feature importance
136.Mean和median
137.Top 100 and bottom 100
138.Linear regression线性回归
139.Pre-post analysis
140.Simpson's paradox
141.Simpson's paradox
142.AB testing
143.Simpson's paradox
144.颜色survey
145.Truncated normal distribution
146.Length of longest continuous increasing subarray
147.Binomial distribution
148.Even和odd 位置的各自sum
149.Occurance of odd and even
150.Normal distribution
151.扔骰子N次概率
152.Youtube product sense
153.鞋店metrics问题
154.Linear regression线性回归
155.Graph (t, x)
156.Probability
157.Explain and improve test result
158.Youtube product sense
159.Pros and cons of flag bad content on youtube
160.Sum of even and odd index
161.How to prove unfair
162.Probability
163.Measure impact of what we have done with no ab test
164.Test new features
165.Explain what AUC is to non-technical audience
166.How to reduce standard error
167.Product sense; ab test
168.如何提前predict unique user per month
169.Will colleague degree will earn more salary
170.Linear regression线性回归
171.Calculating P-Value and Probability
172.Impact of YouTube Ads on Sales
173.Analyzing Decrease in Searching Time per User per Session
174.Modeling to Compare Two Vendors
175.Assumptions for Unbiased Estimation
176.Derive Maximum Likelihood Estimation (MLE) Formulas
177.Experimentation for a New Google Search Feature
178.Predicting Potential Paid Users for Google Play
179.Google Meet Product Success Metrics
180.Predicting the Next Word
181.Coding (Python/ SQL)
182.Measurement and Modeling Concepts
183.Applied Analytics and Experiments
184.Estimating the Number of Unique Queries in a Daily Search Log
185.Comparing OLS Models with Original and Transformed Features
186.Analyzing the Impact of a Music App Feature on Driving Speed
187.Relevance of a Feature in a Linear Model
188.Estimating Coefficients in a Linear Model
189.Reducing the Width of a Confidence Interval
190.Write a statistical simulation for coin tosses.
191.How to reduce the margin of error if the sample size cannot change?
192.Assess the impact of YouTube ad plays on sales.
193.Develop a metric for segregation among three types of birds in a forest.
194.Explain Simpson's Paradox and provide a specific example.
195.Predicting Specific Business Outcomes
196.Model Building Experience
197.SQL Technical Skills Assessment
198.Feature Selection in Machine Learning Design
199.Machine Learning Basics
200.Explain p-value in statistics
201.Chat Group Word Count
