1.Click through rate and probability of clicks.
2.Title: Addressing Home Feed Staleness Complaints in Focus Groups
3.Cross-Platform Daily User Count and Growth Rate Calculation
4.Thoughts on Threefold Increase in Complaint Volume
5.Exploring the Relationship between Cost per Click and Advertiser Churn: A Deep Dive
6.Measuring and Testing Engagement on Billboard Ads
7.Approaches to Excluding Null Values in Dataframes
8.How long should the experiment be conducted?
9.Output List of Fresh Dates Based on Two Lists
10.User Impression Ratio for Fresh Pins within 7 Days
11.Defining fresh content: metrics and methods
12.What is p-value?
13.Defining a Metric for Homefeed Post Variety
14.Analyzing the Pros and Cons of a Stand-Alone Shopping App for Pinterest.
15.Comparing Freshness of Two Lists of Impression Dates
16.Finding the Percent of Users who Saw Fresh Content.
17.Pin Freshness: Evaluating User Views of New vs Old Content.
18.Evaluating a Novel ML Model for Content Recommendations.
19.Calculating Daily User Engagement
20.Replace Zeros with Column Means and Standardize Matrix Using sklearn
21.Customer Analysis Based on City and Country Fields
22.Return Countries with at Least Two Different Users
23.Selecting rows with missing values in age column of a dataframe
24.Metrics, Treatment Groups, and Experiment Duration: Best Practices
25.Function to Compare Freshness between Two Homefeeds with Dates
26.Calculate percentage of users who viewed fresh pins.
27.Defining and Displaying Fresh Pins.
28.Users by Location: Count, Order, and Total
29.Calculating the Standard Deviation of an Array using NumPy
30.Finding Countries with 2+ Business Users and Unknown Cities
31.Considerations for selecting sample size.
32.Difference between t-test and z-test.
33.Evaluating Freshness Impact & AB Testing Guidelines.
34.Function to Determine the Fresher List of Date Impression
35.Calculating Percent of Users Viewing Fresh Content Daily
36.Filtering Out Null Values in DataFrames
37.Find Fresh Content Viewership Percentage from Two Tables.
38.Pinning Freshness: Gauging User Engagement in Pinterest
39.Evaluating the Impact of a New Algorithm on Fresh Content: Key Considerations and Techniques.
40.Calculating Percentage of Users Seeing Fresh Pins
41.Experimental Design and Metric Setting: A Guide.
42.Boosting Engagement on Homefeed with Fresh Pins: Experimentation and Analysis
43.Fresh Pin Daily Impression Rate among Users
44.What is P-value?
45.Power Analysis: Impact of Sample Size and Element Changes
46.Ad Load Analysis by Country and Day for Non-Spam UsersAnswer e
47.Explaining p value in layman's terms.
48.Designing, Measuring, Randomizing, Timing, and Analyzing Experiments
49.Click Probability and Significance Testing Explanation.
50.Ad Load Analysis by Country and Day in 2020
51.Building and Evaluating a Click-through Prediction Model
52.Dealing with Missing Data: Best Practices
53.Launching Image Search: Metrics, Randomization & Experiment Duration
54.Homefeed monotony: Quantifying and solving user complaints.
55.Matrix Coordinate Neighbor Count
56.Trial success rate
57.P-value
58.Prob of getting >=5 clicks
59.Hypothesis testing CTR
60.Promoted and non-spam
61.Probability
62.Evaluate model performance
63.Variety level
64.Shopping ap
65.AB testing
66.Freshness
67.Complaint volume up
68.Advertiser churn
69.Freshness of Pins
70.谁先到家
71.Impression table
72.Handle missing values
73.Ads load
74.Impression table
75.相连相等数字
76.Missing value imputation
77.Fresh content
78.Customer table
79.Detecting Soft 404 System Design
80.Top K Elements Coding Problem
81.User Embedding Design
82.Modeling Temporal Features
83.Sampling Based on Scores Without a Defined Range
84.Describe the process and analyze the results of an experiment that involved changing the content viewed by users.
85.Find all posts violated on a specific date
86.Identify policies violated by a given post id
87.Find all posts violating a specific policy
88.Gradient Descent Implementation
89.Calculating Sigmoid Activation Function
90.Regularization Penalties Based on Zeroed Out Coefficients
91.Ensemble Learning Considerations
92.Bootstrap Aggregation Rationale
93.Case Study Metrics and Statistical Tests
94.Python/R Data Structure Familiarity
95.SQL Data Assumptions
96.Member and Pin Embedding Generation
97.Causal Inference and Test Statistics
98.Python Coding Problem
99.SQL Window Functions and CTEs
100.Coding Challenge
101.Machine Learning Knowledge
102.Machine Learning System Design
