1.Generating sorted output using f(x) with a sorted list
2.Effective Customer Segmentation Strategies for Business Travelers
3.Visualizing LI Friend Connections and Analyzing Central Tendency
4.Visualizing Height Distribution in America: Men vs Women
5.Predicting User Hires using Imbalanced, Regularized, and Tree-based Models
6.Calculating Variance and Proportions with Large Datasets
7.Properties of a Set of Correlated Variables X1...XP
8.Handling Imbalanced Data: Downsample and Upsample Techniques
9.Simulating Dice Roll Probability Distribution with my_function(p)
10.Identifying Business Travelers in COVID Era: Data Collection and Follow-up
11.Choosing the Dynamic Distribution of Two Bank Branches' Five Tellers
12.为Python实现代码
13.N-sided Dice Probability Function
14.1. Regularized Loss Function for Linear Model 2. Validating the Model Created from Sampling Big Data 3. Overcoming Imperfect Salary Data for Analysis 4. Choosing a Linear Model for Imperfect Data
15.Eliminating Duplicate Elements in Merged Lists with O(N) Time Complexity
16.Product of Array Except Self in Linear Time
17.Defining Overfitting and Underfitting: Coping with Overfitting
18.Explaining Decision Trees, Random Forests and Gradient Boosting: Feature Importance Calculation and Information Entropy
19.Two CSV files with Clicks and Assignments Data
20.Flawed Logic in Coin Toss Bet
21.Job Posting Analysis: New, Repeat, Reactivated Posts.
22.Measuring Bay Area Income: Choosing Metrics for Analysis
23.Analyzing the Drop in Job Application Volume.
24.Designing A/B Testing for Multiple Email Formats
25.Diagnosing the Case of a Dropped Job Application
26.Correcting Overfitting: Techniques and Strategies.
27.Understanding AUC: Measuring Model Performance in Analytics
28.Recall and Precision Explained
29.Best Matrices for Binary Classification Model?
30.Understanding Gradient Descent: An Overview
31.Bagging vs. Boosting: Understanding the Key Differences
32.Understanding Overfitting and Underfitting in Machine Learning
33.Exploring the Trade-off Between Bias and Variance
34.Analyzing Decreased Physical Store Sales and Traffic
35.Assessing Assumption That Customers Ignore Ads After 5th View
36.Comparing Variance of Bootstrap Sampling: median vs 95%
37.Can Lasso be solved using Linear Programming? If not, how to adjust?
38.Counting Pairs with Sum Below a Given Value
39.how could you improve linkedin
40.member ld
41.Lowest Common Ancestor of Deepest Leaves
42.SQL query
43.AB Test
44.CDF and probability
45.SQL window function
46.排列组合问题
47.Business travel question
48.Distirbution
49.Multimodal distribution
50.SQL query
51.Sample data ml building
52.排列组合
53.SQL aggregation funciton
54.Tradeoff
55.Measure changing search results from list to box
56.SQL aggregation functions
57.AB Test
58.Job application rate changes
59.P-value expaination
60.AB Test - novelty effect
61.AB Test
1. Generating sorted output using f(x) with a sorted list
f(x) = a*x**2 + b*x + c, 给一个sorted list，用发f(x) generate一个sorted output。
2. Effective Customer Segmentation Strategies for Business Travelers
how to do customer segmentation(business traveller)
3. Visualizing LI Friend Connections and Analyzing Central Tendency
Q1. 每个人在LI都有好友（比如a有100个好友，b有200个好友，c有500个好友），画出number of connections的分布图 （右偏）
Q2. 问了下mean的值range，给出原因
Q3. mean, median, mode比大小，并给出原因
4. Visualizing Height Distribution in America: Men vs Women
Q1. 画出美国男人身高的分布
Q2. 画出美国女人身高的分布
Q3. 把男人女人合并在一起，分布是怎样
5. Predicting User Hires using Imbalanced, Regularized, and Tree-based Models
data: 1 million rows, 10 K feature, someone was hired or not for a position. Predict the likelihood of a user being hired for a position. Imbalanced dataset (undersampling, oversampling, what's the difference between over- and under-sampling), regularization, training/validation dataset split, other models ( tree-based models, decision tree disadvantages, random forest VS boosting tree models, parameters in the random forest model).
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