[面试经验] 数据科学DS面经全[新人求米,后续分享力扣所有 SQL 答案]

DS 面试题目的几大分类
Statistics
Programming
General
Big Data
Python
R
SQL
Modeling
Behavioral
Culture Fit
Problem-Solving


[新人求加米!!! 米多后回报地里会分享自己手动总结的力扣所有 SQL 题目(按考点分类)以及答案!!!]

Statistics
1 What is the Central Limit Theorem and why is it important?
2 What is sampling? How many sampling methods do you know?
3 What is the difference between Type I vs Type II error?
4 What is linear regression? What do the terms P-value, coefficient, R-Squared value mean? What is the significance of each of these components?
5 What are the assumptions required for linear regression?
    There are four major assumptions:
1. There is a linear relationship between the dependent variables and the regressors, meaning the model you are creating actually fits the data,
2. The errors or residuals of the data are normally distributed and independent from each other,
3. There is minimal multicollinearity between explanatory variables, and
4. Homoscedasticity. This means the variance around the regression line is the same for all values of the predictor variable.
6 What is a statistical interaction?
7 What is selection bias?
8 What is an example of a dataset with a non-Gaussian distribution?
9 What is the Binomial Probability Formula?


Programming
1 With which programming languages and environments are you most comfortable working?
2 What are some pros and cons about your favorite statistical software?
3 Tell me about an original algorithm you’ve created.
4 Describe a Data Science project  in which you worked with a substantial programming component. What did you learn from that experience?
5 Do

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Curated Interview Questions from Top Companies

oblem Solving
1 How would you come up with a solution to identify plagiarism?
2 How many “useful” votes will a Yelp review receive?
3 How do you detect individual paid accounts shared by multiple users?
4 You are about to send one million emails. How do you optimize delivery? How do you optimize response?
5 You have a dataset containing 100K rows and 100 columns, with one of those columns being our dependent variable for a problem we’d like to solve. How can we quickly identify which columns will be helpful in predicting the dependent variable. Identify two techniques and explain them to me as though I were 5 years old.
6 How would you detect bogus reviews, or bogus Facebook accounts used for bad purposes?
7 How would you perform clustering on one million unique keywords, assuming you have 10 million data points – each one consisting of two keywords, and a metric measuring how similar these two keywords are? How would you create this 10 million data points table in the first place?
8 How would you optimize a web crawler to run much faster, extract better information, and better summarize data to produce cleaner databases?
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// 祝大家早日上岸

从中学到蛮多的，感谢奥~

超有用的信息！！ 感恩楼主

多谢分享， 非常有用。给你点个赞！

谢谢分享正需要sql相关的问题