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干货|Data Science面试必考3类题!你都会吗?

Data Science数据科学,是最近几年火爆的求职领域。根据Glassdoor的数据:2020年美国十佳工作中,Data Science领域占了2个席位。

不论是从薪水,还是工作满意度来说,Data Science绝对是一个令人羡慕的职业领域。

小编今天给大家整理了3大DS/DA/DE必问三大类型面试题,对Data Science感兴趣的同学,不妨继续往下看!


Technical Interview



👉Name the types of Indexes of which are available in SQL.

👉What is the difference between union and union all? where and having?

👉Dynamic programming problem: Now you have 5 types of coins(1,2,3,5,8) and a total sum(a big number, say 589). How many different combinations of coins can you find to reach this total sum?

Coding题目自然逃不过Leetcode,多刷题有利无害。划重点:在面online coding的轮次之前,千万记得去Glassdoor上看一看有没有过去的面试者分享过面试题,万一就遇到原题了呢~


Math/Stats Interview


常考的概念: Probability (Conditional Probabilities), Bayes Theorem, Hypothesis Testing, Law of Large numbers, Normal distribution等等。


👉(Facebook真题) There are 50 cards of 5 different colors. Each color has cards numbered between 1 to 10. You pick 2 cards at random. What is the probability that they are not of same color and also not of same number?

👉Now assuming you have an A/B testing result reflecting your test result is kind of negative (i.e, p-value ~= 20%). How will you communicate with the product manager?

概率题对大多数中国学生都没问题,Coursera上Introducation to Statistics and Probability课很不错,可以快速复习一些统计学基础知识点:https://click.linksynergy.com/deeplink?id=skg/Sko/Ybo&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprobability-intro


Product Sense Interview

Product Sense是当下Data Science面试中最常见也是最难的一类题目。大多是围绕着metrics和如何提高product performance来展开的。说实话这些对于new grads来说非常不友好的因为没有工作经验。


  1. 如何给用户创造价值?

  2. 如何给公司创造价值?

  3. 如何衡量用户价值&公司价值?

  4. 如何根据数据结果进行改进?


👉What new feature would you implement for Product X? If it were launched, how would you measure the feature’s success?

👉If after running an A/B testing you find the fact that the desired metric(i.e, Click Through Rate) is going up while another metric is decreasing(i.e., Clicks). How would you make a decision?

这里有个很方便的网站,Product Schoolhttps://www.productschool.com/。是各个湾区的tech公司的DS或PM大佬们来分享一些产品案例和学习经验。