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DA/DS 求职刷题指南(上)

最近很多小朋友来咨询DA/DS的岗位。后来发现大家对DA/DS存在很多的误区。很多人认为写好SQL和python就足够应对面试,但实际上,数据科学需要什么技能?需要从哪些方面去准备?面试过程中会问些什么?日常工作是怎样的?很多学生并不清楚。今天就写一篇帖子,来告诉大家想申请DA/DS的岗位,要从哪些方面去准备。


Data Scientist/Data Analyst 通常需要集中准备的分为以下几块内容:

  • Machine Learning

  • 统计,概率与 A/B testing

  • Online coding(Python + R)

  • SQL

  • Product sense

  • Project

  • Extra Skills

一、 MachineLearning 1. 常见面试问题

  • What is overfitting? / Please briefly describe what is bias vs. variance.

  • How do you overcome overfitting? Please list 3-5 practical experience. / What is 'Dimension Curse'? How to prevent?

  • Please briefly describe the Random Forest classifier. How did it work? Any pros and cons in practical implementation?

  • Please describe the difference between GBM tree model and Random Forest.

  • What is SVM? what parameters you will need to tune during model training? How is different kernel changing the classification result?

  • Briefly rephrase PCA in your own way. How does it work? And tell some goods and bads about it.

  • Why doesn't logistic regression use R^2?

  • When will you use L1 regularization compared to L2?

  • List out at least 4 metrics you will use to evaluate model performance and tell the advantage for each of them. (F1 score, ROC curve, recall, etc...)

  • What would you do if you have > 30% missing value in an important field before building the model?

2. 相关资料准备

  • coursera 上 Andrew Ng 的 Machine learning 课程: https://www.coursera.org/learn/machine-learning 算得上考古级别的课程了,内容有些老旧但是很经典,很适合商学院 BA 专业的从 0 开始 补齐 ML 的背景知识

  • 【15 hours of expert ML videos】: https://www.dataschool.io/15- hours-of-expert-machine-learning-videos/

  • 《ISLR》(一个免费链接直通车),入门神书

  • Practical Statistics for Data Scientists: 50 Essential Concepts》,很实用的一本书, 专讲一些细小知识,不深但是读完会感觉多了些对 ML 的理解。

  • Medium-Towards Data Science 专题,比如 Machine Learning 101 (https://medium.com/machine-learning-101)这个小专题,非常浅显易懂,适合初 学者用具象的方式理解抽象算法

  • StackOverflow(https://stackoverflow.com/)自然也是不能漏掉的,学 data 或者编程总 会遇到很细枝末节的问题,这些一般文章里没有,所以就需要求助社群的力量了。

  • DataCamp:Machine Learning A-Zhttps://lnkd.in/gXqdBsQ


二、统计,概率与A/Btesting 1. 常见面试问题

  • What is p-value? What is confidence interval? Explain them to a product manager or non-technical person.. (很明显人家不想让你回答: 画个正态分布然后两边各卡 5%

  • How do you understand the "Power" of a statistical test?

  • If a distribution is right-skewed, what's the relationship between medium, mode, and mean?

  • When do you use T-test instead of Z-test? List some differences between these two.

  • Dice problem-1: How will you test if a coin is fair or not? How will you design the process(有时会要求编程实现)? what test would you use?

  • Dice problem-2: How to simulate a fair coin with one unfair coin?

  • 3 door questions. (自行 google 吧,经典题之一)

  • Bayes Questions: Tom takes a cancer test and the test is advertised as being 99% accurate: if you have cancer you will test positive 99% of the time, and if you don't have cancer, you will test negative 99% of the time. If 1% of all people have cancer and Tom tests positive, what is the prob that Tom has the disease? (非常经典的 cancer screen 的题,做会这一道,其他都没问题了)

  • How do you calculate the sample size for an A/B testing?

  • 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?

  • 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?

  • If given the above 20% p-value, the product manager still decides to launch this new feature, how would you claim your suggestions and alerts?

2. 相关资料准备

  • A/B testing 的资料首推的是 udacity 上免费的 A/B testing(by Google)的课, 同学们的评 价都还不错,很适合全面的了解一下 A/Btesting。

  • 其余的 A/B testing 的内容大多来自于 Medium 上的好文,原因是 A/B testing 是一个 要和实际的业界应用场景结合的东西,只知道原理和基本不懂没啥区别。所以要去看 一看业界的人写的关于 A/B testing 的文章,只 da 有带着案例看,才能懂面试中的问 题都应该怎么样回答。

  • 还有就是如果有在工作的学长姐,长辈等等,一定要不吝啬的问 A/B 方面的问题。他 们说个十几二十分钟,能省下你很多时间去到处扒资料,原因同上条不解释。

  • Stats 的话,有一个非常快的捡起一些统计学基础的内容是 coursera 上 intro to stats and prob 课程,很快,一个下午就可以看完。

  • udemy 课程:Data Science Career Guide - Interview Preparation, 还是很不错的。课 程轻量,学起来无压力。

  • 概率题对于大多数中国学生来说都没问题,都是高中学过的,稍加捡起就行。udemy 的课就可以帮你捡起来


三、Online coding (Python+R)

1. 面试问题(这个考的五花八门,所以不敢说是最常见的)

  • Report the biggest sum of a continuous 3 numbers in a list? with the related index?

  • 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?

  • Please write a function to reverse the key and value in a dictionary. When you have repeated values, please only keep the first key as the new value.

  • Similarly to the "gather" and "spread" functions in the tidyr package, write a one by yourself and test it using XXX dataset.

  • Given a log file with rows featuring a date, a number, and then a string of names, parse the log file and return the count of unique names aggregated by month. (我的 不是这个原题,但是意思很像)

  • Using python to calculate a 30-day rolling profit. (大致就是要用 python 写一个 rolling window)

2. 相关资料准备

  • 算法自然是逃不过 Leetcode 了,Easy 和 Medium 水平的刷一刷有利无害。

  • Youtube 上讲算法的一些视频

  • 划重点,大家在面 online coding 的轮次之前,千万记得去 glassdoor 上看一下会不会 有人 share 一些题目。遇不到原题权当练手,遇到原题了的话简直不要太爽。 (glassdoor --> a company --> interview question --> title)

  • DataCamp:d Intro to Pythonhttps://lnkd.in/grCsv8v Intro to R https://lnkd.in/gKFiDZn Data Wrangling Pydata (90min)https://lnkd.in/gEhF3-W EDA (20min video)https://lnkd.in/gT8_RKh Stats/Prob (Khan Academy)https://lnkd.in/gsyGpVu

  • Udemy 家的两个课:Data Analysis with Pandas and Python 和 Python for Data Science and Machine Learning Bootcamp。 非常简单易懂,上手率非常高。

  • 一个好网站 real python

  • 手上如果还有书就更好了,甩给你们一些选项: https://realpython.com/best-python- books/




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