最近很多小伙伴来咨询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。今天就分享一篇帖子，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/B Testing

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 Python https://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/

### 剩餘內容，下集待续...

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