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假期快乐!超强面试资源等你Pick,先收藏!

专注AI分享 AI科技大本营 2018-10-26



整理 | Jane

出品 | AI科技大本营


【导读】准备面试不是一件简单的事情,本文的作者在过去一段时间先后参加 50 多次面试。过程是艰难的,但是在这个过程中也积累了一些非常有用的资源。今天 AI科技大本营就为大家整理了那些有价值的学习资源,希望也可以帮助到你。大家可以先收藏,留到以后慢慢研究,假期快乐!



数据科学相关面试资源


首先希望大家可以通过下面的这些资源里的问题在面试前进行扫盲。


数据科学面试指导,包含 120 多个真实面试问题,附答案和面试技巧


Data Science Interview Guide.Contains 120 real interview questions, plus select answers and interview tips.

http://www.datasciencequestions.com


学习数据科学


Learn Data Science

https://datascienceinterview.quora.com


数据科学相关面试


The Data Science Interview

https://www.thedsinterview.com


109 个常问问题


109 Commonly Asked Data Science Interview Questions

https://www.springboard.com/blog/data-science-interview-questions/


数据科学面试技巧


What are tips for data science interviews

https://www.quora.com/What-are-tips-for-data-science-interviews-1


应聘 Airbnb 数据科学家要如何做准备


How do I prepare for a phone interview for a data scientist position with Airbnb

https://www.quora.com/How-do-I-prepare-for-a-phone-interview-for-a-data-scientist-position-with-Airbnb


Coding 资源


数据科学家也无法逃脱一些算法编程题,这里推荐一些可以练习问题类似,难易不同的算法题,大家可以在这些网站上练习来提高算法编程能力。


LeetCode 


https://leetcode.com/problemset/all/?difficulty=Medium


HackerRank 


https://www.hackerrank.com


算法分析


Algorithms —— GeeksforGeeks

https://www.geeksforgeeks.org/fundamentals-of-algorithms/


50 个电话面试中会问到的编程问题


Top 50 Programmer Phone Interview Questions with Answers

https://javarevisited.blogspot.com/2015/02/50-programmer-phone-interview-questions-answers.html#ixzz50bD5qdQ4


189 个问题及答案解析(书籍)





数据分析


如果在面试过程中,面试官了解到你有独立分析、解决问题和写代码的能力,你可能有机会做一些更具体的数据分析工作。这里主要介绍以 Python 语言为主的一些学习资源。


学习 Pandas 的 资源 Top 8


Top 8 resources for learning data analysis with pandas

http://www.dataschool.io/best-python-pandas-resources/


关于 Python 的 100 个面试问题与解答


100 Data Science in Python Interview Questions and Answers for 2018

https://www.dezyre.com/article/100-data-science-in-python-interview-questions-and-answers-for-2018/188


40 个检测你 Python 编程技能的问题


40 Questions to test your skill in Python for Data Science

https://www.analyticsvidhya.com/blog/2017/05/questions-python-for-data-science/


Python 系列教程


Data Scientist with Python Track

https://www.datacamp.com/tracks/data-scientist-with-python


没有编程经验的 Python 学习教程


Welcome to the Python Tutorial

https://community.modeanalytics.com/python/


Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython(书籍)





SQL


在数据分析相关工作的面试中都会问到对 SQL 了解与掌握情况,有的是在电话面试中问到,有的是在编程题中进行考察,后者的可能性更大。问题的难度差别很大,下面推荐了3篇文章。


数据分析中要学习的 SQL 教程


The SQL Tutorial for Data Analysis

https://community.modeanalytics.com/sql/tutorial/introduction-to-sql/

About Analytics Training

https://community.modeanalytics.com/sql/tutorial/sql-business-analytics-training/


数据科学面试中有助于练习 SQL 的好资源


What are some good resources to practice SQL for a data science interview

https://www.quora.com/What-are-some-good-resources-to-practice-SQL-for-a-data-science-interview


数据科学面试中关于 SQL 的面试问题


How To Ace Data Science Interviews: SQL

https://towardsdatascience.com/how-to-ace-data-science-interviews-sql-b71de212e433



统计学和概率论


统计对数据科学家来说就非常重要了。在我参加的面试中,首先都检测我是否可以用简单明了的语言来解释一个普通的统计数字或概念。不过随着经验的积累,后续更实用的 A/B 测试会代替这些传统的统计问题。这部分先给大家介绍一些统计学的学习资源。


数据科学面试中要准备哪些关于统计学问题


How should I prepare for statistics questions for a data science interview?What topics should I brush up on

https://www.quora.com/How-should-I-prepare-for-statistics-questions-for-a-data-science-interview-What-topics-should-I-brush-up-on


揭开 P 值的帷幕


Pulling Back The Curtain on P-Values (or How I Learned To Love Small Data)

https://conversionxl.com/blog/pulling-back-curtain-p-values-learned-love-small-data/


单尾测试 vs 双尾测试


One-Tailed vs Two-Tailed Tests (Does It Matter?)

https://conversionxl.com/blog/one-tailed-vs-two-tailed-tests/


用示例解释数据科学的概率基础


Basics of Probability for Data Science explained with examples

https://www.analyticsvidhya.com/blog/2017/02/basic-probability-data-science-with-examples/


机器学习


机器学习涉及很多内容,有很多概念,下面这些文章希望有助于大家更好的掌握重要的知识。


机器学习知识记忆卡


Machine Learning Flashcards

https://machinelearningflashcards.com


如何为机器学习准备数据


How to Prepare Data For Machine Learning

https://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/


如何评估机器学习算法


How to Evaluate Machine Learning Algorithms

https://machinelearningmastery.com/how-to-evaluate-machine-learning-algorithms/


构建有效机器学习模型的 4 个步骤


4 steps in building effective machine learning models

https://www.allerin.com/blog/4-steps-in-building-effective-machine-learning-models


如何准备机器学习面试


How To Prepare For A Machine Learning Interview

https://blog.udacity.com/2016/05/prepare-machine-learning-interview.html


不同分类算法的优点


What are the advantages of different classification algorithms?

https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms


应用机器学习的过程


Applied Machine Learning Process

https://machinelearningmastery.com/process-for-working-through-machine-learning-problems/


41 个机器学习面试的基本问题(附答案)


41 Essential Machine Learning Interview Questions (with answers)

https://www.springboard.com/blog/machine-learning-interview-questions/


在预测模型中如何处理缺失值的问题


How can I deal with missing values in a predictive model

https://www.quora.com/How-can-I-deal-with-missing-values-in-a-predictive-model


机器学习中偏差-方差的介绍


Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning

https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/


建立推荐系统的 5 个步骤


5 steps to setting up a recommender system

https://www.klipfolio.com/blog/recommender-system


15 个小时的专家视频对机器学习进行深入的介绍


In-depth introduction to machine learning in 15 hours of expert videos

https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/


A/B 测试


A/B 测试不是面试中必会问到的问题,作者在 udacity 上参加了免费的学习课程,如果有需要的同学也可以参加一下。


A/B 测试掌握程度测试:从初学者到专业


A/B Testing Mastery: From Beginner To Pro in a Blog Post

https://conversionxl.com/blog/ab-testing-guide/#what-is-ab-testing-and-how-does-it-work


A/B 测试免费学习课程


A/B Testing | Udacity

https://cn.udacity.com/course/ab-testing--ud257


A/B 测试相关面试题


A/B Testing Interview Questions

https://www.tutorialspoint.com/ab_testing/ab_testing_interview_questions.htm


19 个A/B 测试面试题及答案


Answers to the 19 Most Frequently Asked Questions About A/B Testing

https://blog.hubspot.com/blog/tabid/6307/bid/33466/answers-to-the-19-most-frequently-asked-questions-about-a-b-testing.aspx


停止 A/B 测试,需要多少次转换


Stopping A/B Tests: How Many Conversions Do I Need?

https://conversionxl.com/blog/stopping-ab-tests-how-many-conversions-do-i-need/


显著性不等于有效性


Statistical Significance Does Not Equal Validity (or Why You Get Imaginary Lifts

https://conversionxl.com/blog/statistical-significance-does-not-equal-validity/


如何纠正错误的 A/B 测试结果


How Segmentation Can Correct Misleading A/B Testing Results

https://apptimize.com/blog/2014/06/how-segmentation-can-correct-misleading-ab-testing-results/


推荐阅读


这部分内容可以让大家了解到招聘公司或者面试官想要什么样的人才?具备哪些能力?这些都是先于所有技能、技术之上的,因此在你准备面试过程中,了解这些也是非常重要的,所以千万不要忽视它们。


申请数据科学相关工作的一些建议


Advice For Applying To Data Science Jobs

http://hookedondata.org/Advice-for-Applying-to-Data-Science-Jobs/


获得数据科学家工作的两个方面


The two sides of Getting a job as a Data scientist

https://towardsdatascience.com/the-two-sides-of-getting-a-job-as-a-data-scientist-a4571acc58bc


基于 Twitter 进行数据科学工作


Doing Data Science at Twitter

https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6


数据产品经理相关


Trey Causey,Data Product Manager

http://treycausey.com/data_science_interviews.html


战胜它!帮你获得一份数据科学的工作


Crushed it!Landing a data science job

http://www.erinshellman.com/crushed-it-landing-a-data-science-job/


招聘一个数据科学家


Hiring a data scientist

https://blog.wikimedia.org/2017/02/02/hiring-data-scientist/


我在面试中会问什么问题?


Question I'm asking in interviews

https://jvns.ca/blog/2013/12/30/questions-im-asking-in-interviews/


教你创建一份优秀数据科学家的简历


Creating a great data science resume

https://will-stanton.com/2015/07/15/creating-a-great-data-science-resume/


最后希望这些资源可以帮助到大家!收藏了以后慢慢看,先去过节~祝大家十一假期愉快!


原文链接:

https://towardsdatascience.com/the-big-list-of-ds-ml-interview-resources-2db4f651bd63?source=userActivityShare-e0d936e72577-1538276250



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