假期快樂！超強面試資源等你Pick，先收藏！

整理 | Jane

出品 | AI科技大本營

【導讀】準備面試不是一件簡單的事情，本文的作者在過去一段時間先後參加 50 多次面試。過程是艱難的，但是在這個過程中也積累了一些非常有用的資源。今天 AI科技大本營就為大家整理了那些有價值的學習資源，希望也可以幫助到你。大家可以先收藏，留到以後慢慢研究，假期快樂！

數據科學相關面試資源

首先希望大家可以通過下面的這些資源里的問題在面試前進行掃盲。

▌數據科學面試指導，包含 120 多個真實面試問題，附答案和面試技巧

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

▌學習數據科學

Learn Data Science

https://datascienceinterview.quora.com

▌數據科學相關面試

The Data Science Interview

https://www.thedsinterview.com

▌109 個常問問題

109 Commonly Asked Data Science Interview Questions

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

▌數據科學面試技巧

What are tips for data science interviews

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

▌應聘 Airbnb 數據科學家要如何做準備

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 資源

數據科學家也無法逃脫一些演算法編程題，這裡推薦一些可以練習問題類似，難易不同的演算法題，大家可以在這些網站上練習來提高演算法編程能力。

▌LeetCode

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

▌HackerRank

https://www.hackerrank.com

▌演算法分析

Algorithms —— GeeksforGeeks

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

▌50 個電話面試中會問到的編程問題

Top 50 Programmer Phone Interview Questions with Answers

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

▌189 個問題及答案解析（書籍）

數據分析

如果在面試過程中，面試官了解到你有獨立分析、解決問題和寫代碼的能力，你可能有機會做一些更具體的數據分析工作。這裡主要介紹以 Python 語言為主的一些學習資源。

▌學習 Pandas 的 資源 Top 8

Top 8 resources for learning data analysis with pandas

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

▌關於 Python 的 100 個面試問題與解答

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 個檢測你 Python 編程技能的問題

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

▌沒有編程經驗的 Python 學習教程

Welcome to the Python Tutorial

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

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

SQL

在數據分析相關工作的面試中都會問到對 SQL 了解與掌握情況，有的是在電話面試中問到，有的是在編程題中進行考察，後者的可能性更大。問題的難度差別很大，下面推薦了3篇文章。

▌數據分析中要學習的 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/

▌數據科學面試中有助於練習 SQL 的好資源

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

▌數據科學面試中關於 SQL 的面試問題

How To Ace Data Science Interviews: SQL

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

統計學和概率論

統計對數據科學家來說就非常重要了。在我參加的面試中，首先都檢測我是否可以用簡單明了的語言來解釋一個普通的統計數字或概念。不過隨著經驗的積累，後續更實用的 A/B 測試會代替這些傳統的統計問題。這部分先給大家介紹一些統計學的學習資源。

▌數據科學面試中要準備哪些關於統計學問題

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

▌揭開 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/

▌單尾測試 vs 雙尾測試

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

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

▌用示例解釋數據科學的概率基礎

Basics of Probability for Data Science explained with examples

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

機器學習

機器學習涉及很多內容，有很多概念，下面這些文章希望有助於大家更好的掌握重要的知識。

▌機器學習知識記憶卡

Machine Learning Flashcards

https://machinelearningflashcards.com

▌如何為機器學習準備數據

How to Prepare Data For Machine Learning

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

▌如何評估機器學習演算法

How to Evaluate Machine Learning Algorithms

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

▌構建有效機器學習模型的 4 個步驟

4 steps in building effective machine learning models

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

▌如何準備機器學習面試

How To Prepare For A Machine Learning Interview

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

▌不同分類演算法的優點

What are the advantages of different classification algorithms？

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

▌應用機器學習的過程

Applied Machine Learning Process

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

▌41 個機器學習面試的基本問題（附答案）

41 Essential Machine Learning Interview Questions (with answers)

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

▌在預測模型中如何處理缺失值的問題

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

▌機器學習中偏差-方差的介紹

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/

▌建立推薦系統的 5 個步驟

5 steps to setting up a recommender system

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

▌15 個小時的專家視頻對機器學習進行深入的介紹

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 測試

A/B 測試不是面試中必會問到的問題，作者在 udacity 上參加了免費的學習課程，如果有需要的同學也可以參加一下。

▌A/B 測試掌握程度測試：從初學者到專業

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 測試免費學習課程

A/B Testing | Udacity

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

▌A/B 測試相關面試題

A/B Testing Interview Questions

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

▌19 個A/B 測試面試題及答案

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 測試，需要多少次轉換

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

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

▌顯著性不等於有效性

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

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

▌如何糾正錯誤的 A/B 測試結果

How Segmentation Can Correct Misleading A/B Testing Results

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

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