# DA/DS 求职刷题指南（下）- 含急招内推机会

Recap一下，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

## 四、SQL

1. 常见面试问题
• What is the difference between union and union all? where and having?
• Table【in_app_purchase】:
uid: unique user id.
timestamp: specific timestamp detailed to seconds. purchase amount: the amount of a one-time purchase.
This is a table containing in-app purchase data. A certain user could have multiple purchases on the same day

Question 1: List out the top 3 names of the users who have the most purchase amount on ‘2018-01-01’
Question 2: Sort the table by timestamp for each user. Create a new column named “cum amount” which calculates the cumulative amount of a certain user of purchase on the same day.
Question 3: For each day, calculate the growth rate of purchase amount compared to the previous day. if no result for a previous day, show ‘Null’.
Question 4: For each day, calculate a 30day rolling average purchase amount.

• Table【Friending】
time = timestamp of the action
date = human-readable timestamp, i.e, 20108-01-01
action = {‘send’, ‘accept’}
actor_id = uid of the person pressing the button to take the action target_id = uid of another person who is involved in the action.

Question: what was the friend request acceptance rate for requests sent out on 2018-01-01?

• 题目二涵盖了简单的 aggregate 问题，cumulative 问题，rolling window 问题等 等。搞定这些，其他的都只是一些简单变形。
题目三涵盖了 self-join，并且有一些 tricky 的大于等于号的应用，有兴趣可以在地 里查一下 Facebook 面经的解答。
其他的题目无非是多了一些 table，join 麻烦一些或者加了一些 case when，难度 都不会有太大的变化。做好几个经典题，然后自己整理好就可以以不变应万变了。
1. 相关资料准备
• 扫盲网站:SQL ZOO 和 W3schools，非常实用，适合翻阅。
• 两个 Udemy 的 SQL 课:SQL - MySQL for Data Analytics and Business Intelligence 和 The Ultimate MySQL Bootcamp
• 刷题的话，Leetcode上有一些题，可以做一下。还有好心人直接做了个整理，在这里: summary of sql in leetcode。
• Hackerrank 上的题自然是要全刷光的，因为难度非常简单，快的话一两天也许就做完了。
• DataCamp:
Data Gathering- Why API Medium What Is an API and Why Should I Use One? | by Tyler Elliot Bettilyon | Medium
Intro to SQL https://lnkd.in/giWs-3N
Complete SQL Bootcamp https://lnkd.in/gsgf_fF
Data Visualization Medium The 7 Kinds of Data Visualization People | by Elijah Meeks | Nightingale | Medium
• 更多的网站:18 best sql online learning resources
• 建议自己下载一个 My SQL 装到电脑上，模拟真实的 SQL 环境来学习。Mysql 里关于Windows function 和 frame clause 的教程: Windows function ，Frame Clause。这个非常重要，windows function 可以说是 SQL 面试里的大杀器，非常节省时间而且思 路清晰。
• 建议也学会用 WITH common_table_expression。可以让你的 SQL 看起来非常整洁和容易理解。
• 最最重要的来了。如果你觉得刷完题或者学完以上的内容就万事大吉了，那还真的不 是。我一开始也有这样的误区。实际上刷完 Hackerank 也并不能帮你很快的做出我给的例题。而其实，对于 metrics 或者 product 的了解能够帮助你很好的准备 SQL 面 试，因为所有的 SQL 面试都是围绕着与 business 相关的 metrics而展开的。举例而言，游戏公司一定会考 DAU(daily active user)或者 purchase rate, Facebook 就会是 friend request 相关的，以此类推。所以熟悉你申请公司的业务再针对性准备 SQL，一定会事半功倍。

## 五、Product sense

1. 常见面试问题
• Today you immediately notice that our app’s new users are doubled. What could be the reason? Do you think it’s good or not?
• If we have an app with in-app purchase, name at least 4 metrics you would like to monitor in your dashboard.
• If you are running an A/B testing and find that the result is very positive, thus you decide to launch it. In the first 2 weeks, the performance of our website is very positive./However, with time flying by, all metrics seem to go back to normal. How will you explain this result?
• Assume we are Facebook and we would like to add a new ‘love’ button. should we do this?
• We are running 30 tests at the same time, trying different versions of our home page. In only one case test wins against the old home page. P-value is 0.04. Would you make the change?
• 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?
• Assume that you are assigned to estimate the LTV(lifetime value) of our game app player. what kind of metrics would you like to calculate so as to make a good prediction?/Assume that you already collect all that you want. How would you make this prediction/estimation?
• If you got a chance to add on new features for our app to increase our profit within a very short term. What will you do?

1. 相关资料准备

## 六、 Project

1. Projects/Competitions - Kaggle Kernels
https://www.kaggle.com/
2. Problem Solving Challenges - HackerRank
HackerRank

1. Communication - Data Storytelling
https://lnkd.in/gtiCSNT
How to Analyze Data: A Basic Guide | Geckoboard blog
3. NLP - How to solve 90% of NLP
https://lnkd.in/gh8bKe4
4. Recommendation Systems - Spotify
How Does Spotify Know You So Well? | by Sophia Ciocca | Medium
5. Time Series Analysis - Complete Guide
https://lnkd.in/gFZU2Rb

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3. Silicon Valley从事风险投资，私募股权，投资银行和咨询行业的技术投资公司。主要在IPO前约1-4年投资于成长型公司和后期阶段的公司。团队包含顾问委员会，由一群杰出的成功企业家和风险资本家组成。加入公司将亲身接触风险资本，私募，投行和咨询行业，并通过研究和尽职调查评估市场潜力和投资机会。

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5. AI金融服务创业公司，汽车财务平台，帮助人们优化拥有汽车的成本和体验。从帮助优化汽车保险成本开始，建立一个由机器学习驱动的个人化服务，寻求节省固定成本的方法，协商更好的利率和文书工作，以自动切换和节省资金。公司由连续创业企业家成立，曾建造并扩展了YourMechanic，截至目前已筹集了超过5000万美元的资金。

[Job Descriptions/Requirements]

Investment Data Analyst

• Partnering with investors to respond to and address their data needs;
• Developing a keen understanding of how data is utilized in our investment processes to generate insights and ideas;
• Designing and implementing programmatic data accuracy, outlier detection, error correction and remediation processes;
• Evaluating new and differentiated data within the firm and helping strategically prioritize new data initiatives;
• Working closely with our Data Engineering teams and defining the on-boarding and production requirements for all new data;
• Must have a passion for data and experience in applying that passion to high quality data products;
• Must have the ability to perform data analysis and wrangle the data using Python and a strong understanding of time-series data, third party data vendors, and how they apply to quant and fundamental analysis;
• Prior experience with quantitative investors (either as a quant or vendor) is strongly preferred;
• Serve as an in-house expert on data, leveraging your knowledge of vendor and market data collection;
• Experience working in an agile environment and with development teams. Strong understanding of SQL and relational databases and familiar with AWS is a huge plus.

Tech Product Manager

• Monitor and analyze market trends;
• Study competitors’ services and products;
• Explore new ways of improving existing services and products;
• Provide product training and technical expertise;
• Identify and present innovative product solutions;
• Work with development leads so that product requirements are understood;
• Work with project management software;
• Work within a software development methodology like AGILE;
• Coordinate product releases with marketing, sales, and development teams;

Project Analyst

• Performing financial projections through the input and review of income, operating expenses, capital budgets;
• Evaluating potential investments with respect to the financial return on investment;
• Assisting in the preparation of preliminary investment summaries;
• Assisting with due diligence review and coordinating project closings;
• Experience in working with minimal direction from supervisor and take initiative to follow up on projects and /or assignments. Make decision and resolve problems with minimal supervisor, and exercise good judgment with priorities;
• Experience using a range of organizational and time management skills to coordinate and prioritize a diverse, complex workload and to meet competing deadlines in a fast paced environment with high attention to detail.
• Strong communication and interpersonal skills;
• One or more of the following is a plus: 1) outstanding skill in designing, editing and reviewing professional documents, etc.; 2) fluency in Chinese (translation to and from English required); 3) strong research (company research, market research, etc.) capabilities.

Data Scientist

• Apply advanced knowledge of SQL and the ability to build complex modeling features;
*Build machine learning models that leverage our unique data sources to recommend optimal product, offer, content, and information;
• Build end-to-end infrastructure from exploring your data, designing, deploying, testing, to monitoring your own models;
• Help identify new opportunities by applying machine learning and statistical models for improved business outcomes;
• MS or Ph.D. or equivalent experience in a quantitative field such as computer or data science, math, statistics, or physics;
• Trackable experience in developing and deploying machine learning or deep learning models in a professional setting;
*Domains of expertise should include at least one of the following: collaborative filtering, content based recommender systems, link-click prediction, predictive customer targeting;
• Strong communication skills, and ability to work with multiple stakeholders.

Data Analyst

• Owner of the core company data pipeline, responsible for scaling up data processing flow to meet the rapid data growth;
• Consistently evolve data model & data schema based on business and engineering needs;
• Implement systems tracking data quality and consistency;
• SQL and MapReduce job tuning to improve data processing performance;
• Proficient in SQL, especially with Postgres dialect preferred;
• Expertise in Python, BI software (preferably Metabase or Tableau), Hadoop preferred.