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发帖数: 1 | 1 前两天提到了大家对DA/DS存在的误区，分享了想申请DA/DS岗位，要从哪些方面去准备
的部分要素。
"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
今天将继续分享，SQL, Product Sense, Project, Extra Skills 该如何准备！"
## 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。
2. 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
* 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 https://lnkd.in/gvahtsN
Intro to SQL https://lnkd.in/giWs-3N
Complete SQL Bootcamp https://lnkd.in/gsgf_fF
Data Visualization Medium https://lnkd.in/g3FSRgY
* 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?
大多是围绕着 metrics 和如何提高 product performance 来展开的。说实话这些对于
new grads 来说非常不友好因为没有工作经验。
2. 相关资料准备
* Product school: https://www.productschool.com/。 貌似是各个湾区的 tech 公
司的 DS 或 PM 大佬们来分享一些案例和学习经验。
* Metrics:https://www.cooladata.com/19-metrics-every-mobile-games-needs-
track/
一个关于 Game metrics 的汇总
* Critical metrics every product manager must track (https://
productcoalition.com/critical-metrics-every-product-manager-must-track-
c5f1e46e3423)
* A/B testing:A/B 测试中 20 个必须知道的问题
* A Collection of Data Science Takehome Challenges: 可以让你有机会解决很多实
际的 DS 问题，并且也和 product 有关。
一些个人想法:
大多的产品问题都是围绕着产品的 metrics 或运营中遇到的问题展开的。如果针对互
联网 行业来说， 一个典型的产品要从推出后经历以下阶段:user acquisition -->
user engagment / retention --> monitization。
讲一个异曲同工的事件来帮助理解吧。了解头条系公司的都知道，公司内有一个流水线
作 业的产品工厂模式。他们只有三个最核心的职能部门，技术，user growth 和商业
化。技术是保障了整体的运营，所以在整体的产品很稳定之后，就要看一下用户的留存
率。如果 发现用户的留存率(retention)或使用率(engagement)非常高，则进入 user
growth 的 推动环节，大批量的 marketing 来拉动新的用户增长(user acquisition)
。最后把商业化 (广告等)的内容接入产品，迅速变现。虽然顺序和我说的不太一样，
但是实际上就是在 在这三个部分对一个产品进行不断的迭代。所以 metrics 也离不开
这三个部分。user acquisition 讲求新用户注册率，user engagement 讲求 DAU(日活
量)，monitization 讲 求 LTV 和 ARPDAU(Average revenue per daily active user)
等等。逐渐加深对互联网 产品的理解，就能更好的应对 metrics 的问题。面试中
metrics 的问题，本质上一切都是为了产品迭代和用户增长以及变现。
关于产品中被问到的 A/B testing 的问题，很多是面试官为了考察你是否能针对特 定
的情况来分析 A/B testing 的结果。只看 P-value 是非常学生的思维，具体情况中的
A/B testing 是要从一开始的 new feature 的想法到后来设计整体的实验再到分析结
果再到最后给建议的一整个流程。面试官想要的是一个批判思维，需要你对每一个步骤
都扣的非常细 致，以此来确保实验和你的结论之间没有断层。
## 六、 Project
1. Projects/Competitions - Kaggle Kernels
https://www.kaggle.com/
2. Problem Solving Challenges - HackerRank
https://lnkd.in/g9Ps2cb
## 七、ExtraSkills
1. Communication - Data Storytelling
https://lnkd.in/gtiCSNT
2. Business Analytics- Geckoboard
https://lnkd.in/g2X-Xtp
3. NLP - How to solve 90% of NLP
https://lnkd.in/gh8bKe4
4. Recommendation Systems - Spotify
https://lnkd.in/gH2GQKu
5. Time Series Analysis - Complete Guide
https://lnkd.in/gFZU2Rb
**[急招内推机会 - New Grads]**
1. 全球机构投资公司。通过培养研究，创新和协作的文化，努力在所有市场环境中提
供一致，互不相关的绝对回报。公司致力于产生金融与技术的交汇，将领先的投资组合
和金融分析的深厚行业知识与软件工程和定量研究相结合。利用团队的集体专业知识来
寻找新的投资机会，分析市场状况，最大程度地降低风险，并为投资伙伴提供优质的服
务。公司已在世界各地拥有500多名员工，拥护一种思想自由交流，促进职业发展与世
界一流的福利。
招聘 Entry Level [Investment Data Analyst]
全职起薪 $75000，Sponsor OPT/Ext/H1b
2. 南加品牌策划公司。将不同的世界和文化聚集在一起，在品牌和媒体创作者之间架
起了桥梁。从中国到洛杉矶到迪拜再到意大利，通过整合各行各业的营销人员，公司已
成为国际影响力营销行业的领导者，并拥有“雄心勃勃”的权利，以实现看似不可能的
事情。我们照顾我们的创作者，我们相信共同成长，因为我们知道只有在出色的人带领
下，品牌营销才能发挥最佳作用。
招聘 Entry Level [Tech Product Manager]
全职起薪 $80000，Sponsor OPT/Ext/H1b/GC
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。
招聘 Entry Level [Project Analyst]
全职起薪 $66000，Sponsor OPT/Ext/H1b
4. 美西湾区最大电子杂货商，通过与本地供应商合作，重新设计价值链并利用社会购
买力，正在彻底改变杂货店的业务。目前在不同地区，不同类别和不同种族之间的增长
速度都非常出色（同比增长6倍）。迄今为止，已经从DST，Blackstone，Tiger Global
，Lightspeed Ventures，Goodwater Capital，XVC和iFly等主要投资者那里筹集了4亿
美元以上的资金
招聘 Entry Level [Data Scientist]
全职起薪 $90000，Sponsor OPT/Ext/H1b/GC
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。
招聘 Entry Level [Data Analyst]
全职起薪 $72000，Sponsor OPT/Ext/H1b
[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;
* Answer product related inquiries.
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.
## 更多求职资料、更多New Grads Friendly内推机会，私信咨询！ |
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