mirror of
https://github.com/LCTT/TranslateProject.git
synced 2025-03-21 02:10:11 +08:00
2016-9-27-16:54
This commit is contained in:
parent
0abc0064b3
commit
6702fc71de
@ -1,12 +1,16 @@
|
||||
Translating by wikiios
|
||||
Building a data science portfolio: Machine learning project
|
||||
构建一个数据科学投资:机器学习项目
|
||||
===========================================================
|
||||
|
||||
>This is the third in a series of posts on how to build a Data Science Portfolio. If you like this and want to know when the next post in the series is released, you can [subscribe at the bottom of the page][1].
|
||||
>这是如何构建一个数据科学投资系列文章的第三弹。如果你喜欢本文并且想知道本系列下一篇文章发布的时间,你可以订阅本文底[邮箱][1]。
|
||||
|
||||
Data science companies are increasingly looking at portfolios when making hiring decisions. One of the reasons for this is that a portfolio is the best way to judge someone’s real-world skills. The good news for you is that a portfolio is entirely within your control. If you put some work in, you can make a great portfolio that companies are impressed by.
|
||||
数据科学公司越来越多地研究投资并同时作出雇佣决定。原因之一就是一个投资是评判某人真实能力最好的方式。对你来说好消息是一个投资是完全在你掌控之中。如果你花些心思在其中,你就可以做出一个令公司印象深刻的投资。
|
||||
|
||||
The first step in making a high-quality portfolio is to know what skills to demonstrate. The primary skills that companies want in data scientists, and thus the primary skills they want a portfolio to demonstrate, are:
|
||||
做出高质量投资的第一步是了解应该展示哪些技能,
|
||||
|
||||
- Ability to communicate
|
||||
- Ability to collaborate with others
|
||||
|
Loading…
Reference in New Issue
Block a user