From c430acb471201c8b9e02c69ced00267861a332a4 Mon Sep 17 00:00:00 2001 From: Noobfish Date: Tue, 2 Aug 2016 21:12:45 +0800 Subject: [PATCH] Translating by noobfish since 2016.08.02 --- ...ng a data science portfolio - Machine learning project.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/sources/team_test/part 1 - Building a data science portfolio - Machine learning project.md b/sources/team_test/part 1 - Building a data science portfolio - Machine learning project.md index 70d9b65f92..439be578ce 100644 --- a/sources/team_test/part 1 - Building a data science portfolio - Machine learning project.md +++ b/sources/team_test/part 1 - Building a data science portfolio - Machine learning project.md @@ -1,3 +1,8 @@ ++@noobfish translating since Aug 2nd,2016. ++ ++ + + >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]. 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.