从微博段子说起，微博上关于数据分析有两个段子，我经常当作案例讲，第一个段子，说某投资商对某企业所属行业有兴趣，要做背景调查，甲是技术流，一周分析各种网上数据，四处寻找行业材料，天天熬夜，终于写出一份报告；乙是人脉流，和对方高管喝了次酒，请对方核心人员吃了顿饭，所有内幕数据全搞定，问谁的方法是对的；第二个段子，某电商发现竞争对手淘宝店，周收入突然下降了30%，但是隔周后又自然恢复，中间毫无其他异常现象，于是老板让分析师分析，苦逼的分析师辛苦数日，做各种数学模型，总算找到勉强的理由自圆其说，老板读毕，虽说不能让人信服，却也没有更合理的解释，某日，见对手老板，闲聊此事，“你们某段时间怎么突然收入下降？” “嗨，别提了，回家一趟，公司放羊了。”老板恍然大悟。
A Practical Intro to Data Science
A Practical Intro to Data Science
There are plenty of articles and discussions on the web about what data science is, what qualities define a data scientist, how to nurture them, and how you should position yourself to be a competitive applicant. There are far fewer resources out there about the steps to take in order to obtain the skills necessary to practice this elusive discipline. Here we will provide a collection of freely accessible materials and content to jumpstart your understanding of the theory and tools of Data Science.
At Zipfian Academy, we believe that everyone learns at different paces and in different ways. If you prefer a more structured and intentional learning environment, we run a 12 week immersive bootcamp training people to become data scientists through hands-on projects and real-world applications. We also host a free Skillshare course covering much of this material at a high level.
We would love to hear your opinions on what qualities make great data scientists, what a data science curriculum should cover, and what skills are most valuable for data scientists to know. Share your thoughts over at Hacker News!
_While the information contained in these resources is a great guide and reference, the best way to become a data scientist is to make, create, and share!
Python Data Tools
Here are a few observations inspired by conversations I had during the just concluded PyData conference1.
The Python data community is well-organized: