调查发现，数据挖掘和分析技能已经在“2016年最希望学会的职业技能”排名中位列第二，不难看出，数据科学正成为一种流行职业。
从行业内部的蓬勃发展，到招聘会上HR们的大声宣传，我们都可以看出，数据科学家已经成为当下最炙手可热的职业之一。 行业专业人士分析，未来几年数据科学岗位缺口将达到数百万。不仅仅是顶级科技公司需要数据科学家，未来各行各业都会需要设立数据科学相关岗位，来帮助他们发展。 工作岗位有了，但是可以做这些工作的人却很少。数据科学行业急需人才，但是现实情况却不容乐观，虽然大批大批的人涌入科技行业，但是他们大部分人都不知道，除了编程、计算机和工程学之外，还有别的高科技岗位。今年早些时候，都柏林大学设立了数据科学学士点，以满足行业对数据科学家的大量需求，这是爱尔兰首个数据科学学士点。 那么数据科学家是干什么的呢?哪些地方需要数据科学家?怎么样才能成为数据科学家?如果你正因为这些问题而犹豫要不要开始学习数据科学，那么我可以告诉你，成为数据科学家其实非常简单。 首先，你需要知道什么是数据科学。简单来说，数据科学是一门交叉学科，而数据科学家需要学会数据读取与分析，根据数据提取信息，实现数据可视化，以各种各样的方式和角度分析解读数据。
现在你已经知道了数据科学家是干什么的了，接下来就可以培养自己成为一名数据科学家了。下面让我们用图文相结合的方式，8步教你变身数据科学家。
第一步：学好数据学、数学和机器学习
数据科学不过就是和数字打交道的学科而已，所以你需要温习一下你的数学知识。数据科学家还需要掌握分析和展示技巧，正确解读数据并准确分析出数据表达的信息。
第二步：学会编程
数据科学家还需要具备其他的技巧，如计算机，端对端研发，用Python语言、R语言或者SAS编程。
第三步：熟悉数据库
数据科学家每天与数据打交道，当然还需要熟练使用数据库软件，如Oracle和MySQL。
第四步：了解数据科学家工作流程
在你具备了必要的技能之后，接下来你需要熟悉数据科学家的工作流程，从一开始收集数据，到最后完成分析报告，中间需要经过哪些环节呢?
第五步：提高大数据水平
开始处理网络平台数据，需要了解大数据处理过程与其他数据处理过程的不同之处，并且熟悉如何存储数据与处理数据。
第六步：成长、联络、学习
同时，你还需要去参加比赛，挑战自己提高水平;主动接触数据科学行业的专家，去发掘你自己对这个行业的兴趣方向;找到一个喜欢的项目，增加数据科学实践，提高数据表达能力;培养职业直觉，找到理论与实践结合点。
第七步：完全投入
以上几步都完成之后，你就可以开始实战演练了。通过实习和训练营，一步一步成为一名真正的数科学家。
第八步：加入大数据社区
加入大数据社区，及时了解大数据行业新闻讯息，牢牢把握大数据航向!
有这么多的公司需要数据科学家和分析师，只要你身怀技艺，他们都会争着来讨好你，希望能把你纳入麾下。
英文原文：
How to become a data scientist in 8easy(ish) steps
With data mining and analytic skills rankingas the second most desirable skill of 2016, it’s clear that data science isbecoming a popular career track.
From rumblings in the industry to loudproclamations at recruitment fairs, data science has staked its claim as one ofthe most in-demand skill sets right now.
We’ve heard from industry professionalsthat there will be millions of jobs in data science over the next number ofyears. These roles will be required across a huge variety of sectors, not justin top tech companies.
The problem is that what we have in jobs,we currently lack in suitable candidates. The data science sector is crying outfor candidates and, while more people are moving towards the rapidly growingtech sector, they might not be aware of roles outside of coding, computing orengineering.
What does a data scientist do? Where arethe data science roles? How would someone even begin to train as a datascientist? If these questions are holding you back from considering a career indata science, you might be surprised to find out how straightforward yourtraining could be.
But first, you need to know what it is. Putsimply, data science is a multidisciplinary field that involves reading andprocessing data, extracting knowledge from that data, and being able tovisualise, analyse and explain that data in a comprehensive way.
Now that you know what a data scientistdoes, you can become one. Data science is all about maths and stats, so youwould need to brush up on your mathematical skills. A data scientist would alsoneed analytical and presentation skills. They would have to be able to read andunderstand data and analyse it successfully.
They would also require technical skillsinvolving computer science, end-to-end development, and coding with programssuch as Python, R and SAS. A data scientist would also need to be comfortablewith databases such as Oracle and MySQL.
DCU launched its BSc in data scienceearlier this year – the first of its kind in Ireland – to answer the industrycall for talent in the data science sector. This degree will train you in theskills we have outlined for you to enter the field of data science.
Once you have the skills you need, you needto understand the workflow of a data scientist, from the collection of data allthe way to the end report. You should also start connecting with industryprofessionals and develop your own intuition and interest in the field.Consider where you want to use your data science skills.
Once you’ve honed your skills, developedyour industry knowledge and grasped where your interest in the field lies, it’stime to develop your practical knowledge; from internships and graduatepositions up to your dream job.
With so many companies looking for datascientists and analysts, you’ll be spoiled for choice once you have the skillsand training you need.
