03.19.05-test 过渡到数据科学职业所需的知识
对具有专业技能的开发人员的需求是在上升 across the board, and companies are particularly interested in filling数据科学 roles。 A IBM最近的研究 projects that the number of work opportunities in数据科学 will increase 15% in the coming years, adding 364,000 openings by 2020。 It also predicts that demand for data scientists and engineers will grow by 39% in the next five years。
Why has数据科学 become so important to businesses? “The explosion of数据科学 is motivated by businesses capturing orders of magnitude more data about their customers, processes, products, and services than in the past, everything from website user behavior to turbine engine diagnostics,” says 克里斯·阿尔邦，数据科学家和机器学习卡片。 公司比以往任何时候都具有收集和存储大量信息的能力。 数据科学家负责将这些数据转换为可以为决策提供信息，降低风险并发现意外情况的见解。 Albon说：“要使这些数据有用，需要专门的知识和技能，介于统计，计算机科学和软件工程之间。”
As more companies rely on data to make better decisions, they’re hiring for positions in数据科学 to help them get there。 If you’re a developer interested in transitioning into a career in数据科学, here are a couple things to know about the role。
You’ll Learn New Things
When transitioning from a role as a developer to a position focused on data, your existing computer science and software engineering skills will be highly valuable。 However,数据科学 roles also require sophisticated statistical knowledge。 If you want to work in数据科学, you need to hone your modeling and machine learning chops。
Strong SQL skills are table stakes for data scientists and data engineers。 These roles also require competence in statistical modeling and machine learning tools, such as the Python 包含NumPy和scikit-learn的数据科学生态系统 or the rich statistical tooling of R (my own数据科学 toolkit of choice!)。
You Can Start Right Now
The most effective way to develop数据科学 skills may be to begin where you are right now。 It can be a challenge to get a new job with the title “data scientist” when you don’t have experience or a PhD in statistics, but you can still implement statistical techniques and try out machine learning methods in your job as a developer right now。 Steph Locke,数据科学 consultant/trainer at 洛克数据, emphasizes the data workforce shortage that she sees。 “Data science is growing because a lot of organisations have tons of data and not enough people to make all the worthwhile decisions off the back of it。 We get things wrong, we don’t have time, or we just don’t make the optimum choice。”
What are the benefits of you developing your数据科学 skills right where you are? You can make a difference in your own company by using rigorous statistical methods, and companies can fill their数据科学 needs by finding and training people with data-oriented mindsets from within their own organizations。 If this doesn’t quite work out that way for you, then no problem! You have experience and projects to 在寻求下一个机会时谈论。
You Need Creativity and Communication
It’s important to know that数据科学 work involves a high level of creativity and communication。 Data scientists are highly technical, but also often work closely with marketing, sales, and operations。 The ability to communicate effectively with people from diverse backgrounds is important。 This communication happens through data visualization, writing, and speaking。 In my own job, I communicate with people from software developers to product managers to sales people, and I need all of them to understand, for example, a model I have built: what it means, how to interpret its results, and how certain we can be in its predictions。
Being an effective data scientist also requires creative, strategic thinking。 Doing数据科学 isn’t just about optimizing a model’s predictive power。 In fact, grinding away to eke out slight improvements in accuracy would often be a bad way for me to spend my time! Instead, data scientists creatively consider which problems to work on and how best to get to actionable insights。
如果您正在考虑开始从事数据科学事业，那么现在是进行过渡的好时机。 有了这些想法，您现在对以数据为中心的角色的期望有了更多了解。 如果您有兴趣查看那里有哪些数据科学工作，请查看堆栈溢出作业，您现在可以在此处找到数百个数据科学家的列表。