Data Science is an interdisciplinary field that uses a combination of code, statistical analysis, and algorithms to gain insights from structured and unstructured data.

数据科学是一个跨学科领域，它结合使用代码，统计分析和算法来从结构化和非结构化数据中获取见解。

Let’s break this down.

让我们分解一下。

We’re all kind of familiar with data. It’s stored information. Anything we read online is data. Anything we do that is recorded can be a data point. So a “data scientist” is someone who works with data and uses a structured approach to find insight from a set of data. They do this in any number of fields, from healthcare, to marketing, to medical sciences. The focus of a data scientist is on mathematical models — statistics and algorithms. An algorithm can be defined as “a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.” You can think about an algorithm as a set of steps to follow in order to solve a problem, like a Rubik’s cube. If you think back to high school algebra, you might remember the formula for a line on a graph:

我们都非常熟悉数据。 它存储了信息。 我们在线阅读的都是数据。 我们所做的任何记录都会成为数据点。 因此，“数据科学家”是从事数据工作并使用结构化方法从一组数据中寻找见解的人。 他们在医疗，营销，医学等许多领域都做到这一点。 数据科学家的重点是数学模型-统计和算法。 可以将算法定义为“在计算或其他问题解决操作(尤其是计算机)中要遵循的过程或一组规则”。 您可以将算法视为解决问题的一组步骤，例如魔方。 如果回想起高中代数，您可能还记得图中的一条线的公式：

y = mx + b

y = mx + b

You can determine the slope of a line based on data points and this basic algebraic equation. If you start with two data points, you can predict what a “y” value would be, given an “x” value.

您可以根据数据点和此基本代数方程确定直线的斜率。 如果从两个数据点开始，则可以在给定“ x”值的情况下预测“ y”值。

From this we can use the equation to extrapolate an equation.

由此，我们可以使用方程式外推方程式。

Which will indicate that if we have an “x” value of 1, the algorithm provides a “y” value of 2.1.

这将表明如果我们的“ x”值为1，则算法提供的“ y”值为2.1。

This is basically the kind of problem that a data scientist tries to solve, but with things like what will make a customer purchase a product and how a stock portfolio will perform over time, which are much more complicated and involve way more factors than a simple algebra. They use code and other technologies to build these models, and are constantly working to improve their predictions. They are working for companies like Spotify, Yelp, and Google.

基本上，这是数据科学家试图解决的问题，但是诸如使客户购买产品的原因以及随着时间的推移股票投资组合的绩效之类的事情要复杂得多，涉及的因素要比简单的多。代数 他们使用代码和其他技术来构建这些模型，并一直在努力改善他们的预测。 他们为Spotify，Yelp和Google等公司工作。

The thing about Data Science, though, is that it is a new field that is still getting defined. While every company seems to want a Senior Data Scientist, the job descriptions can vary incredibly. It’s also a weird field where some companies want a super experienced person with a PhD and others are excited to employ someone at an entry level, someone who may have completed a Boot Camp. One thing I like about this field, is that if you study Data Science, you learn a bunch of skills that can be used in other, similar, roles. For example, a Data Analyst might need to know about statistics, data cleaning, Big Data, and APIs. A Data Engineer should understand the same things, and what a Data Scientist needs to do in order to support them, as well as be able to code efficiently in multiple languages (I use Python and SQL), understand Amazon Web Services, or another Cloud based platform, and other basic data related things.

但是，关于数据科学的问题是，这是一个仍在定义中的新领域。 尽管每个公司似乎都希望有一位高级数据科学家，但职位描述却千差万别。 这也是一个很奇怪的领域，有些公司希望拥有一名经验丰富的博士学位的人，而另一些公司则兴奋地聘请了入门级的人，这些人可能已经完成了新手训练营。 我喜欢这个领域的一件事是，如果您学习数据科学，就会学到很多可以在其他类似角色中使用的技能。 例如，数据分析师可能需要了解统计信息，数据清理，大数据和API。 数据工程师应该理解相同的事物，以及数据科学家需要做什么才能支持它们，以及能够以多种语言(我使用Python和SQL)进行高效编码，了解Amazon Web Services或其他云基础平台和其他与基础数据相关的事物。

Needless to say, there are a lot of opportunities and directions you can go in if you choose to learn Data Science. As a person working in data, you have the ability to provide insight to complex information about customers, you can help define how ethical your companies analytics or machine learning models are, you hold a lot of unique and interesting power. You are required to constantly be learning new things, solving new problems and troubleshooting odd inconsistencies.

不用说，如果您选择学习数据科学，可以找到很多机会和方向。 作为数据工作人员，您可以洞悉有关客户的复杂信息，可以帮助定义公司分析或机器学习模型的道德标准，并拥有许多独特而有趣的功能。 您需要不断学习新事物，解决新问题并解决奇怪的不一致问题。

If this is something you are interested in learning more about, you can check out TechCultivator on LinkedIn and Instagram. They are a company dedicated to helping underrepresented folks get rewarding data science and software development jobs through skill building, mentorship, networking and community.

如果您有兴趣了解更多信息，可以在LinkedIn和Instagram上查看TechCultivator。 他们是一家致力于通过技能建设，指导，网络和社区帮助代表性不足的人们获得有价值的数据科学和软件开发工作的公司。