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

數據科學是一個跨學科領域，它結合使用代碼，統計分析和算法來從結構化和非結構化數據中獲取見解。

Let’s break this down.

讓我們分解一下。

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:

我們都非常熟悉數據。 它存儲了信息。 我們在線閱讀的都是數據。 我們所做的任何記錄都會成為數據點。 因此，“數據科學家”是從事數據工作并使用結構化方法從一組數據中尋找見解的人。 他們在醫療，營銷，醫學等許多領域都做到這一點。 數據科學家的重點是數學模型-統計和算法。 可以將算法定義為“在計算或其他問題解決操作(尤其是計算機)中要遵循的過程或一組規則”。 您可以將算法視為解決問題的一組步驟，例如魔方。 如果回想起高中代數，您可能還記得圖中的一條線的公式：

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.

您可以根據數據點和此基本代數方程確定直線的斜率。 如果從兩個數據點開始，則可以在給定“ x”值的情況下預測“ y”值。

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

由此，我們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.

這將表明如果我們的“ x”值為1，則算法提供的“ y”值為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，這是數據科學家試圖解決的問題，但是諸如使客戶購買產品的原因以及隨著時間的推移股票投資組合的績效之類的事情要復雜得多，涉及的因素要比簡單的多。代數 他們使用代碼和其他技術來構建這些模型，并一直在努力改善他們的預測。 他們為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，關于數據科學的問題是，這是一個仍在定義中的新領域。 盡管每個公司似乎都希望有一位高級數據科學家，但職位描述卻千差萬別。 這也是一個很奇怪的領域，有些公司希望擁有一名經驗豐富的博士學位的人，而另一些公司則興奮地聘請了入門級的人，這些人可能已經完成了新手訓練營。 我喜歡這個領域的一件事是，如果您學習數據科學，就會學到很多可以在其他類似角色中使用的技能。 例如，數據分析師可能需要了解統計信息，數據清理，大數據和API。 數據工程師應該理解相同的事物，以及數據科學家需要做什么才能支持它們，以及能夠以多種語言(我使用Python和SQL)進行高效編碼，了解Amazon Web Services或其他云基礎平臺和其他與基礎數據相關的事物。

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.

不用說，如果您選擇學習數據科學，可以找到很多機會和方向。 作為數據工作人員，您可以洞悉有關客戶的復雜信息，可以幫助定義公司分析或機器學習模型的道德標準，并擁有許多獨特而有趣的功能。 您需要不斷學習新事物，解決新問題并解決奇怪的不一致問題。

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.

如果您有興趣了解更多信息，可以在LinkedIn和Instagram上查看TechCultivator。 他們是一家致力于通過技能建設，指導，網絡和社區幫助代表性不足的人們獲得有價值的數據科學和軟件開發工作的公司。