如何成為數據科學家

目錄 (Table of Contents)

Introduction

介紹

Variety of Skills

各種技能

Uniqueness

獨特性

Impact

影響力

Remote

遠程

Pay

工資

Summary

摘要

介紹 (Introduction)

As we inch further into the year, I have seen more and more postings for data science positions, especially on LinkedIn, and other similar job-posting sites. After an expected lull due to current events, companies have figured out their budget and focus. Some of those companies include newer data science positions that they need to hire as soon as possible or in the near future.

隨著時間的流逝，我看到越來越多的數據科學職位發布，尤其是在LinkedIn和其他類似的職位發布網站上。 在由于當前事件而出現預期的停頓之后，公司已經確定了預算和重點。 其中一些公司包括較新的數據科學職位，他們需要盡快或在不久的將來聘用這些職位。

There are several reasons for becoming a data scientist. I am going to highlight five main reasons I became a data scientist, and hopefully, it can align with some of the reasons why you would become one as well.

成為數據科學家有幾個原因。 我將重點介紹我成為數據科學家的五個主要原因，并希望它可以與您也成為數據科學家的一些原因保持一致。

各種技能 (Variety of Skills)

As with many positions that have any general set of expected skills, data science is no exception, and can usually be thought to have these skills that I will outline below. Of course, there are others, but I will focus on the skills I come across the most at various companies as a data scientist.

與許多具有一般預期技能的職位一樣，數據科學也不例外，通?？梢哉J為我具備以下這些技能。 當然，還有其他人，但是我將重點介紹我作為數據科學家在各種公司中最常遇到的技能。

• Python (R)

  Python(R)

— the heavily debated Python versus R is usually controversial, but ultimately, it just depends on what the company is already using as their main programming language. Sometimes, data scientists can work alone and form models and output results directly to a stakeholder, and usually refer more to R in this case. However, in my experience, it has been easier to work cross-functionally with both data engineers and software engineers with the use of Python. This language is oftentimes used for deployment purposes, so, it can be easier to start with Python from the start. The benefit is that in the process of learning data science, you will learn Python or R, which will help you earn a variety of skills that can support you better down the road if you chose a different career path such as software development.

—爭論激烈的Python與R通常是有爭議的，但最終，它僅取決于公司已將其用作主要編程語言。 有時，數據科學家可以單獨工作，并直接將模型和結果輸出給利益相關者，在這種情況下，通常會更多地參考R。 但是，以我的經驗來看，使用Python與數據工程師和軟件工程師進行跨功能的工作變得更加容易。 該語言通常用于部署目的，因此從一開始就可以更輕松地使用Python。 好處是，在學習數據科學的過程中，您將學習Python或R，這將幫助您獲得各種技能，如果您選擇了不同的職業道路，例如軟件開發，這些技能將為您提供更好的支持。

• SQL

  SQL

— another popular skill for data scientists is SQL. Sometimes, online courses and universities neglect to stress the importance of how widely used this language is for data scientists. It is nearly used for every project I work on because the dataset is not simply given to you. You have to make your own dataset, and that involves querying your database tables with SQL. Like Python (and somewhat R), learning SQL is useful not only for data science but for data engineering and data analytics as well.

—數據科學家的另一項流行技能是SQL。 有時，在線課程和大學忽略了強調這種語言對數據科學家的廣泛使用的重要性。 它幾乎用于我從事的每個項目，因為數據集不是簡單地提供給您的。 您必須創建自己的數據集，這涉及使用SQL查詢數據庫表。 像Python( 和R )一樣，學習SQL不僅對數據科學有用，而且對數據工程和數據分析也很有用。

• Business

  商業

— while this skill is not a programming language, it is still important. Business, more so a concept, is something every data scientist learns. Similarly to SQL, it is not taught in education settings nearly as much as it should. What I mean by the business is that you need to really get used to jumping into situations that are not strictly just data science. The business uses data scientists to either make a process more efficient or find insights that will change the business in the future. Oftentimes, education for data science will focus so much on obtaining the highest accuracy for say, segmenting different types of customers. It can be great to achieve 98% accuracy, but if you are not able to come up with a plan for how you would implement the model and its results thereafter, then your model is useless.

—盡管此技能不是編程語言，但它仍然很重要。 業務，更是一個概念，是每個數據科學家都學習的東西。 與SQL相似，它在教學環境中的教授程度也不盡如人意。 我的業務意思是，您需要真正習慣于跳入嚴格不只是數據科學的情況。 該業務使用數據科學家來提高流程效率或尋找可改變未來業務的見解。 通常，數據科學教育將重點放在獲得最高準確度上，例如，細分不同類型的客戶。 達到98％的準確性可能會很棒，但是如果您無法針對如何實現模型及其結果制定一個計劃，那么您的模型將毫無用處。

You need to know that stakeholders, CEO’s, C-Suite/higher leadership, will ask what you will do with your results to change the business. So in turn, you would want to apply those customer segmentation groups to a marketing campaign through various, targeted emails. Then, you would create a test of some sorts to see how the emails performed, say with an AB test. As you can see, just having an extremely accurate model is just one part of the data science and business process. Practicing this business process over and over again is extremely beneficial.

您需要知道，利益相關者，CEO，C-Suite /高層領導會問您將如何處理結果以改變業務。 因此，您又希望通過各種針對性的電子郵件將這些客戶細分組應用于營銷活動。 然后，您將創建某種測試，例如AB測試，以查看電子郵件的性能。 如您所見，只有一個非常準確的模型只是數據科學和業務流程的一部分。 一遍又一遍地實踐這個業務流程是非常有益的。

• Statistics

  統計

— there was more focus on statistics in school, and it can prove to solve many problems for a data scientist. Knowing statistics is critical for data scientists, as it is the foundation of machine learning models. Practicing analysis of variance, or population sampling, etc, is useful in several forms of the business, say marketing campaigns again, or AB testing.

—在學校，人們更加關注統計數據，它可以證明可以解決數據科學家的許多問題。 了解統計數據對于數據科學家至關重要，因為它是機器學習模型的基礎。 進行方差分析或總體抽樣等，對幾種形式的業務很有用，例如再次進行營銷活動或AB測試。

獨特性 (Uniqueness)

The growing field of data science may, at first, seem that the position is not as unique as it used to be. However, it is still just as unique, and even more unique at the specific company you will be working at. There may be other roles like security engineers that could possibly be more unique, but data science is one-of-a-kind.

起初，數據科學領域的發展似乎似乎并不像以前那樣獨特。 但是，它仍然是唯一的，甚至在您將要工作的特定公司中也更加獨特。 可能還有其他角色(例如安全工程師)可能會更加獨特，但是數據科學是獨一無二的 。

• Small Headcount

  小人數

To expound on small headcount, software engineers, where even a small tech company can be comprised of near 30 developers, will usually have anywhere from one to four data scientists. When your role is this unique, you can learn valuable skills, touch multiple departments, and impact your company significantly. That is not to say the other aforementioned fields lack these benefits, but I do believe you are more likely to encounter various parts of the business in data science. Ultimately, you will feel great about your everyday work. This benefit leads me to my next point — impact.

為了說明人數少的問題，即使是一家小型科技公司也可以由近30名開發人員組成的軟件工程師，通常將擁有1-4名數據科學家。 當您的角色如此獨特時，您可以學習寶貴的技能，聯系多個部門并顯著影響公司。 這并不是說上述其他領域沒有這些好處，但我確實相信您更有可能在數據科學領域遇到業務的各個部分。 最終，您將對日常工作感到滿意。 這種好處將我引向我的下一個要點-影響。

影響力 (Impact)

After working as a data scientist at multiple companies, it has become clear that even just one project can impact a business indefinitely with significant benefits.

在多家公司擔任數據科學家之后，很明顯，即使只有一個項目也可以無限期地對企業產生重大利益。

The impact a data scientist can make is outstanding. You can automate previously manual processes, saving the company thousands or even millions of dollars. You can save your company time, and allocate time better spent. The projects you will work on are various in nature and importance.

數據科學家可以產生的影響是杰出的。 您可以使以前的手動流程自動化，從而為公司節省數千甚至數百萬美元。 您可以節省公司的時間，并分配更好的時間。 您將從事的項目的性質和重要性各不相同。

For example, I worked on a project that automated a large portion of a manual process, with high accuracy. It was truly amazing to feel how impactful you can be on the business. The best feeling, however, is the impact you can make on society, health, etc. There are countless ways to have a positive impact on something with data science, and your day-to-day work is no exception.

例如，我參與了一個項目，該項目以很高的準確性使手動過程的大部分自動化。 感覺到您對企業的影響力真是太神奇了。 然而，最好的感覺是您可以對社會，健康等產生的影響。通過數據科學，有無數種方法可以對事物產生積極影響，而且您的日常工作也不例外。

遠程 (Remote)

Before the current state of The World, remote work was already a prevalent benefit of tech roles, especially that are in data science. Unfortunately, there are several types of careers that cannot benefit from this point, which I admire extremely and am thankful for.

在當前的世界狀態之前，遠程工作已經是技術角色的普遍利益，尤其是在數據科學領域。 不幸的是，有幾種類型的職業不能從這一點中受益，我對此表示非常欽佩和感謝。

If you like to work from home, then data science will be an excellent opportunity for you. There are severe tools and platforms that aid in creating a successful environment without a physical office. You can use video conferencing, messaging, and project management as well as versioning tools. Tools include, but are not limited to:

如果您喜歡在家工作，那么數據科學將是您的絕佳機會。 有嚴格的工具和平臺可幫助您在沒有物理辦公室的情況下創建成功的環境。 您可以使用視頻會議，消息傳遞和項目管理以及版本控制工具。 工具包括但不限于：

* Zoom* Slack* GitHub* Jira* Confluence

Working from home is personally a huge benefit for me. I see it as an opportunity to enjoy my day more. Living in a city that can give you hours of traffic can be not the best feeling, so being able to eliminate that completely is an enormous positive.

個人而言，在家工作對我來說是一個巨大的好處。 我認為這是一個享受我的一天的機會。 在一個可以給您帶來數小時交通流量的城市里生活并不是最好的感覺，因此能夠完全消除這種狀況是巨大的積極意義。

工資 (Pay)

Yes, data science pays well. I wanted to make sure I included this as the last benefit, as not only is it well known already, but it is not the most important factor in deciding on a career. While more money is great, if you do not like your field, then you will be miserable. However, if you enjoy data science, and expect to build your brand and career in it, then you can expect to have high payouts.

是的，數據科學的收益很高。 我想確保將這作為最后的好處，因為這不僅已經廣為人知，而且不是決定職業的最重要因素。 雖然更多的錢是偉大的，但是如果您不喜歡自己的領域，那么您將很痛苦。 但是，如果您喜歡數據科學，并希望在其中建立自己的品牌和職業，那么您可以期望獲得很高的回報。

According to Glassdoor [2], the average base pay for a data scientist is $113,309 / yr.

根據Glassdoor [2]，數據科學家的平均基本工資為每年113,309美元。

Of course, there are variants between states and even cities in those states, so you can expect different ranges depending on where you live. Some companies offer large bonuses annually as well. Because your role is incredibly impactful, you can also expect shares or stocks in a company at some companies.

當然，各州之間甚至各州的城市之間都存在變體，因此您可以根據自己的居住地預計會有不同的范圍。 一些公司每年也提供巨額獎金。 由于您的角色具有令人難以置信的影響力，因此您還可以期望某些公司的公司股票或股票。

Additionally, depending on the job description or job functionality, you can expect variations in salary. Points to consider when negotiating for a data scientist salary include, but are not limited to:

此外，根據職位描述或職位功能，您可以期望薪水有所不同。 談判數據科學家薪金時要考慮的要點包括但不限于：

• skills (SQL, Python, R, etc)

  技能(SQL，Python，R等)

• seniority

  資歷

• who you report to

  向誰報告

• undergraduate or master’s/Ph.D. required

  本科或碩士/博士學位 需要

• years of experience

  多年經驗

• machine learning expected/deployment

  機器學習預期/部署

• data engineering expected

  預期的數據工程

摘要 (Summary)

Photo by Patrick Perkins on Unsplash [3]. Patrick Perkins在Unsplash上拍攝的照片[3]。

As you can see, there are several reasons for becoming a data scientist, especially in 2020. The top five reasons to become a data scientist are: the variety of skills you will learn along the way, uniqueness in your company, impact on your company, remote — work from home, and pay. Data science may not go away for a while and could very well become even more of a popular career. It is important to keep in mind that there are branches of data science like business intelligence, software engineering, and machine learning that are also great careers. Hopefully, you will become a data scientist, and will at least experience these five beneficial reasons for yourself.

如您所見，成為數據科學家的原因有很多，尤其是在2020年。成為數據科學家的五個原因是：您將學習的各種技能，公司的獨特性，對公司的影響，遠程-在家工作并付費。 數據科學可能不會消失一陣子，并且很可能會成為流行的職業。 重要的是要牢記，諸如商業智能，軟件工程和機器學習之類的數據科學分支也是很不錯的職業。 希望您將成為數據科學家，并且至少會為自己經歷這五個有益的原因。

I hope you found this article interesting and useful. Thank you for reading! Feel free to comment down below your experience or reach out to me!

希望您覺得本文有趣而有用。 感謝您的閱讀！ 隨意在您的經歷下留言或與我聯系！

翻譯自: https://towardsdatascience.com/the-top-5-reasons-to-become-a-data-scientist-cc5492e8cdd7

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