By Kyra Wong and Kendall Kikkawa

黃凱拉(Kyra Wong)和菊川健多 ( Kendall Kikkawa)

什么是“數據科學”？ (What is ‘Data Science’?)

Data collection, an important aspect of “data science”, is not a new idea. Before the tech boom, every industry already had some sort of data system in place. Think of the government Census, or even medical records. So why is data science only recently becoming such a common career path?

數據收集是“數據科學”的重要方面，并不是一個新想法。 在技??術繁榮之前，每個行業都已經有了某種數據系統。 想想政府人口普查，甚至病歷。 那么，為什么數據科學只是在最近才成為如此普遍的職業道路？

To put it simply, Silicon Valley’s tech boom led to a massive data boom. All of a sudden, tech giants like Google and Facebook saw themselves with unprecedented amounts of data from their users. The next time you make a Google search, look under the search bar and you’ll find how many results were produced from that single word or phrase. All of that information is stored somewhere.

簡而言之，硅谷的技術繁榮導致了大規模的數據繁榮。 突然之間，像Google和Facebook這樣的科技巨頭看到了來自用戶的空前數據。 下次進行Google搜索時，請查看搜索欄下方的內容，您會發現該單個單詞或短語產生了多少個結果。 所有這些信息都存儲在某個地方。

Now think about this: There are over 2.7 billion monthly active Facebook users as of June 2020. Imagine your own Facebook profile with your pictures, statuses, friend lists, search histories, and apply that to Twitter, Instagram, Spotify, and other platforms you use most frequently. I think you get the picture.

現在考慮一下：截至2020年6月，每月有超過27億的Facebook活動用戶。想象一下您自己的Facebook個人資料，其中包含圖片，狀態，朋友列表，搜索歷史記錄，并將其應用于Twitter，Instagram，Spotify和其他平臺最經常使用。 我想你明白了。

Thus, the question quickly arose: What do the companies do with all of this data? They needed people to organize, clean, and interpret it, thus beginning Silicon Valley’s hasty search for data scientists.

因此，很快出現了一個問題：這些公司如何處理所有這些數據？ 他們需要人們進行組織，清理和解釋，從而開始了硅谷對數據科學家的倉促搜尋。

What does ‘data science’ mean, though? At family gatherings, every college kid knows the drill: What school do you go to, and what’s your major? Some say nursing, business, or even astrophysics. Everyone, for the most part, has a pretty good idea of what those majors imply.

但是，“數據科學”是什么意思？ 在家庭聚會上，每個大學生都知道這門課：您要去哪所學校，您的專業是什么？ 有人說護理，商業甚至是天體物理學。 在大多數情況下，每個人都對這些專業的含義有很好的了解。

On the flip side, I can’t even begin to count how many times I’ve had to explain what my Data Science major even means. So, I’ve broken it down to a script: “It’s a combination between the technical, or coding, aspect of computer science, the computational aspect of statistics, and strategic lens of business”.

另一方面，我什至無法開始計算我不得不解釋我的數據科學專業甚至意味著什么的次數。 因此，我將其分解為一個腳本：“它是計算機科學的技術或編碼，統計的計算方面以及業務的戰略視角之間的結合”。

I’ve said that exact sentence so many times now that I’ve considered putting it on a shirt.

我已經說了那么多次準確的句子，以至于我考慮過把它放在襯衫上。

為什么選擇伯克利的數據科學？ (Why Data Science at Berkeley?)

Now that you have a general understanding of what data science is, what sets Berkeley’s Data Science program apart from others? Well for one, we were one of the first undergraduate Data Science programs in the country. UC Berkeley’s introductory data science course, “Data 8” is now the largest class on campus, and it has inspired other universities such as Cornell, University of Chicago, NYU, and others to create their own versions of the course.

既然您已經對什么是數據科學有了大致的了解，那么什么使Berkeley的Data Science計劃與眾不同？ 不錯，我們是該國最早的數據科學本科課程之一。 加州大學伯克利分校的入門數據科學課程“ Data 8”現在是校園內最大的課程，它啟發了康奈爾大學，芝加哥大學，紐約大學等其他大學，創建了自己的課程版本。

Faculty from our rigorous Statistics program and world-renowned Computer Science program joined forces to construct the curriculum from the ground up. If your schedule lines up, you may be lucky enough to learn from John DeNero, a former senior research scientist at Google who played a major role in developing Google Translate, or from Ani Adhikari, a living legend at Berkeley who is simultaneously intimidating and brilliant.

我們嚴格的統計學課程和世界知名的計算機科學課程的教職員工共同合作，從頭開始構建課程。 如果您的日程安排合理，您可能有幸向曾在Google Translate開發中發揮重要作用的Google前高級研究科學家John DeNero或伯克利的活著傳奇人物Ani Adhikari學習，他同時具有威懾和輝煌。

As for the curriculum, it allows for a respectable degree of flexibility. You can choose to focus on computer science, mathematics, or take more statistics-heavy courses depending on your passions or strengths.

至于課程，它允許一定程度的靈活性。 您可以選擇專注于計算機科學，數學，或者根據自己的熱情或專長選擇更多的統計重磅課程。

Starting off with the lower-division courses also allows students to build a strong foundation towards delving deeper into the major. If you ask any data scientist, they would say that calculus, computer science, introductory data science, and linear algebra are all crucial to understand before going any further.

從低年級課程開始，還可以使學生為深入學習該專業打下堅實的基礎。 如果您問任何數據科學家，他們會說微積分，計算機科學，入門數據科學和線性代數對于進一步了解它們都是至關重要的。

With those building blocks in place, students can then start mastering the more advanced skills necessary for the industry. Probability, modeling and learning, human context and ethics, and computational depth are all upper division requirements for the major.

有了這些構建模塊，學生就可以開始掌握該行業所需的更高級的技能。 概率，建模和學習，人文環境和道德以及計算深度都是該專業的最高分科要求。

Personally, I find that the human context and ethics requirement is the most important, especially considering the moral responsibility that the tech industry found itself carrying with the collection of people’s data. With the advent of large companies like Facebook and Tiktok having broken headlines for controversial data usage, we can see the need for this requirement in real time. I go as far to say that every technology-related major at every university should have a similar human ethics requirement (you can read more about Data Ethics in our article here).

就個人而言，我發現最重要的是人文背景和道德要求，尤其是考慮到技術行業發現自己承擔著收集人的數據所承擔的道德責任。 隨著Facebook和Tiktok等大型公司的出現，有關爭議性數據使用的頭條新聞破滅，我們可以實時看到對這一要求的需求。 我去盡量地說，每一個技術相關的各高校主要應該有類似人類的倫理道德要求(你可以在我們的文章關于倫理學的數據在這里 )。

Berkeley’s data science major also includes a domain emphasis, which allows students to hone in on what particular sector of data science they want to pursue. This includes business analytics, chemistry, mathematics, physics, or even social welfare and poverty (you can check out the full list of domain emphases here).

伯克利(Berkeley)的數據科學專業還包括一個領域重點，使學生可以磨練自己想要追求的數據科學的特定領域。 這包括業務分析，化學，數學，物理學，甚至社會福利和貧困(您可以在此處查看領域重點的完整列表)。

Data science can be applied to practically any industry under the sun, and the Domain Emphasis is a great way for students to get a taste of what their data science work might look like in the real-world. Students thus get a more holistic and well-rounded education through this single degree. This requirement reflects the diversity of data science in industry, and it makes UC Berkeley Data Science majors even more hirable.

數據科學幾乎可以應用于陽光下的任何行業，“領域重點”是讓學生領略其數據科學工作在現實世界中的樣子的一種好方法。 因此，通過該單一學位，學生將獲得更全面和全面的教育。 這項要求反映了行業中數據科學的多樣性，這使加州大學伯克利分校數據科學專業的人才更加可租。

數據科學在校園中的參與 (Data Science Involvement on Campus)

Outside of the classroom, the number of opportunities to get involved with Data Science has grown rapidly, especially since the announcement of the Data Science Major a few years ago. The Data Science Discovery Program is one such opportunity; the program provides undergraduates with the chance to contribute to innovative data science research that reinforces a campus-wide commitment to social impact. Because data science is such an interdisciplinary field with a multitude of real-world applications, this program gives students the chance to apply their knowledge and skills in a domain they are truly passionate about.

在課堂之外，參與數據科學的機會數量Swift增加，尤其是自幾年前宣布數據科學專業以來。 數據科學發現計劃就是這樣一個機會。 該計劃為大學生提供了機會，為創新的數據科學研究做出貢獻，從而加強了整個校園對社會影響的承諾。 由于數據科學是一個跨學科領域，具有許多實際應用，因此該計劃使學生有機會在他們真正熱衷的領域中運用他們的知識和技能。

Past projects have used cardiac sensor data to help researchers at the UCSF Medical Center detect heart disease, applied machine learning to help small farmers increase their yields in the face of dynamic threats, and implemented learning algorithms to better understand urban environments and to reduce the negative environmental impact of large cities. (check out the Discovery Program to learn more about some of the projects that students have worked on: Data Science Discovery Program).

過去的項目使用心臟傳感器數據來幫助UCSF醫學中心的研究人員檢測心臟病，應用機器學習來幫助小農面對動態威脅來提高產量，并實施了學習算法以更好地了解城市環境并減少負面影響。大城市的環境影響。 (查看發現計劃，以了解有關學生從事的某些項目的更多信息： 數據科學發現計劃 )。

And as if the 40+ projects per semester in the Discovery Program weren’t enough, there are other organizations that support data science research too. Check out The Berkeley Institute for Data Science, the Berkeley School of Information, and Berkeley Artificial Intelligence Research for more!

似乎發現計劃每學期40個以上的項目還不夠，還有其他組織也支持數據科學研究。 進一步了解伯克利數據科學研究所 ， 伯克利信息學院和伯克利人工智能研究 ！

At Berkeley, faculty members and students are working together on cutting edge research. This emphasis on both academics and research helps prepare Berkeley Data Science students for the industry, academia, or anything within the radius of the data science realm!

在伯克利，教職員工和學生正在共同致力于前沿研究。 對學術和研究的重視有助于伯克利數據科學專業的學生為行業，學術界或數據科學領域內的任何事物做好準備！

Another great way to get involved with Data Science outside of specific classes is to join the course staff for your past classes! Some of Berkeley’s largest computer science and data science classes enroll over 1500 students every semester, and undergraduate student instructors are vital to the success of those departments. Joining the course staff allows you to help others develop a passion for data science, gain a deeper understanding of course material, and become further immersed in the Data Science community on campus.

參加特定課程之外的數據科學的另一種好方法是加入課程人員參加您以前的課程！ 伯克利最大的一些計算機科學和數據科學課程每學期招收1500多名學生，而本科生導師對于這些部門的成功至關重要。 加入課程人員可以使您幫助其他人發展對數據科學的熱情，加深對課程資料的了解，并進一步融入校園的數據科學社區。

Last but not least, you can join a student organization on campus! Data Science clubs have been multiplying by the dozens in recent years, providing students with chances to get involved in whatever they take a liking to.

最后但并非最不重要的一點是，您可以加入校園的學生組織！ 近年來，Data Science俱樂部的數量已增加了數十個，為學生提供了參與他們喜歡的事物的機會。

Specifically, our organization has two core committees: Projects and Education. Our Projects Committee partners with clients in various industries to uncover buried insights in their data and forge cutting-edge predictive models. Our Education Committee aims to expose high school students to the data science field through educational workshops. There are other data science organizations on campus that also give students the chance to get involved in data journalism, research, passion projects, and more!

具體來說，我們的組織有兩個核心委員會：項目和教育。 我們的項目委員會與各個行業的客戶合作，以??發現其數據中隱藏的見解，并建立最先進的預測模型。 我們的教育委員會旨在通過教育研討會使高中生接觸數據科學領域。 校園中還有其他數據科學組織，這些組織也使學生有機會參與數據新聞，研究，熱情項目等等！

There is certainly no shortage of opportunities on campus, but it is up to you to find what you’re passionate about, do your own research, and go get involved. The Data Science Department will definitely support you in whatever you choose!

在校園里當然不乏機會，但是要由您決定自己感興趣的事物，進行自己的研究并參與其中。 數據科學部門絕對會為您提供任何選擇支持！

伯克利與灣區的關系 (Relationship between Berkeley and the Bay Area)

UC Berkeley is about an hour’s drive north from where major tech companies like Google, Tesla, and Apple are headquartered. Cal’s celebrated academics have attracted aspiring tech workers and entrepreneurs from all around the world, and its proximity to this innovation hub, combined with its top-notch programs, have strengthened the relationship between the University and Silicon Valley.

加州大學伯克利分校(UC Berkeley)向北大約一個小時的車程，這些主要科技公司的總部位于Google，特斯拉和蘋果。 加州大學的著名學者吸引了來自世界各地的有抱負的技術工作者和企業家，而加州大學與這個創新中心的毗鄰以及一流的計劃加強了大學與硅谷之間的關系。

Berkeley career fairs are well attended by recruiters from top companies in the area, and many even host recruitment events and interviews on campus (although, we’ll see how this plays out in the virtual 2020–2021 Academic Year). This ongoing relationship has led to hundreds of Berkeley students landing jobs or internships at these big tech companies each year.

伯克利的招聘會吸引了該地區頂尖公司的招聘人員參加，許多招聘會甚至在校園內舉辦招聘活動和面試(盡管我們將在虛擬的2020-2021學年中看到這一點)。 這種持續的關系每年導致數百名伯克利大學的學生在這些大型科技公司找到工作或實習。

As more Berkeley graduates infiltrate Silicon Valley, Cal’s alumni network continues to grow, offering Cal students with more networking opportunities, and presenting them with more doors that may open up in the future.

隨著越來越多的伯克利大學畢業生滲透到硅谷，加州大學的校友網絡不斷發展，為加州大學的學生提供了更多的交流機會，并為他們提供了更多可能打開的大門。

Now you may be wondering, what if I don’t want to go work at a FAANG (Facebook, Apple, Amazon, Netflix, Google) company or any large tech company? There are tons of small companies in the Bay Area working on amazing things. Many of these companies are present on campus as well, through programs like Berkeley SkyDeck, QB3 Berkeley, the Citrus Foundry, and several others. The majority of these companies are looking for ways to leverage their data in new and exciting ways, and they are always looking for Berkeley students and graduates to help them do so.

現在您可能想知道，如果我不想去FAANG(Facebook，Apple，Amazon，Netflix，Google)公司或任何大型科技公司工作怎么辦？ 灣區有許多小型公司從事令人驚奇的事情。 通過Berkeley SkyDeck ， QB3 Berkeley ， Citrus Foundry等計劃，這些公司中的許多公司也都在校園內。 這些公司中的大多數都在尋找以新穎而令人興奮的方式利用其數據的方法，并且他們一直在尋找Berkeley的學生和畢業生來幫助他們做到這一點。

As the world becomes more full of data across industries, companies of all sizes across the Bay Area are seeking employees with skills in data science — Berkeley’s world-renowned reputation and its closeness to tech hubs puts us right at the heart of the world’s tech innovation.

隨著世界各行各業的數據越來越多，灣區的各種規模的公司都在尋找具有數據科學技能的員工-伯克利享譽全球的聲譽及其與技術中心的緊密聯系使我們處于世界技術創新的核心地位。

結論 (Conclusion)

While data collection and data analysis are not new concepts, “Data Science” is definitely emerging as the hot new thing in the tech industry because it encompasses the more traditional methods of data analysis, along with new techniques in the fields of Machine Learning, Artificial Intelligence, and more.

盡管數據收集和數據分析不是新概念，但“數據科學”無疑正在成為技術行業中的熱門新事物，因為它涵蓋了更傳統的數據分析方法以及機器學習，人工等領域的新技術。情報等等。

Whether data is being used to detect disease, analyze climate change, or recommend your next binge watch, there is a dire need for data scientists who can understand algorithms AND recognize the potential ethical threats they pose. Berkeley’s Data Science curriculum effectively meets both of these demands, and by offering flexibility within the major, Data Science students are able to pursue whatever they are most passionate about.

無論是將數據用于檢測疾病，分析氣候變化還是推薦您的下一個暴飲暴食，數據科學家都急切需要了解算法并認識到其構成的潛在道德威脅。 伯克利的數據科學課程有效地滿足了這兩個要求，并且通過提供專業內的靈活性，數據科學的學生能夠追求自己最感興趣的事物。

In summary, Berkeley makes Data Science accessible to all — because the Data Science major is not as competitive as others on campus, the department welcomes students from all backgrounds. Cal is certainly doing its part to empower the next generation of data scientists.

總而言之，伯克利使所有人都可以使用數據科學-因為數據科學專業的競爭力不如校園中的其他人，該部門歡迎來自各個背景的學生。 Cal肯定會盡自己的一份力量來授權下一代數據科學家。

Feel free to reach out to us if you have any feedback, or if you want to know more about the major. Also, follow us on Instagram @bigdata.berkeley and visit our website at bd.berkeley.edu if you want to learn more about Big Data at Berkeley!

如果您有任何反饋意見，或者想進一步了解該專業，請隨時與我們聯系。 另外，如果您想在伯克利了解更多有關大數據的信息，請在Instagram @ bigdata.berkeley上關注我們，并訪問我們的網站bd.berkeley.edu ！