By Kyra Wong and Kendall Kikkawa

黄凯拉(Kyra Wong)和菊川健多 ( Kendall Kikkawa)

什么是“数据科学”？ (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?

数据收集是“数据科学”的重要方面，并不是一个新想法。 在技​​术繁荣之前，每个行业都已经有了某种数据系统。 想想政府人口普查，甚至病历。 那么，为什么数据科学只是在最近才成为如此普遍的职业道路？

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.

简而言之，硅谷的技术繁荣导致了大规模的数据繁荣。 突然之间，像Google和Facebook这样的科技巨头看到了来自用户的空前数据。 下次进行Google搜索时，请查看搜索栏下方的内容，您会发现该单个单词或短语产生了多少个结果。 所有这些信息都存储在某个地方。

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.

现在考虑一下：截至2020年6月，每月有超过27亿的Facebook活动用户。想象一下您自己的Facebook个人资料，其中包含图片，状态，朋友列表，搜索历史记录，并将其应用于Twitter，Instagram，Spotify和其他平台最经常使用。 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，很快出现了一个问题：这些公司如何处理所有这些数据？ 他们需要人们进行组织，清理和解释，从而开始了硅谷对数据科学家的仓促搜寻。

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，“数据科学”是什么意思？ 在家庭聚会上，每个大学生都知道这门课：您要去哪所学校，您的专业是什么？ 有人说护理，商业甚至是天体物理学。 在大多数情况下，每个人都对这些专业的含义有很好的了解。

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，我什至无法开始计算我不得不解释我的数据科学专业甚至意味着什么的次数。 因此，我将其分解为一个脚本：“它是计算机科学的技术或编码，统计的计算方面以及业务的战略视角之间的结合”。

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

我已经说了那么多次准确的句子，以至于我考虑过把它放在衬衫上。

为什么选择伯克利的数据科学？ (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.

既然您已经对什么是数据科学有了大致的了解，那么什么使Berkeley的Data Science计划与众不同？ 不错，我们是该国最早的数据科学本科课程之一。 加州大学伯克利分校的入门数据科学课程“ Data 8”现在是校园内最大的课程，它启发了康奈尔大学，芝加哥大学，纽约大学等其他大学，创建了自己的课程版本。

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.

我们严格的统计学课程和世界知名的计算机科学课程的教职员工共同合作，从头开始构建课程。 如果您的日程安排合理，您可能有幸向曾在Google Translate开发中发挥重要作用的Google前高级研究科学家John DeNero或伯克利的活着传奇人物Ani Adhikari学习，他同时具有威慑和辉煌。

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.

至于课程，它允许一定程度的灵活性。 您可以选择专注于计算机科学，数学，或者根据自己的热情或专长选择更多的统计重磅课程。

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.

从低年级课程开始，还可以使学生为深入学习该专业打下坚实的基础。 如果您问任何数据科学家，他们会说微积分，计算机科学，入门数据科学和线性代数对于进一步了解它们都是至关重要的。

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.

有了这些构建模块，学生就可以开始掌握该行业所需的更高级的技能。 概率，建模和学习，人文环境和道德以及计算深度都是该专业的最高分科要求。

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).

就个人而言，我发现最重要的是人文背景和道德要求，尤其是考虑到技术行业发现自己承担着收集人的数据所承担的道德责任。 随着Facebook和Tiktok等大型公司的出现，有关争议性数据使用的头条新闻破灭，我们可以实时看到对这一要求的需求。 我去尽量地说，每一个技术相关的各高校主要应该有类似人类的伦理道德要求(你可以在我们的文章关于伦理学的数据在这里 )。

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)的数据科学专业还包括一个领域重点，使学生可以磨练自己想要追求的数据科学的特定领域。 这包括业务分析，化学，数学，物理学，甚至社会福利和贫困(您可以在此处查看领域重点的完整列表)。

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.

数据科学几乎可以应用于阳光下的任何行业，“领域重点”是让学生领略其数据科学工作在现实世界中的样子的一种好方法。 因此，通过该单一学位，学生将获得更全面和全面的教育。 这项要求反映了行业中数据科学的多样性，这使加州大学伯克利分校数据科学专业的人才更加可租。

数据科学在校园中的参与 (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.

在课堂之外，参与数据科学的机会数量Swift增加，尤其是自几年前宣布数据科学专业以来。 数据科学发现计划就是这样一个机会。 该计划为大学生提供了机会，为创新的数据科学研究做出贡献，从而加强了整个校园对社会影响的承诺。 由于数据科学是一个跨学科领域，具有许多实际应用，因此该计划使学生有机会在他们真正热衷的领域中运用他们的知识和技能。

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).

过去的项目使用心脏传感器数据来帮助UCSF医学中心的研究人员检测心脏病，应用机器学习来帮助小农面对动态威胁来提高产量，并实施了学习算法以更好地了解城市环境并减少负面影响。大城市的环境影响。 (查看发现计划，以了解有关学生从事的某些项目的更多信息： 数据科学发现计划 )。

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!

似乎发现计划每学期40个以上的项目还不够，还有其他组织也支持数据科学研究。 进一步了解伯克利数据科学研究所 ， 伯克利信息学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，教职员工和学生正在共同致力于前沿研究。 对学术和研究的重视有助于伯克利数据科学专业的学生为行业，学术界或数据科学领域内的任何事物做好准备！

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.

参加特定课程之外的数据科学的另一种好方法是加入课程人员参加您以前的课程！ 伯克利最大的一些计算机科学和数据科学课程每学期招收1500多名学生，而本科生导师对于这些部门的成功至关重要。 加入课程人员可以使您帮助其他人发展对数据科学的热情，加深对课程资料的了解，并进一步融入校园的数据科学社区。

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点是，您可以加入校园的学生组织！ 近年来，Data Science俱乐部的数量已增加了数十个，为学生提供了参与他们喜欢的事物的机会。

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!

具体来说，我们的组织有两个核心委员会：项目和教育。 我们的项目委员会与各个行业的客户合作，以​​发现其数据中隐藏的见解，并建立最先进的预测模型。 我们的教育委员会旨在通过教育研讨会使高中生接触数据科学领域。 校园中还有其他数据科学组织，这些组织也使学生有机会参与数据新闻，研究，热情项目等等！

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!

在校园里当然不乏机会，但是要由您决定自己感兴趣的事物，进行自己的研究并参与其中。 数据科学部门绝对会为您提供任何选择支持！

伯克利与湾区的关系 (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.

加州大学伯克利分校(UC Berkeley)向北大约一个小时的车程，这些主要科技公司的总部位于Google，特斯拉和苹果。 加州大学的著名学者吸引了来自世界各地的有抱负的技术工作者和企业家，而加州大学与这个创新中心的毗邻以及一流的计划加强了大学与硅谷之间的关系。

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.

伯克利的招聘会吸引了该地区顶尖公司的招聘人员参加，许多招聘会甚至在校园内举办招聘活动和面试(尽管我们将在虚拟的2020-2021学年中看到这一点)。 这种持续的关系每年导致数百名伯克利大学的学生在这些大型科技公司找到工作或实习。

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.

随着越来越多的伯克利大学毕业生渗透到硅谷，加州大学的校友网络不断发展，为加州大学的学生提供了更多的交流机会，并为他们提供了更多可能打开的大门。

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.

现在您可能想知道，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办？ 湾区有许多小型公司从事令人惊奇的事情。 通过Berkeley SkyDeck ， QB3 Berkeley ， Citrus Foundry等计划，这些公司中的许多公司也都在校园内。 这些公司中的大多数都在寻找以新颖而令人兴奋的方式利用其数据的方法，并且他们一直在寻找Berkeley的学生和毕业生来帮助他们做到这一点。

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.

随着世界各行各业的数据越来越多，湾区的各种规模的公司都在寻找具有数据科学技能的员工-伯克利享誉全球的声誉及其与技术中心的紧密联系使我们处于世界技术创新的核心地位。

结论 (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.

尽管数据收集和数据分析不是新概念，但“数据科学”无疑正在成为技术行业中的热门新事物，因为它涵盖了更传统的数据分析方法以及机器学习，人工等领域的新技术。情报等等。

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.

无论是将数据用于检测疾病，分析气候变化还是推荐您的下一个暴饮暴食，数据科学家都急切需要了解算法并认识到其构成的潜在道德威胁。 伯克利的数据科学课程有效地满足了这两个要求，并且通过提供专业内的灵活性，数据科学的学生能够追求自己最感兴趣的事物。

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.

总而言之，伯克利使所有人都可以使用数据科学-因为数据科学专业的竞争力不如校园中的其他人，该部门欢迎来自各个背景的学生。 Cal肯定会尽自己的一份力量来授权下一代数据科学家。

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!

如果您有任何反馈意见，或者想进一步了解该专业，请随时与我们联系。 另外，如果您想在伯克利了解更多有关大数据的信息，请在Instagram @ bigdata.berkeley上关注我们，并访问我们的网站bd.berkeley.edu ！