netflix 数据集
by Julie Beckley & Chris Pham
朱莉·贝克利( Julie Beckley)和克里斯·范( Chris Pham)
This Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering (DSE) at Netflix through the eyes of two team members: Chris Pham and Julie Beckley.
通过两名团队成员Chris Pham和Julie Beckley的眼光，此次问答对Netflix数据科学与工程(DSE)中的各种技能，项目和文化提供了见解。
[Chris] Julie and I joined the Streaming DSE team at Netflix a few years ago and have been close colleagues and friends since then. At work, we regularly lean on each other for help based on our respective areas of expertise — I bring my breadth of big data tools and technologies while Julie has been building statistical models for the past decade. Outside of work, we share a love of good food and coffee, exchanging tips on making espresso.
[克里斯]朱莉和我几年前加入了Netflix的Streaming DSE团队，从那时起一直是亲密的同事和朋友。 在工作中，我们会根据各自的专业领域定期互相依靠，以寻求帮助–我在Julie过去十年建立统计模型的同时，带来了大数据工具和技术的广度。 在工作之余，我们共享美食和咖啡的热爱，交流制作意式浓缩咖啡的技巧。
[Julie] I took a traditional path to data science. Since mathematics was my favorite subject in school, I decided to pursue it for my bachelors degree at McGill University (while indulging in French culture in the beautiful city of Montreal). Over the course of the four years it became clear that I enjoyed combining analytical skills with solving real world problems, so a PhD in Statistics was a natural next step. After completing my education, I was still not certain whether I wanted a job in academia or industry. I took a role as a Research Staff Member at IBM Research, which served as a middle ground with a joint focus on real world applications, academic research, and even allowed me to teach a graduate Machine Learning course! I then transitioned to a full industry role at Netflix.
[Julie]我走了一条通往数据科学的传统道路。 由于数学是我在学校最喜欢的科目，因此我决定在麦吉尔大学攻读学士学位(同时在美丽的蒙特利尔市沉迷于法国文化)。 在过去的四年中，很明显，我喜欢将分析技能与解决现实世界中的问题相结合，因此，统计学博士学位是自然而然的下一步。 完成学业后，我仍然不确定我想要在学术界还是工业界工作。 我担任IBM Research的研究人员，这是一个中间立场，共同致力于现实世界的应用程序，学术研究，甚至还允许我教授研究生的机器学习课程！ 然后，我在Netflix担任了完整的行业职位。
[Chris] I initially wanted to build a career in consulting after receiving my graduate degree in Economics because I had a passion for analytical problem solving and statistical modeling. A role in data science eventually seemed like a natural transition, but it wasn’t without its hurdles: With my consulting background, I had to go through a few other roles first while learning how to code on the side. A lot of my learning and training was self-guided until 2016, when a manager at my last company took a chance on me and helped me make the rare transfer from a role in HR to Data Science.
[克里斯]在获得经济学的研究生学位后，我最初想从事咨询职业，因为我热衷于分析问题解决和统计建模。 数据科学中的角色最终看起来像是一个自然的过渡，但这并非没有障碍：凭借我的咨询背景，我必须在学习如何一边编码的同时首先经历其他几个角色。 直到2016年，我的许多学习和培训都是自我指导的，直到我上一家公司的经理抓住了我一次机会，并帮助我完成了从人力资源职位到数据科学的罕见转变。
[Julie] Chris and I have the same primary stakeholders (or engineering team that we support): Encoding Technologies. They are continuously innovating compression algorithms to efficiently send high quality audio and video files to our customers over the internet. I focus on improving experimentation methodology to test how well the newest files are working: do they need less bits to stream while providing a higher video quality? Do they cause less errors? My work is typically developed in R or Python. I love the cross-functional nature of my work, as it allows me to learn from others and creatively explore new statistical methodologies to improve the Netflix service.
[Julie] Chris和我有相同的主要利益相关者(或我们支持的工程团队)：编码技术。 他们正在不断创新压缩算法，以通过互联网将高质量的音频和视频文件有效地发送给我们的客户。 我专注于改进实验方法，以测试最新文件的运行状况：它们需要更少的比特流以提供更高的视频质量吗？ 它们会减少错误吗？ 我的作品通常是用R或Python开发的。 我喜欢我工作的跨职能性质，因为它使我可以向他人学习并创造性地探索新的统计方法，以改善Netflix服务。
[Chris] When I first started working with Encoding Technologies, there was so much data waiting to be translated into actionable insights. It was fun starting from almost nothing and transforming all of that data into self-serve tools and dashboards for the team to understand their contribution to the Netflix streaming experience. These projects have involved using Spark, Python, SQL, Tableau, and Jupyter notebooks. Over the last year, I’ve spent a lot of time analyzing data to inform how we roll out new encoding innovations to the diverse ecosystem of devices that stream Netflix.
[克里斯]当我刚开始使用编码技术时，有太多数据等待转化为可行的见解。 从几乎一无所有开始，然后将所有这些数据转换为自助服务工具和仪表板，让团队了解他们对Netflix流媒体体验的贡献，这很有趣。 这些项目涉及使用Spark，Python，SQL，Tableau和Jupyter笔记本。 在过去的一年中，我花了很多时间来分析数据，以告知我们如何针对流式处理Netflix的各种设备生态系统推出新的编码创新。
[Julie] Encoding experimentation (and more broadly, streaming experimentation) is critical for ensuring our customers have a good Quality of Experience when watching Netflix. In other words, the content you’re about to watch needs to load quickly with high video quality. When we test new encodes, we need effective data science methods to quickly and accurately understand whether customers are having a better experience. With these insights, the engineering teams can quickly understand what’s working well and what needs to be improved. It’s super exciting to see the impact of my work when I hear from friends and family that Netflix is streaming well for them!
[Julie]编码实验(更广泛地说是流实验)对于确保我们的客户在观看Netflix时拥有良好的体验质量至关重要。 换句话说，您要观看的内容需要以高视频质量快速加载。 在测试新编码时，我们需要有效的数据科学方法来快速准确地了解客户是否有更好的体验。 利用这些见解，工程团队可以快速了解哪些方面运作良好，哪些方面需要改进。 当我从朋友和家人那里听到Netflix对他们的支持很好时，看到我的工作产生的影响真是令人兴奋！
[Chris] There’s a lot of things to consider when we roll out a new compression algorithm. Which devices get this treatment? What is the benefit to the streaming experience? Is the benefit uniform, or do certain cohorts of members — such as those who stream over a cellular connection — benefit more? How does a decision of this scale affect the efficiency of our globally distributed content delivery network, Open Connect? It’s one big optimization problem that requires balancing several different factors. Streaming DSE is at the center of it all, bringing together different teams at Netflix and using data to drive decisions that impact our members around the world.
[克里斯]当我们推出一种新的压缩算法时，有很多事情要考虑。 哪些设备可以接受这种治疗？ 流媒体体验有什么好处？ 利益是统一的吗？还是某些特定的成员群体(例如通过蜂窝连接流式传输的成员)会受益更多？ 这种规模的决定如何影响我们全球分布的内容交付网络Open Connect的效率？ 这是一个很大的优化问题，需要平衡几个不同的因素。 串流DSE是一切的中心，将Netflix的不同团队聚集在一起，并使用数据来推动影响世界各地成员的决策。
[Julie] One of the special things about working at Netflix is that a diverse set of skills and backgrounds is truly appreciated, since there are many ways to add value to the company. From my experience, being proactive in pushing forward on your ideas is key. The values in the Netflix culture document allow for a framework where everyone is a leader to work well — this is because we expect initiative, direct and candid feedback, and transparency in everything we do. This leads to a great environment where I am constantly challenged, learning, and receiving constructive feedback on how I can do better!
[朱莉(Julie)]在Netflix工作的特别之处之一是，人们对各种技能和背景有着真正的赞赏，因为有很多方法可以为公司增值。 根据我的经验，积极主动地提出您的想法是关键。 Netflix文化文件中的价值观提供了一个框架，每个人都是领导者都能很好地工作-这是因为我们期望我们的行动能够主动，直接和坦率地反馈，并且保持透明。 这导致了一个巨大的环境，我不断地挑战，学习并收到关于如何做得更好的建设性反馈！
[Chris] I think a big part of our jobs is continuously thinking about how data can benefit our stakeholders. Julie and I will never know as much about video and audio compression algorithms as our talented Encoding Technologies team, but we should be the ones most familiar with the data: How to access, analyze, and visualize it; how to transform it into metrics that act as strong and accurate proxies for a member’s experience; and how to guide others to draw the right conclusions from data so they can act on it. Writing memos is a big part of Netflix culture, which I’ve found has been helpful for sharing ideas, soliciting feedback, and documenting project details. So writing well, especially the ability to translate technical concepts for a non-technical audience, is also very useful.
[克里斯]我认为我们工作的很大一部分是不断思考数据如何使我们的利益相关者受益。 我和朱莉(Julie)和我们的编码技术团队一样，对视频和音频压缩算法一无所知，但我们应该是最熟悉数据的人：如何访问，分析和可视化数据；如何使用数据？ 如何将其转换为衡量会员经验的强大而准确的指标； 以及如何指导他人从数据中得出正确的结论，以便他们可以据此采取行动。 写备忘录是Netflix文化的重要组成部分，我发现这对共享想法，征求反馈意见以及记录项目详细信息很有帮助。 因此，写得好，特别是对非技术读者来说翻译技术概念的能力也非常有用。
[Julie] One piece of advice I would pass along (and wish I could give to my younger self) is not to stress and try to plan every step of your data science career. Your career is long (and unpredictable!), so as long as you work hard and stay motivated, it will move in an exciting direction.
[朱莉(Julie)]我会通过的一条建议(并希望我能给年轻的自己)：不要过分强调并尝试计划数据科学事业的每个步骤。 您的职业生涯漫长(而且变幻莫测！)，因此，只要您努力工作并保持积极性，它就会朝着令人兴奋的方向发展。
[Chris] Everyone wants to build fancy models or tools, but fewer are willing to do the foundational things like cleaning the data and writing the documentation. I’ve found that volunteering and being proactive (no matter the task) has been an effective way of building trust with others, and it opened my career up to many more opportunities early on.
[克里斯]每个人都想建立漂亮的模型或工具，但是很少有人愿意做一些基本的事情，例如清理数据和编写文档。 我发现志愿服务和主动进取(无论执行什么任务)是与他人建立信任的有效方法，并且这为我的职业生涯尽早提供了更多机会。
If this post resonates with you and you’d like to explore opportunities with Netflix, check out our analytics site, search open roles, and learn about our culture. You can also find more stories like this here.
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翻译自: https://netflixtechblog.com/how-our-paths-brought-us-to-data-and-netflix-4eced44a6872
netflix 数据集
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