In advance of our upcoming event — Data Science Salon: Applying AI and ML to Media, Advertising, and Entertainment, we asked our speakers, who are some of nation’s leading data scientists in the media, advertising, and entertainment industries, to answer a few of our most pressing questions about the future of their industries. Read on for their insights — there’s some great advice in there!

在我们即将举行的活动-数据科学沙龙：将AI和ML应用到媒体，广告和娱乐中之前，我们请我们的演讲者(他们是媒体，广告和娱乐行业的美国领先数据科学家中的一些人)回答了一些关于他们行业未来的最紧迫的问题。 继续阅读以获取他们的见解-那里有一些很棒的建议！

What are some reasons a data scientist would want to move from another field into media/ad/entertainment?

数据科学家想要从另一个领域进入媒体/广告/娱乐领域的原因有哪些？

“I’ve really enjoyed working in media because there are so many aspects of the company that data science can help with. I’ve been able to work on forecasting, operations research, user segmentation, natural language processing, content recommendations. Data science improves our readers’ experience with the Times but also helps with business concerns ranging from newspaper distribution to advertising sales. As the newspaper business continues to evolve with readers’ changing habits, I’m sure that the scope of our work will only increase.” -Anne Bauer, Director of Data Science, NY Times

“我真的很喜欢在媒体上工作，因为公司的许多方面都可以提供数据科学帮助。 我已经能够进行预测，运营研究，用户细分，自然语言处理，内容推荐。 数据科学不仅可以改善读者对《纽约时报》的体验，还可以解决从报纸发行到广告销售等商业问题。 随着报业随着读者习惯的变化而不断发展，我相信我们的工作范围只会扩大。” -《纽约时报》数据科学总监Anne Bauer

“I think most data scientists are looking for a few key things in the roles they take and those are: interesting problems to work on, an abundance of data, and the ability to grow and learn new things. The media industry has more data available to it now than ever before and with that comes incredible opportunities to develop innovative ways to leverage that data for business impact. On top of that, the industry is changing at an accelerating pace as people’s media consumption habits evolve with the advent of new media platforms and technologies. In an industry that is changing as quickly as the media space, data scientists have to stay current with the latest advances in machine learning, analytics, and computing platforms to be competitive. This has created an exciting environment where someone with great analytical skills who is willing to learn the industry can have a tremendous impact.” -Bob Bress, Head of Data Science for Freewheel, a Comcast Company.

“我认为大多数数据科学家都在寻找他们扮演的角色中的一些关键问题，这些关键问题是：有待解决的有趣问题，大量数据以及发展和学习新事物的能力。 媒体行业现在可以使用的数据比以往任何时候都多，随之而来的机遇是开发创新方法以利用这些数据带来业务影响的难得机会。 最重要的是，随着新媒体平台和技术的出现，人们的媒体消费习惯不断演变，该行业正在以加速的步伐发展。 在一个瞬息万变的行业中，数据科学家必须紧跟机器学习，分析和计算平台的最新发展，以保持竞争力。 这创造了一个令人兴奋的环境，一个愿意学习该行业的具有出色分析能力的人可以产生巨大的影响。” -康卡斯特(Comcast)公司Freewheel数据科学负责人Bob Bress。

“It’s a quick changing field with constant evolution of user media habits that require research, creative thinking, and persuasion. The media space is a great place for a data scientist or analyst who enjoys a constantly changing environment that demands out of the box thinking.” -Wes Shockley, Senior Manager — Audience & Analytics, Meredith Corporation

“这是一个瞬息万变的领域，用户媒体习惯的不断发展需要研究，创造性思维和说服力。 媒体空间是数据科学家或分析师的理想之地，他们喜欢不断变化的环境，需要开箱即用的思维。” -Meredith公司受众与分析高级经理Wes Shockley

“I think most people join journalism because they believe in the mission and potential of the media to do good. When working in this space you have the potential to create or support the institutions holding power to account and driving meaningful conversations and change. You have the opportunity to be of service to a variety of people looking for information and answers. It’s unlike anything else.

“我认为大多数人加入新闻界是因为他们相信媒体的使命和潜力，可以做得很好。 在这样的空间中工作时，您有可能创建或支持负责问责并推动有意义的对话和变更的机构。 您有机会为寻求信息和答案的各种人提供服务。 这与其他任何东西都不一样。

If that doesn’t capture your attention, it is also worth mentioning working in media is an NLP data scientist’s dream (to paraphrase Tess Jeffers, a data scientist in the WSJ newsroom). Media also provides any number of interesting challenges to solve: propensity, churn, revenue, topic modeling, audience clustering, and more.” -Alyssa Zeisler, Research & Development Chief, Senior Product Manager, Editorial Tools, Wall Street Journal.

如果那没有引起您的注意，那么值得一提的是，在媒体中工作是NLP数据科学家的梦想( 换句话说， 《华尔街日报》新闻室的数据科学家Tess Jeffers )。 媒体还提供许多有趣的挑战来解决：倾向性，流失率，收入，主题建模，受众群体等等。” -《华尔街日报》研究与开发总监，高级产品经理，编辑工具总监Alyssa Zeisler。

“Depending on the research domain, data has many modalities: speech, acoustics, images, signals, point clouds, graphs, words, and more. Although there are specific visualization techniques for each domain, I especially enjoy the data rooting from visual content, using geometric priors, and its underlying high-dimensional nature. For anyone interested in vision and graphics applications of machine learning, media/entertainment industry is strongly suggested.” -Ilke Demir, Senior Research Scientist, Intel

取决于研究领域，数据具有多种形式：语音，声学，图像，信号，点云，图形，单词等等。 尽管每个领域都有特定的可视化技术，但我尤其喜欢使用几何先验及其潜在的高维特性从视觉内容中获取数据。 对于对机器学习的视觉和图形应用感兴趣的任何人，强烈建议使用媒体/娱乐行业。” -英特尔高级研究科学家Ilke Demir

“There are many unspoken and novel applications of data science in the entertainment industry today, but the plethora of opportunities yet to be discovered are what’s really exciting. It’s an industry that’s over 100 years old, and the chance to modernize and scale it lies in the contributions of data scientists. The next innovation in entertaining and bringing joy beyond the way we currently consume and produce movies, TV, and music is just around the corner, waiting for data scientists to unleash.” -Kim Martin, Data Science Manager at Netflix

“如今，在娱乐行业中，数据科学有许多潜口的和新颖的应用，但是尚未发现的大量机会确实令人兴奋。 这是一个拥有100多年历史的行业，其现代化和规模扩展的机会在于数据科学家的贡献。 娱乐和带来超越我们当前消费和制作电影，电视和音乐的方式的下一个创新指日可待，等待数据科学家释放。” -Netflix数据科学经理Kim Martin

“Growth. The Marketing Analytics Market is expected to reach USD 4.68 billion by 2025, at a CAGR of 14% over the forecast period 2020–2025. This is further boosted by the adoption of cloud technology and Big Data which will further increase the growth of the marketing analytics market.” -Denver Serrao, Sr. Software Development Engineer at WPEngine

“成长。 到2025年，市场分析市场预计将达到46.8亿美元，在2020-2025年的预测期内，复合年增长率为14％。 云技术和大数据的采用进一步推动了这一点，这将进一步促进营销分析市场的增长。” -Denver Serrao，WPEngine的高级软件开发工程师

“I think having a passion for the industry is key. Unlike industries such as biotech or pharmaceuticals, media and entertainment are inherently relatable to the vast majority of us, simply due to their prevalence in our daily lives. I myself began my data science career at Paramount Pictures (Viacom) due to my love for movies. I believe this level of familiarity and fondness for the subject matter is hard to cultivate otherwise, and it translates to better motivation at work.” -Daryl Kang, Lead Data Scientist at Forbes

“我认为对行业充满热情是关键。 与生物技术或制药等行业不同，媒体和娱乐与我们绝大多数人有着内在的联系，这仅仅是因为它们在我们的日常生活中很普遍。 由于对电影的热爱，我本人在派拉蒙影业(Viacom)开始了我的数据科学职业。 我认为，很难以其他方式培养对主题的这种熟悉和喜爱，它可以转化为更好的工作动力。” -福布斯首席数据科学家Daryl Kang

“There is a strong component of human psychology and behavior that is part of most decisions in media/advertising/entertainment. While data and algorithms can be automated and learn a lot, there is a strong human element that requires diverse voices and thinking in order to truly connect users to content well.” -Amit Bahattacharayya, Head of Data Science at VOX Media

“人类心理和行为的重要组成部分是媒体/广告/娱乐大多数决定的一部分。 尽管数据和算法可以实现自动化并学到很多东西，但强大的人为因素要求多种多样的声音和思维方式，才能真正将用户与内容良好地联系起来。” -VOX Media数据科学主管Amit Bahattacharayya

What advice do you have for new entrants to the field? (aka, what do you wish someone had told you?)

您对新进入该领域有何建议？ (aka，您希望有人告诉您什么？)

“First and foremost: the importance of clearly communicating is often underappreciated, but can mean the difference in an analysis or body of work being used or not. New entrants should work on how to articulate ideas and communicate them in ways that a stakeholder is likely to understand, whether that individual relies more on numbers or anecdotes. Learning what is a valuable problem to solve, how to ask good questions with data and solve problems creatively are similar and adjacent skills.

“首先，最重要的是：清晰沟通的重要性通常未被重视，但这可能意味着所使用的分析或工作主体之间的差异。 新进入者应该研究如何表达想法，并以利益相关者可能理解的方式交流思想，无论这个人更多地依赖数字还是轶事。 学习什么是有价值的问题，如何用数据提出好的问题以及创造性地解决问题是相似和相邻的技能。

It’s also worth noting that a variety of backgrounds are relevant, so don’t think you’re missing a specific skill that will keep you from progressing. Our chief of data science is from astrophysics, a lead data scientist on the team comes from biology, and I’ve spent my entire career in newsrooms (and not in data roles). Having an understanding, appreciation and hunger can be just as, if not more important to your ongoing success.” -Alyssa Zeisler, Research & Development Chief, Senior Product Manager, Editorial Tools, Wall Street Journal.

还要指出的是，各种背景都是相关的，所以不要以为您缺少会阻碍您进步的特定技能。 我们的数据科学负责人来自天体物理学，该团队的首席数据科学家来自生物学，我的整个职业生涯都花在新闻编辑室(而不是数据角色)。 理解，欣赏和饥饿对您的持续成功同样重要，甚至更为重要。” -《华尔街日报》研究与开发总监，高级产品经理，编辑工具总监Alyssa Zeisler。

“For anyone looking to get into data science in the media space there are a few pieces of advice I would give:

“对于希望在媒体领域进入数据科学的任何人，我都会提供一些建议：

• Get to know your business in-depth. Technical skills are only half the battle. Data science only provides value when it is applied in a way that solves specific business problems.深入了解您的业务。 技术技能只是成功的一半。 数据科学只有在以解决特定业务问题的方式加以应用时才能提供价值。
• Build momentum. Find small ways that data science can provide business impact to build confidence and garner business investment in larger initiatives.建立动力。 寻找数据科学可以提供业务影响的小方法，以建立信心并在较大的计划中获得业务投资。
• Develop skills beyond machine learning. No matter how sophisticated your model is, if you put garbage in, you will get garbage out. Become an expert at exploratory data analysis and ask a lot of questions to know what the data you are working with truly represents. Knowledge of statistical analyses and optimization methods can also yield great benefits.”培养超越机器学习的技能。 无论您的模型多么复杂，如果将垃圾放入其中，都会将其清除。 成为探索性数据分析方面的专家，并提出很多问题以了解您使用的数据真正代表了什么。 统计分析和优化方法的知识也可以带来巨大的好处。”

-Bob Bress, Head of Data Science for Freewheel, a Comcast Company.

-康卡斯特(Comcast)公司Freewheel数据科学负责人Bob Bress。

“For me, data science has been largely about learning from other’s expertise. Initially I thought I would study in depth a few different machine learning algorithms and statistical techniques. But, applying these data science tools to problems at the NYTimes has involved learning about a much broader set of topics from a wide range of people. How are we currently addressing the problem, why does that fall short, and what data can we use to improve our approach? How can we work with the rest of the company to improve the data quality so that machine learning can be more effective? How can we present the results of our modeling in a useful way, or integrate our production model into existing company infrastructure?

“对我来说，数据科学主要是学习他人的专业知识。 最初，我以为我会深入研究一些不同的机器学习算法和统计技术。 但是，将这些数据科学工具应用于《纽约时报》的问题涉及从众多人那里学习更广泛的主题。 我们当前如何解决该问题，为什么还不能做到这一点，以及我们可以使用哪些数据来改进我们的方法？ 我们如何与公司其他部门合作以改善数据质量，从而使机器学习更加有效？ 我们如何以一种有用的方式展示建模结果，或者将生产模型集成到现有的公司基础架构中？

Coming from academic research, I was used to a paradigm where the analysis and scientific results were paramount and the communication of results was a necessary but secondary task. In my experience with data science, I’ve found that good communication, in both directions, and smooth integration are often just as important to the success of a project as the modeling and analysis. Even straightforward projects can end up quite wide-ranging!” -Anne Bauer, Director of Data Science, NY Times

来自学术研究，我习惯了一个范式，其中分析和科学结果是最重要的，结果的交流是必要但次要的任务。 根据我在数据科学方面的经验，我发现双向的良好沟通和流畅的集成对于项目成功与建模和分析同样重要。 即使是简单的项目也可能涉及面很广！” -纽约时报数据科学总监Anne Bauer

“Don’t be intimidated by the work at first; a lot of learning happens on the job. This is especially true for those coming from a non-technical background — as a matter of fact, they often bring a diverse set of opinions to the discussion.

“一开始不要被这项工作吓倒； 在工作中会发生很多学习。 对于那些来自非技术背景的人来说尤其如此-事实上，他们经常在讨论中带来各种各样的观点。

Unlike in academia, having a firm grasp of basic programming goes a lot farther than mere theoretical data science skills.

与学术界不同，对基本程序的牢固掌握远不只是理论数据科学技能。

Finally, don’t be disheartened if the work doesn’t seem much like data science at first. Real-world data is messy, and it might take a while to reveal its value.” -Daryl Kang, Lead Data Scientist at Forbes

最后，如果一开始工作看起来不太像数据科学，请不要灰心。 现实世界的数据比较混乱，可能需要一段时间才能揭示其价值。” -福布斯首席数据科学家Daryl Kang

“There is no right path. Figure out what you are good at and find a way to join that with your job and function.” -Amit Bahattacharayya, Head of Data Science at VOX Media

“没有正确的道路。 找出自己擅长的领域，并找到一种将其与工作和职能结合在一起的方法。” -VOX Media数据科学主管Amit Bahattacharayya

“Data science can be learned by anyone who has a computer and access to the internet, so there will always be a large pool of data scientists with a set of homogeneous technical skills. The single most important trait that will lift your data science career to a higher level and set you apart from the crowd are your communication skills. Developing a data science solution involves complex techniques starting from acquiring data to training a ML model. The ability to translate analysis outputs into actionable business insights, and communicate them to business stakeholders is the most significant trait of a great data scientist. Logically, the communication of analysis outputs determines the impact of a data science solution as the ability to engage stakeholders. Emotionally, this helps us speak the same language as stakeholders and carve more meaningful alignment. Leveraging the business language to effectively communicate technical results is imperative, as it encourages the stakeholders to participate effectively in the ideation and validation of results. The best data scientists are empathetic in communicating results by crafting a compelling story with clear insights to present facts and figures to facilitate understanding for everyone.” -Upasna Gautam, Manager, Product & Data at CNN/WarnerMedia

拥有计算机并可以访问互联网的任何人都可以学习数据科学，因此，总是会有大量具有相同技术技能的数据科学家。 使您的数据科学事业发展到更高水平并使您与众不同的最重要的特征就是沟通技巧。 开发数据科学解决方案涉及从获取数据到训练ML模型的复杂技术。 将分析输出转换为可操作的业务见解并将其传达给业务利益相关者的能力是出色的数据科学家的最重要特征。 从逻辑上讲，分析输出的交流将数据科学解决方案的影响确定为吸引利益相关者的能力。 从情感上讲，这有助于我们说出与利益相关者相同的语言，并实现更有意义的契合。 必须利用业务语言来有效地传达技术结果，因为这会鼓励利益相关者有效参与结果的构思和验证。 最好的数据科学家通过精心设计一个引人入胜的故事，并提供清晰的见解来展示事实和数据，以促进每个人的理解，从而善于传达结果。” -CNN / WarnerMedia产品与数据经理Upasna Gautam

“I personally have been in a primarily engineering role in my career. From a data science perspective though, it’s important to have a quantitative bent of mind. Most professionals in this field have an education that combines statistics, maths, programming/computer science along with some domain knowledge in marketing. The ideal person has a strong quantitative orientation as well as a feel for consumer behavior and strategies that affect it.” -Denver Serrao, Sr. Software Development Engineer at WPEngine

“我个人在我的职业生涯中一直担任主要的工程职位。 但是，从数据科学的角度来看，有一个定量的想法很重要。 该领域的大多数专业人员都接受了将统计，数学，编程/计算机科学与市场营销领域的某些知识相结合的教育。 理想的人具有强烈的定量取向，并且对影响其的消费者行为和策略有感觉。” -Denver Serrao，WPEngine的高级软件开发工程师

“Not all opportunities are created equal. Although you can gain skill and exercise your talent as a data scientist working in a variety of domains, the moment you find that domain you connect with, you’ll see your impact multiply. In that moment, you’ll move beyond just doing what you know, into that space of purpose and drive greater innovation. You’ll wonder what you ever did before”. -Kim Martin, Data Science Manager at Netflix

“并非所有机会都是平等创造的。 尽管您可以在多个领域中工作，成为一名数据科学家，可以提高技能并发挥自己的才能，但是当您发现与之联系的领域时，就会看到影响力成倍增加。 在那一刻，您将超越所做的一切，进入目标空间并推动更大的创新。 您会想知道您以前做过的事情”。 -Netflix数据科学经理Kim Martin

“SQL and Python are essential — but so is creativity.” -Wes Shockley, Senior Manager — Audience & Analytics, Meredith Corporation

“ SQL和Python是必不可少的-但创造力也是如此。” -Meredith公司受众与分析高级经理Wes Shockley

“Always be curious about the facts and the reasoning, and always vocalize your curiosity. When your perspective is built on curiosity, data, and learning; you cannot escape from scientific breakthroughs. This also enables building scientifically grounded products with proper evaluations and theoretical foundations, which are more likely to survive in the longer term.” -Ilke Demir, Senior Research Scientist, Intel

“总是对事实和推理感到好奇，并总是发出好奇心。 当您的观点基于好奇心，数据和学习时； 您无法摆脱科学突破。 这也使我们能够构建具有科学依据的产品，并提供适当的评估和理论基础，从而更有可能长期生存。” -英特尔高级研究科学家Ilke Demir

“Attend meetups, even if they’re virtual. Data science is full of jargon, and it gets even more specialized when you move into a subfield such as Media, Entertainment, or Advertising. Meetups provide free exposure to this jargon! Even if you have plenty of technical knowledge, this will allow you to soak up the lingua franca of the field so that you’re ready to talk the talk when you get to an interview.” -Dominick Rocco, Data Scientist at PhData

“参加聚会，即使他们是虚拟的。 数据科学充满了行话，当您进入诸如媒体，娱乐或广告之类的子领域时，它变得更加专业。 聚会可免费使用此行话！ 即使您具有丰富的技术知识，这也可以让您吸收该领域的通用语言，以便在接受采访时随时可以进行演讲。” -PhData的数据科学家Dominick Rocco

What does “data science” mean to you? Or, what do you see as the difference between data science, ML, and AI?

“数据科学”对您意味着什么？ 或者，您认为数据科学，机器学习和人工智能之间的区别是什么？

“‘Data Science’ describes the application of analytical methods to data to drive insights. Those analytical methods could include machine learning, statistical analyses, probabilistic modelling, data mining or other methods. ‘Machine learning’ refers to a class of algorithms which generally seek to make a prediction or classification on data while allowing for the algorithm to ‘learn’ and adapt based on training data without explicit code directing it to do so. Machine learning provides a dynamic way of adjusting forecasts or classification methods as underlying data changes. ‘Artificial Intelligence’ more generally describes the simulation of human intelligence by machines. That simulation in many cases uses machine learning algorithms but may also use rule-based expert systems or other probabilistic-based simulation methods. We often see AI and ML used interchangeably today because new applications of AI tend to leverage ML based algorithms” -Bob Bress, Head of Data Science for Freewheel, a Comcast Company.

““数据科学”描述了分析方法在数据上的应用，以推动见解。 这些分析方法可以包括机器学习，统计分析，概率建模，数据挖掘或其他方法。 “机器学习”是指一类算法，通常寻求对数据进行预测或分类，同时允许算法基于训练数据进行“学习”和适应，而无需明确的代码来指导这样做。 机器学习提供了一种动态的方式，可以根据基础数据的变化来调整预测或分类方法。 “人工智能”更笼统地描述了机器对人类智能的模拟。 该模拟在许多情况下使用机器学习算法，但也可能使用基于规则的专家系统或其他基于概率的模拟方法。 今天，我们经常看到AI和ML可以互换使用，因为AI的新应用倾向于利用基于ML的算法。”-Comcast公司Freewheel数据科学主管Bob Bress。

“Data science is the occupation of extracting value from real-world data; ML and AI are technologies that fall into the data scientists toolkit, along with others such as statistics and data manipulation. AI technologies are those which use programs or machines to mimic cognitive behaviors, while ML is a subfield of AI focused on programs or machines that automatically learn their cognitive behavior from data. Generally, an AI or ML scientist will focus on developing those technologies, often using standard benchmark datasets that are cleaner than real-world data. A data scientist, on the other hand, will take the latest and greatest AI technologies and apply them to messy real-world data to create value for individuals and businesses.” -Dominick Rocco, Data Scientist at PhData

“数据科学是从现实数据中提取价值的职业； ML和AI以及其他诸如统计和数据处理之类的技术均属于数据科学家工具包。 AI技术是使用程序或机器模仿认知行为的技术，而ML是AI的一个子领域，专注于自动从数据中学习其认知行为的程序或机器。 通常，AI或ML科学家通常会使用比实际数据干净的标准基准数据集来专注于开发那些技术。 另一方面，数据科学家将采用最新最好的AI技术，并将其应用于凌乱的现实数据中，从而为个人和企业创造价值。” -PhData的数据科学家Dominick Rocco

“Data science is preparing, analyzing and deriving meaningful observations from data. It may, or may not be towards AI, and it may or may not be using ML. On the other hand, AI is creating an illusion of human-like intelligence and autonomy in machines, which usually depends on carefully crafted systems and data. Machine learning is the foundation of enabling machines to learn and reason from data and/or observations. As we progress towards deep learning and complex AI applications, the dependency on high quality data becomes crucial, so data science becomes an essential part of AI/ML applications.” -Ilke Demir, Senior Research Scientist, Intel

数据科学正在准备，分析和从数据中得出有意义的观察结果。 它可能会或可能不会针对AI，并且可能会或可能不会使用ML。 另一方面，人工智能正在机器中创造出类似于人类的智能和自主权的幻觉，这通常取决于精心制作的系统和数据。 机器学习是使机器能够从数据和/或观察中学习和推理的基础。 随着我们向深度学习和复杂的AI应用程序发展，对高质量数据的依赖变得至关重要，因此数据科学成为AI / ML应用程序的重要组成部分。” -英特尔高级研究科学家Ilke Demir

“Data science is the study of extracting value from data, while AI is the ability of machines to perceive and to adapt to changes in their environment through actions that optimize their objectives. While emblematic of the great technological advances of the present day, neither field is a recent phenomenon. Going by its definition, data science existed for a long as recorded information was available, while the field of AI research began as early as the 1950s. Even the game-changing archetype of modern AI systems, neural networks, was already conceived by the 1980s. What changed was the exponential increase in computing power, coupled with a fall in costs, and the mass proliferation of data in recent years. This enabled data science to alter the paradigm of AI research, supplanting a field that was once logic-based with one that simulates learning through statistical models — we call this machine learning.” -Daryl Kang, Lead Data Scientist at Forbes

“数据科学是从数据中提取价值的研究，而人工智能是机器通过优化目标的行动感知并适应环境变化的能力。 尽管象征着当今的巨大技术进步，但是这两个领域都不是最近出现的现象。 按照它的定义，数据科学存在的时间很长，只要有记录的信息就可以使用，而AI研究领域则早在1950年代就开始了。 甚至在1980年代，也已经构想出改变现代AI系统，改变游戏规则的原型-神经网络。 变化的是，近年来计算能力呈指数级增长，再加上成本下降以及数据的大量扩散。 这使数据科学改变了AI研究的范式，取代了曾经基于逻辑的领域和通过统计模型来模拟学习的领域，我们称之为机器学习。” -福布斯首席数据科学家Daryl Kang

“Data science is the application of the scientific process to answering questions with data.” -Wes Shockley, Senior Manager — Audience & Analytics, Meredith Corporation

“数据科学是科学过程在回答数据问题中的应用。” -Meredith公司受众与分析高级经理Wes Shockley

“I take the science part of data science very seriously. It is not that hard to learn to program or a new language or framework. On the other hand, I can’t teach you math and logical thinking. A true scientist is skeptical, asks the hardest questions of themselves, and has incredible attention to detail. These are the characteristics that we need to be more than analysts.

“我非常重视数据科学的科学部分。 学习编程或新的语言或框架并不难。 另一方面，我不能教你数学和逻辑思维。 一位真正的科学家对此表示怀疑，会问自己最棘手的问题，并且对细节的关注程度令人难以置信。 这些是我们需要比分析师更多的特征。

As for the difference, I don’t really see much difference except an evolving set of words that the world uses to describe ‘How do I use data to model some process and make the most useful predictions that I can?’” -Amit Bahattacharayya, Head of Data Science at VOX Media

至于差异，除了世界上用来描述“我如何使用数据来建模某些过程并做出我能做的最有用的预测”的不断发展的词语外，我并没有看到太多差异。”-阿米特·巴哈塔恰拉亚VOX Media数据科学主管

What’s next for you, career-wise?

从职业角度来说，您接下来要做什么？

“I consider myself lucky to be working in the media and advertising space at a time when Data Science is playing an increasingly important role in driving value within the industry. I hope to play a leadership role in increasing the adoption of and the investment in data science technologies and personnel across my company and the industry.” -Bob Bress, Head of Data Science for Freewheel, a Comcast Company.

“当Data Science在推动行业价值增长中发挥越来越重要的作用时，我感到自己很幸运能够在媒体和广告领域工作。 我希望在提高公司和整个行业对数据科学技术和人员的采用和投资方面发挥领导作用。” -康卡斯特(Comcast)公司Freewheel数据科学负责人Bob Bress。

“Unlike in academia, the most effective data scientists in the industry are those that can best productize and sell their data products. In this regard, I believe the greatest opportunity for growth comes in the shift to cloud computing as it allows the data scientist to focus more on the logic and algorithm at hand and less on infrastructure and DevOps. Hence, I expect to see more data scientists take on the role of cloud architect in the future.” -Daryl Kang, Lead Data Scientist at Forbes

与学术界不同，业内最有效的数据科学家是那些能够最好地生产和销售其数据产品的科学家。 在这方面，我认为增长的最大机会来自向云计算的转变，因为云计算使数据科学家可以将更多的精力放在手头的逻辑和算法上，而将精力放在基础架构和DevOps上。 因此，我希望将来看到更多的数据科学家担当云架构师的角色。” -福布斯首席数据科学家Daryl Kang

“I would like to continue teaching, innovating and mentoring and helping guide small to medium sized organizations be smart w/ their data.”

“我想继续进行教学，创新和指导，并帮助指导中小型组织使用其数据来提高智能。”

-Amit Bahattacharayya, Head of Data Science at VOX Media

-VOX Media数据科学主管Amit Bahattacharayya

“Leading my team down the road to high performing predictive insights, so that when an opportunity is missed, it was by choice.” -Wes Shockley, Senior Manager — Audience & Analytics, Meredith Corporation

“带领我的团队走上高效的预测洞察力的道路，因此，当机会错失时，这是由您选择的。” -Meredith公司受众与分析高级经理Wes Shockley

“It is absolutely amazing to drive the research in the world’s largest volumetric capture stage! My curiosity points to a different research question at every corner of the studio, and we are building unique AI solutions everyday. Having unprecedented amount of visual data and working hand in hand with artists for award winning productions, we are revolutionizing the entertainment industry with AI and data. I feel honored and privileged to have this unique position where my research can actually impact the world through immersive 3D experiences.” -Ilke Demir, Senior Research Scientist, Intel

“在世界上最大的体积捕获阶段进行这项研究绝对是惊人的！ 我的好奇心指向工作室各个角落的另一个研究问题，我们每天都在构建独特的AI解决方案。 我们拥有无与伦比的视觉数据，并与艺术家携手合作，获得屡获殊荣的作品，我们正在通过AI和数据革新娱乐业。 我很荣幸能够拥有这个独特的职位，使我的研究能够通过沉浸式3D体验真正影响世界。” -英特尔高级研究科学家Ilke Demir

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Hear from these speakers and more at Data Science Salon: Applying AI and ML to Media, Advertising, and Entertainment, September 22–25, 2020.

2020年9月22日至25日，在数据科学沙龙：将AI和ML应用于媒体，广告和娱乐中 ，聆听这些演讲者的更多内容。