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!

在我們即將舉行的活動-數據科學沙龍：將AI和ML應用到媒體，廣告和娛樂中之前，我們請我們的演講者(他們是媒體，廣告和娛樂行業的美國領先數據科學家中的一些人)回答了一些關于他們行業未來的最緊迫的問題。 繼續閱讀以獲取他們的見解-那里有一些很棒的建議！

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

數據科學家想要從另一個領域進入媒體/廣告/娛樂領域的原因有哪些？

“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

“我真的很喜歡在媒體上工作，因為公司的許多方面都可以提供數據科學幫助。 我已經能夠進行預測，運營研究，用戶細分，自然語言處理，內容推薦。 數據科學不僅可以改善讀者對《紐約時報》的體驗，還可以解決從報紙發行到廣告銷售等商業問題。 隨著報業隨著讀者習慣的變化而不斷發展，我相信我們的工作范圍只會擴大。” -《紐約時報》數據科學總監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.

“我認為大多數數據科學家都在尋找他們扮演的角色中的一些關鍵問題，這些關鍵問題是：有待解決的有趣問題，大量數據以及發展和學習新事物的能力。 媒體行業現在可以使用的數據比以往任何時候都多，隨之而來的機遇是開發創新方法以利用這些數據帶來業務影響的難得機會。 最重要的是，隨著新媒體平臺和技術的出現，人們的媒體消費習慣不斷演變，該行業正在以加速的步伐發展。 在一個瞬息萬變的行業中，數據科學家必須緊跟機器學習，分析和計算平臺的最新發展，以保持競爭力。 這創造了一個令人興奮的環境，一個愿意學習該行業的具有出色分析能力的人可以產生巨大的影響。” -康卡斯特(Comcast)公司Freewheel數據科學負責人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

“這是一個瞬息萬變的領域，用戶媒體習慣的不斷發展需要研究，創造性思維和說服力。 媒體空間是數據科學家或分析師的理想之地，他們喜歡不斷變化的環境，需要開箱即用的思維。” -Meredith公司受眾與分析高級經理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.

“我認為大多數人加入新聞界是因為他們相信媒體的使命和潛力，可以做得很好。 在這樣的空間中工作時，您有可能創建或支持負責問責并推動有意義的對話和變更的機構。 您有機會為尋求信息和答案的各種人提供服務。 這與其他任何東西都不一樣。

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.

如果那沒有引起您的注意，那么值得一提的是，在媒體中工作是NLP數據科學家的夢想( 換句話說， 《華爾街日報》新聞室的數據科學家Tess Jeffers )。 媒體還提供許多有趣的挑戰來解決：傾向性，流失率，收入，主題建模，受眾群體等等。” -《華爾街日報》研究與開發總監，高級產品經理，編輯工具總監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

取決于研究領域，數據具有多種形式：語音，聲學，圖像，信號，點云，圖形，單詞等等。 盡管每個領域都有特定的可視化技術，但我尤其喜歡使用幾何先驗及其潛在的高維特性從視覺內容中獲取數據。 對于對機器學習的視覺和圖形應用感興趣的任何人，強烈建議使用媒體/娛樂行業。” -英特爾高級研究科學家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，在娛樂行業中，數據科學有許多潛口的和新穎的應用，但是尚未發現的大量機會確實令人興奮。 這是一個擁有100多年歷史的行業，其現代化和規模擴展的機會在于數據科學家的貢獻。 娛樂和帶來超越我們當前消費和制作電影，電視和音樂的方式的下一個創新指日可待，等待數據科學家釋放。” -Netflix數據科學經理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

“成長。 到2025年，市場分析市場預計將達到46.8億美元，在2020-2025年的預測期內，復合年增長率為14％。 云技術和大數據的采用進一步推動了這一點，這將進一步促進營銷分析市場的增長。” -Denver Serrao，WPEngine的高級軟件開發工程師

“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

“我認為對行業充滿熱情是關鍵。 與生物技術或制藥等行業不同，媒體和娛樂與我們絕大多數人有著內在的聯系，這僅僅是因為它們在我們的日常生活中很普遍。 由于對電影的熱愛，我本人在派拉蒙影業(Viacom)開始了我的數據科學職業。 我認為，很難以其他方式培養對主題的這種熟悉和喜愛，它可以轉化為更好的工作動力。” -福布斯首席數據科學家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

“人類心理和行為的重要組成部分是媒體/廣告/娛樂大多數決定的一部分。 盡管數據和算法可以實現自動化并學到很多東西，但強大的人為因素要求多種多樣的聲音和思維方式，才能真正將用戶與內容良好地聯系起來。” -VOX Media數據科學主管Amit Bahattacharayya

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

您對新進入該領域有何建議？ (aka，您希望有人告訴您什么？)

“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：清晰溝通的重要性通常未被重視，但這可能意味著所使用的分析或工作主體之間的差異。 新進入者應該研究如何表達想法，并以利益相關者可能理解的方式交流思想，無論這個人更多地依賴數字還是軼事。 學習什么是有價值的問題，如何用數據提出好的問題以及創造性地解決問題是相似和相鄰的技能。

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.

還要指出的是，各種背景都是相關的，所以不要以為您缺少會阻礙您進步的特定技能。 我們的數據科學負責人來自天體物理學，該團隊的首席數據科學家來自生物學，我的整個職業生涯都花在新聞編輯室(而不是數據角色)。 理解，欣賞和饑餓對您的持續成功同樣重要，甚至更為重要。” -《華爾街日報》研究與開發總監，高級產品經理，編輯工具總監Alyssa Zeisler。

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

“對于希望在媒體領域進入數據科學的任何人，我都會提供一些建議：

• 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.
  深入了解您的業務。 技術技能只是成功的一半。 數據科學只有在以解決特定業務問題的方式加以應用時才能提供價值。
• Build momentum. Find small ways that data science can provide business impact to build confidence and garner business investment in larger initiatives.
  建立動力。 尋找數據科學可以提供業務影響的小方法，以建立信心并在較大的計劃中獲得業務投資。
• 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.”
  培養超越機器學習的技能。 無論您的模型多么復雜，如果將垃圾放入其中，都會將其清除。 成為探索性數據分析方面的專家，并提出很多問題以了解您使用的數據真正代表了什么。 統計分析和優化方法的知識也可以帶來巨大的好處。”

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

-康卡斯特(Comcast)公司Freewheel數據科學負責人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?

“對我來說，數據科學主要是學習他人的專業知識。 最初，我以為我會深入研究一些不同的機器學習算法和統計技術。 但是，將這些數據科學工具應用于《紐約時報》的問題涉及從眾多人那里學習更廣泛的主題。 我們當前如何解決該問題，為什么還不能做到這一點，以及我們可以使用哪些數據來改進我們的方法？ 我們如何與公司其他部門合作以改善數據質量，從而使機器學習更加有效？ 我們如何以一種有用的方式展示建模結果，或者將生產模型集成到現有的公司基礎架構中？

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

來自學術研究，我習慣了一個范式，其中分析和科學結果是最重要的，結果的交流是必要但次要的任務。 根據我在數據科學方面的經驗，我發現雙向的良好溝通和流暢的集成對于項目成功與建模和分析同樣重要。 即使是簡單的項目也可能涉及面很廣！” -紐約時報數據科學總監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.

“一開始不要被這項工作嚇倒； 在工作中會發生很多學習。 對于那些來自非技術背景的人來說尤其如此-事實上，他們經常在討論中帶來各種各樣的觀點。

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

與學術界不同，對基本程序的牢固掌握遠不只是理論數據科學技能。

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，如果一開始工作看起來不太像數據科學，請不要灰心。 現實世界的數據比較混亂，可能需要一段時間才能揭示其價值。” -福布斯首席數據科學家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

“沒有正確的道路。 找出自己擅長的領域，并找到一種將其與工作和職能結合在一起的方法。” -VOX Media數據科學主管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

擁有計算機并可以訪問互聯網的任何人都可以學習數據科學，因此，總是會有大量具有相同技術技能的數據科學家。 使您的數據科學事業發展到更高水平并使您與眾不同的最重要的特征就是溝通技巧。 開發數據科學解決方案涉及從獲取數據到訓練ML模型的復雜技術。 將分析輸出轉換為可操作的業務見解并將其傳達給業務利益相關者的能力是出色的數據科學家的最重要特征。 從邏輯上講，分析輸出的交流將數據科學解決方案的影響確定為吸引利益相關者的能力。 從情感上講，這有助于我們說出與利益相關者相同的語言，并實現更有意義的契合。 必須利用業務語言來有效地傳達技術結果，因為這會鼓勵利益相關者有效參與結果的構思和驗證。 最好的數據科學家通過精心設計一個引人入勝的故事，并提供清晰的見解來展示事實和數據，以促進每個人的理解，從而善于傳達結果。” -CNN / WarnerMedia產品與數據經理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

“我個人在我的職業生涯中一直擔任主要的工程職位。 但是，從數據科學的角度來看，有一個定量的想法很重要。 該領域的大多數專業人員都接受了將統計，數學，編程/計算機科學與市場營銷領域的某些知識相結合的教育。 理想的人具有強烈的定量取向，并且對影響其的消費者行為和策略有感覺。” -Denver Serrao，WPEngine的高級軟件開發工程師

“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

“并非所有機會都是平等創造的。 盡管您可以在多個領域中工作，成為一名數據科學家，可以提高技能并發揮自己的才能，但是當您發現與之聯系的領域時，就會看到影響力成倍增加。 在那一刻，您將超越所做的一切，進入目標空間并推動更大的創新。 您會想知道您以前做過的事情”。 -Netflix數據科學經理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-但創造力也是如此。” -Meredith公司受眾與分析高級經理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

“總是對事實和推理感到好奇，并總是發出好奇心。 當您的觀點基于好奇心，數據和學習時； 您無法擺脫科學突破。 這也使我們能夠構建具有科學依據的產品，并提供適當的評估和理論基礎，從而更有可能長期生存。” -英特爾高級研究科學家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

“參加聚會，即使他們是虛擬的。 數據科學充滿了行話，當您進入諸如媒體，娛樂或廣告之類的子領域時，它變得更加專業。 聚會可免費使用此行話！ 即使您具有豐富的技術知識，這也可以讓您吸收該領域的通用語言，以便在接受采訪時隨時可以進行演講。” -PhData的數據科學家Dominick Rocco

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

“數據科學”對您意味著什么？ 或者，您認為數據科學，機器學習和人工智能之間的區別是什么？

“‘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.

““數據科學”描述了分析方法在數據上的應用，以推動見解。 這些分析方法可以包括機器學習，統計分析，概率建模，數據挖掘或其他方法。 “機器學習”是指一類算法，通常尋求對數據進行預測或分類，同時允許算法基于訓練數據進行“學習”和適應，而無需明確的代碼來指導這樣做。 機器學習提供了一種動態的方式，可以根據基礎數據的變化來調整預測或分類方法。 “人工智能”更籠統地描述了機器對人類智能的模擬。 該模擬在許多情況下使用機器學習算法，但也可能使用基于規則的專家系統或其他基于概率的模擬方法。 今天，我們經常看到AI和ML可以互換使用，因為AI的新應用傾向于利用基于ML的算法。”-Comcast公司Freewheel數據科學主管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

“數據科學是從現實數據中提取價值的職業； ML和AI以及其他諸如統計和數據處理之類的技術均屬于數據科學家工具包。 AI技術是使用程序或機器模仿認知行為的技術，而ML是AI的一個子領域，專注于自動從數據中學習其認知行為的程序或機器。 通常，AI或ML科學家通常會使用比實際數據干凈的標準基準數據集來專注于開發那些技術。 另一方面，數據科學家將采用最新最好的AI技術，并將其應用于凌亂的現實數據中，從而為個人和企業創造價值。” -PhData的數據科學家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

數據科學正在準備，分析和從數據中得出有意義的觀察結果。 它可能會或可能不會針對AI，并且可能會或可能不會使用ML。 另一方面，人工智能正在機器中創造出類似于人類的智能和自主權的幻覺，這通常取決于精心制作的系統和數據。 機器學習是使機器能夠從數據和/或觀察中學習和推理的基礎。 隨著我們向深度學習和復雜的AI應用程序發展，對高質量數據的依賴變得至關重要，因此數據科學成為AI / ML應用程序的重要組成部分。” -英特爾高級研究科學家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

“數據科學是從數據中提取價值的研究，而人工智能是機器通過優化目標的行動感知并適應環境變化的能力。 盡管象征著當今的巨大技術進步，但是這兩個領域都不是最近出現的現象。 按照它的定義，數據科學存在的時間很長，只要有記錄的信息就可以使用，而AI研究領域則早在1950年代就開始了。 甚至在1980年代，也已經構想出改變現代AI系統，改變游戲規則的原型-神經網絡。 變化的是，近年來計算能力呈指數級增長，再加上成本下降以及數據的大量擴散。 這使數據科學改變了AI研究的范式，取代了曾經基于邏輯的領域和通過統計模型來模擬學習的領域，我們稱之為機器學習。” -福布斯首席數據科學家Daryl Kang

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

“數據科學是科學過程在回答數據問題中的應用。” -Meredith公司受眾與分析高級經理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.

“我非常重視數據科學的科學部分。 學習編程或新的語言或框架并不難。 另一方面，我不能教你數學和邏輯思維。 一位真正的科學家對此表示懷疑，會問自己最棘手的問題，并且對細節的關注程度令人難以置信。 這些是我們需要比分析師更多的特征。

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

至于差異，除了世界上用來描述“我如何使用數據來建模某些過程并做出我能做的最有用的預測”的不斷發展的詞語外，我并沒有看到太多差異。”-阿米特·巴哈塔恰拉亞VOX Media數據科學主管

What’s next for you, career-wise?

從職業角度來說，您接下來要做什么？

“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.

“當Data Science在推動行業價值增長中發揮越來越重要的作用時，我感到自己很幸運能夠在媒體和廣告領域工作。 我希望在提高公司和整個行業對數據科學技術和人員的采用和投資方面發揮領導作用。” -康卡斯特(Comcast)公司Freewheel數據科學負責人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

與學術界不同，業內最有效的數據科學家是那些能夠最好地生產和銷售其數據產品的科學家。 在這方面，我認為增長的最大機會來自向云計算的轉變，因為云計算使數據科學家可以將更多的精力放在手頭的邏輯和算法上，而將精力放在基礎架構和DevOps上。 因此，我希望將來看到更多的數據科學家擔當云架構師的角色。” -福布斯首席數據科學家Daryl Kang

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

“我想繼續進行教學，創新和指導，并幫助指導中小型組織使用其數據來提高智能。”

-Amit Bahattacharayya, Head of Data Science at VOX Media

-VOX Media數據科學主管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

“帶領我的團隊走上高效的預測洞察力的道路，因此，當機會錯失時，這是由您選擇的。” -Meredith公司受眾與分析高級經理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

“在世界上最大的體積捕獲階段進行這項研究絕對是驚人的！ 我的好奇心指向工作室各個角落的另一個研究問題，我們每天都在構建獨特的AI解決方案。 我們擁有無與倫比的視覺數據，并與藝術家攜手合作，獲得屢獲殊榮的作品，我們正在通過AI和數據革新娛樂業。 我很榮幸能夠擁有這個獨特的職位，使我的研究能夠通過沉浸式3D體驗真正影響世界。” -英特爾高級研究科學家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，在數據科學沙龍：將AI和ML應用于媒體，廣告和娛樂中 ，聆聽這些演講者的更多內容。