Shadowing Practice: Class Takeaways — Turning Data Into a Superpower - Learn English Speaking with Video
Ders oluşturuluyor...
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Hi, my name is Mohsen Bayadi and I'm a professor of operation information and technology at Stanford Graduate School of Business.
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In a world with a ton of information, the ability to turn raw data into better decisions is a superpower.
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Today I will be sharing five key takeaways from my course business intelligence from big data
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first guide your decisions by data human intuition is powerful but when it comes to assessing uncertainty it's sometimes pretty flawed
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we naturally see patterns when there is no pattern
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or we overreact to noise let me give you an example
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consider a hospital vp in charge of quality of care who sees a drop in infection rates
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and assumes a new protocol is working.
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But often, it's just a lucky streak.
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Random noise shows up as a trend.
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By grounding your decisions in diverse data rather than a single perspective or AI tool,
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you can tease out the true signal from the noise and manage uncertainty with better precision.
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second invest in your technical capabilities by getting your hands dirty the philosophy of this course is
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that the best way to learn is by doing you can't just read about ai
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and data science you have to use the tools yourself to truly lead you need to understand the capabilities
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and limitations of these technologies in this class we dive into writing code using ai apis
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And it's the beauty of these hands -on experiences that demystifies the technology for us,
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forcing you to understand it better and empowers you to build quick proof of concepts that can transform your business.
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Third, formulate the right questions.
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We often rush to find answers, but the hardest part of using data for decisions is asking the right questions.
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A sophisticated AI agent or model is often useless if it solves the wrong problem.
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An important skill we work on in this course is to
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learn to translate a business challenge into a concrete model where you can identify the right AI -based method to solve it.
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Before using any technology, ask yourself, what exactly are we trying to solve?
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And how will data and AI tell us if we have succeeded?
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fourth augment technology with expert human judgment we often see two extremes those who avoid ai
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because it makes mistakes and those who over rely on it both are suboptimal
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and the latter is even dangerous these technologies often have blind spots nuanced issues of bias
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that won't go away easily your goal shouldn't be cognitive offloading or letting the machine do all the thinking.
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Instead, use your expert judgment to verify the output, understand the limitations, and layer your reasoning on top of the technology.
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Finally, lead through collaboration.
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Decision -making with data is a team sport.
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An individual contributor's style of work that happens in a silo often has limited impact because it lacks business context.
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As a leader, your competitive advantage comes from bridging that gap.
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Cross -functional teamwork is key.
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By enabling deep collaboration between domain experts,
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AI scientists, and engineers, you make sure the technical solutions are aligned with real business value.
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Don't just consume the analysis, actively shape it.
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So, what does this all add up?
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By guiding decisions with data, investing in your capabilities, and balancing AI power with human wisdom, you don't just become a user of technology.
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You become a leader who can navigate uncertainty with more clarity.
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Implementing these principles will help you lead your teams effectively in a world that is becoming more AI -driven.
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Teaching is beautiful because it helps you learn better.
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At the same time, sometimes I just get excited, like I want to learn about a new technology and I want to share that with others.
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So it allows me to do both.
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Bu dersin kelimeleri ve konuşma notları
Bu konuşma dersi “Class Takeaways — Turning Data Into a Superpower” videosuna dayanıyor. En çok tekrarlanan kelimeler: technology, decision, learn, uncertainty, capabilities. Bu videoda gölgeleme çalışması için 41 cümle ve 640 kelime var. Konuşma bölümü 4:51 sürüyor. Konuşmacı dakikada yaklaşık 132 kelimelik dengeli bir hızla konuşuyor; gölgeleme için rahat bir tempo. Kelimelerin yalnızca %80’i İngilizcede en sık kullanılan 3.000 kelime arasında, bu yüzden kelime dağarcığı zorlayıcı.
Bu videodaki önemli kelimeler
Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| solve fiil | /sɒlv/ | çözmek, halletmek |
| uncertainty isim | /ʌnˈsɜːtənti/ | belirsizlik, muğlaklık |
| collaboration isim | /kəˌlæbəˈɹeɪʃən/ | imece, iş birliği |
| judgment isim | /ˈd͡ʒʌd͡ʒ.mənt/ | yargı, hüküm |
| rush fiil | /ɹʌʃ/ | acele etmek, aşıkmak |
| concrete sıfat | /ˌkɑnˈkɹit/ | somut |
| output isim | /ˈaʊtpʊt/ | çıktı |
| skill isim | /skɪl/ | beceri, maharet |
| layer isim | /ˈleɪ̯ɚ/ | katman, tabaka |
| principle isim | /ˈpɹɪn.sɪ.pəl/ | prensip, ilke |
| infection isim | /ɪnˈfɛkʃən/ | enfeksiyon |
| wisdom isim | /ˈwɪzdəm/ | bilgelik, hikmet |
| functional sıfat | /ˈfʌŋkt͡ʃənəl/ | işlevsel, fonksiyonel |
| scientist isim | /ˈsaɪəntəst/ | âlim, âlime |
| implement fiil | /ˈɪmpləmənt/ | tatbik etmek, uygulamak |
Duyacağınız deyimsel fiiller
| Kelime | Anlam |
|---|---|
| show up fiil | ortaya çıkmak |
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | have succeeded · has limited |
| İlgi cümlecikleri who / which + cümle — bir kişi ya da şey hakkında ek bilgi | those who avoid · those who over |
Dikkat edilecek telaffuzlar
Konuşmacı don't, I'm, can't gibi kısaltılmış biçimleri 7 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “sh” ve “zh” sesleri: collaboration /kəˌlæbəˈɹeɪʃən/, limitation /lɪmɪˈteɪʃən/, rush /ɹʌʃ/, infection /ɪnˈfɛkʃən/, precision /pɹɪˈsɪʒ.ən/
- Uzun kelimeler — vurguyu doğru yere koyun: capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, uncertainty /ʌnˈsɜːtənti/, collaboration /kəˌlæbəˈɹeɪʃən/, limitation /lɪmɪˈteɪʃən/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/
Türkçe konuşanların zorlandığı sesler:
- /w/ — dudaklar yuvarlak, /v/ değil: wisdom /ˈwɪzdəm/, takeaway /ˈteɪkəweɪ/
- Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: skill /skɪl/, principle /ˈpɹɪn.sɪ.pəl/, protocol /ˈpɹoʊtəˌkɑl/, streak /stɹiːk/, transform /tɹænsˈfɔɹm/
- /æ/ — “e”den daha açık: collaboration /kəˌlæbəˈɹeɪʃən/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, actively /ˈæktɪvli/, transform /tɹænsˈfɔɹm/, translate /tɹænzˈleɪt/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- Normal hızda cümle cümle tekrar edin; ritminiz konuşmacıyla aynı olana kadar her cümleyi yineleyin.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; solve, uncertainty, collaboration gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.