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Can data science help us find what makes a hit television show /
"Presented by Shilpi Bhattacharyya, Data Scientist at IBM. Who does not love the American television sitcom - Friends? And we definitely want to learn what makes this sitcom so popular. Can the most important aspects of some of the top shows of all the times be related? Is there something commo...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Los Angeles, California] :
Data Science Salon,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 0 | 0 | |a Can data science help us find what makes a hit television show / |c Data Science Salon. |
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| 520 | |a "Presented by Shilpi Bhattacharyya, Data Scientist at IBM. Who does not love the American television sitcom - Friends? And we definitely want to learn what makes this sitcom so popular. Can the most important aspects of some of the top shows of all the times be related? Is there something common which makes them a success? If not, can we find out and draw a correlation amongst them? In this talk, I would demonstrate the essential elements of few of these most successful sitcoms which have helped them connect with the audience at such a massive scale around the world. I would use data science and machine learning techniques as sentiment analysis, data visualization and correlation graphs on the transcripts available for these sitcoms to achieve the results. I would also focus briefly on the favorite characters. I believe this work would be able to bring out a concrete answer to the apparent question amongst the makers to understand the reasons which makes a hit show, with evidence backed up by data science."--Resource description page | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Information visualization. | |
| 650 | 0 | |a Electronic data processing |x Structured techniques. | |
| 650 | 0 | |a Situation comedies (Television programs) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Visualisation de l'information. | |
| 650 | 6 | |a Informatique |x Techniques structurées. | |
| 650 | 6 | |a Comédies de situation. | |
| 650 | 7 | |a Electronic data processing |x Structured techniques. |2 fast |0 (OCoLC)fst00907059 | |
| 650 | 7 | |a Information visualization. |2 fast |0 (OCoLC)fst00973185 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Situation comedies (Television programs) |2 fast |0 (OCoLC)fst01744318 | |
| 700 | 1 | |a Bhattacharyya, Shilpi, |e on-screen presenter. | |
| 710 | 2 | |a Data Science Salon, |e publisher. | |
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