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Learning and the future are the key topics in the recent Youtube videos on Data Science. The main questions revolve around: “how to become a Data Scientist”, “what is a data scientist”, and “where data science is going”. But why there is so little explanation of data science to the masses?



7. Data Science – Eindhoven University of Technology – (views: 58K)

Category: Advertisement

A dramatic advertisement by Eindhoven University of Technology on their new programs in Data Science. The communication style is cinematographic, using computer graphics to mimic a sci-fi action movie, resembling to Minority Effect and Inception, through the use of technologies like Oculus rift and multi-touch interfaces. Here are the links to their Bachelor and Master programs.

8. Gigs: A day in the life of a data scientist – (views: 57K)

Category: Educational

RCRtv takes a look at a day in the life of a data scientist at the AT&T Foundry in Plano, Texas.

The video is an interview to the Senior Data Scientist Karthik Rajagopalan that answers to the questions: what data science does, background, how to stay on the cutting edge, tools adopted and some final advice for aspiring data scientists.

9. Data scientist vs Data analyst , their roles and qualification – (views: 47K)

Category: Educational

A screencasting video from Bigdata Simplified about the difference between data scientists and data analysts. The video stresses a lot on how the data is generated, its value and how to unleash its power, ending on what characterises a data analyst and what instead is peculiar of a data scientist.

10. The Future of Data Science – Data Science @ Stanford – (views: 37K)

Category: Educational

Dr. Euan Ashley, Associate Professor of Medicine & Genetics at Stanford University, Dr. Vijai Pande, Professor of Chemistry at Stanford University, Dr. Hector Garcia-Molina, Professor of Engineering & Electrical Engineering at Stanford University and Dr. John Hennessy, President of Stanford University, all together for an almost 26 minutes interesting conference on Data Science. The questions they try to address are, i.e.: how real is this emerging discipline? What opportunities and challenges does it present? How can Stanford nurture data science in research and education?

Observations:

Recently, I had the chance to talk to different data science startups. Their problem is: how reaching out their customers, alias business people, VC etc.

So, at this point I’ll translate my point of view in some questions:

  1. Is it possible to talk about data science only referring to data science? The most watched top-ten videos are definitely not for a tech-illiterate audience, but for those who are already fertile to know more about this field. My idea is instead: why don’t we pivot the discussion on much more hot topics, such as policy making, urbanism, security, health, art and privacy?
  2. Instead of focusing on “convincing” non data-driven companies to adopt a data-driven culture, hence to hire data scientists, why don’t we focus on translating data science to the masses first?

No matter how hard, on the internet, even at the bootcamp, they were stressing the importance of storytelling in Data Science, I felt that communication is a big issue in this field. And I’m not talking only about the visualization and reporting. It is old, it is clustered and often self-referential. I am aware I may sound bold and even a bit harsh, but what moves me to be this honest is a real desire to open Data Science up to the world. A key-performanceparameter should be how many tech-illiterate people get to know and familiarise with this field. Data Science might not be the holy grail, but it can change the world.

As a direct consequence of this thought, I decided to retrieve and analyse the text metadata of the first 500, most viewed videos on YouTube, from 2014, containing the words “data science” and or “data scientist” in their title. Once collected all the metadata, I concatenated in a bag of words all the tags, descriptions and title. I then used word2vec from gensim python framework. The analysis is simple but the results are intreresting.
The great majority of the 30725 words from the metadata are related to “business” (job, career, working, management, industry etc.), “education” (university, courses, learning, tutorial, programming etc.) and “data science tools and techniques” (machine learning, algorithms etc.). Words like “Future” and “Social” are only 0.13% of the total. The most similar words to “Future” are again related to “business” (job, career, information, product, chief). “Journalism”, “Progress”, “Political”, “Psychology” are less than 0.016% of the terms adopted when it comes to videos about Data Science. It is a field that also doesn’t seem to be “celebrity-oriented”. Dr. DJ Patil is the only influencer “to stand out”: 0.1% of the total number of words.

The picture that might come out from this very simple analysis is that:

  1. Data Science is a clustered topic. Those who already know about it look for it. Especially for tutorials and educational purposes. The others don’t.
  2. Data Science at the moment (from a communication point of view) strictly relates with careers/business.
  3. There is an evident lack of reframing Data Science into “hot topics”.

One of the discussions I was having with a senior data scientist was about explaining the reliability of Data Science for business. I liked his expression “that Science should remind us of the Galilean experimental scientific method. In this field we experiment, a lot. It’s really hard to explain this point.”

We should also experiment with communication.

Bio: Marco Nasuto is a Data Scientist, Aerospace Engineer and Filmmaker. Now working in Denmark.

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