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Srividya Kannan Ramachandran
  • New York, NY
  • United States
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Profile Information

Short Bio
Srividya is a senior data scientist based in New York City. Besides building statistical models in R/Python to drive capex investment decisions in the telecom industry, she also displays her abstract art in 2 Chelsea, NY-based art galleries.

You can contact her via email at srivi019ATgmail.com
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/srividyakannanramachandran
Professional Status
Manager
Years of Experience:
8
Industry:
Telecommunications
Your Job Title:
Lead Data Scientist
How did you find out about DataScienceCentral?
LinkedIn
Interests:
Networking, Recruiting
What is your Favorite Data Mining or Analytical Website?
http://kaggle.com

Srividya Kannan Ramachandran's Blog

Can computers think? An epistemology for Artificial Intelligence

Posted on January 20, 2017 at 12:02pm 0 Comments

As AI enters our homes through smart home devices or tries to conquer our streets through self-driving cars, one need not be a Luddite to contemplate the potentially heavy implications of AI upon our daily lives and livelihood. The key to answering the question and indeed to understand the ultimate limits of AI is to ask if machines can really think. In this article, I list three tests drawn from three different disciplines to address that…

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Charting a Mandate for a Chief Data Scientist

Posted on January 4, 2017 at 6:00pm 0 Comments

The profusion of big data alongside helpful nudges from Wall Street has inspired many companies to create Chief Data Officer(CDO) and Chief Data Scientist(CDS) roles. The mandate for these roles remains inchoate much in tune with the incipient nature of application of machine learning and predictive analytics within a large corporate structure.

In a previous post, I had introduced a new paradigm – the…

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ALDI – A New Paradigm for Integrating Marketing Analytics with Data Science

Posted on December 21, 2016 at 3:00pm 0 Comments

Owing to the data deluge and the Cambrian explosion of machine learning techniques over the past decade, one might have expected the transformation of marketing strategy into a predominantly quantitative discipline by now. The fact that it hasn’t happened yet, and the observation that marketing is still influenced by a lot of qualitative inputs can be ascribed to two reasons, in my opinion. The first and principal reason continues to be institutional inertia. Second, there is a…

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Machine Learning is dead – Long live machine learning!

Posted on May 31, 2016 at 8:00am 0 Comments

You may be thinking that this title makes no sense at all. ML, AI, ANN and Deep learning have made it into the everyday lexicon and here I am, proclaiming that ML is dead. Well, here is what I mean…

The open sourcing of entire ML frameworks marks the end of a phase of rapid development of tools, and thus marks the death of ML as we have known it so far. The next phase will be marked with ubiquitous application of these tools into software applications. And that is how ML…

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