l'Ishwar incroyable Nataraj

Now this post is a part 2 to my previous post on Data Implosion and not Explosion. Off late I have been reading a lot of books on data especially related to Data Science and one of the books I am on the verge of completion is Developing Analytic Talent: Becoming a Data Scientist by Vincent Granville. Now the reason I purchased this book was to get a deeper insight into what Data Scienists do and what is required in order to become one. The unfortunate part of this book is that getting a deeper insight into Data Science is absolutely incomprehensible. The reason behind this is that majority of the formulae (which I would love to understand and apply) are not defined clearly. And majority of these formulae's do not have a definition of the co-efficients used. Coming from a Computer Science background, I am familiar with the core aspects like the Big O notation and how to derive it from one's code, so reading through these parts was eating with a spoon since I do leverage these c...

I was planning on writing this article a while ago but never came down to it.... but I do have some time on my hands to write about how data has changed over the years. Nowadays data has been imploding and what I want to infer from implosion is that the manner in which data is being collated and published to end users with various transformations along the way creates a sense of cause and effect. You have massive data-sets but the information from this can be co-related in so many different ways that eventually one is unable to figure out how to go about getting the required information onto the end user's field of vision. Let us consider Big Data solutions....now whenever we feel that the volume of data has reached its significant end in terms of storage limitations we can go about introducing a solution (a big data solution) in order to contain the explosion and ensure that there is no data loss.... hence in this case we can always ensure that in the eventuality of a massive upt...