The successful use of predictive analytics is strongly reliant on having unrestricted access to large amounts of accurate, clean, and relevant data. While predictive models utilising decision trees and k-means clustering can be quite complicated, the most difficult aspect is usually the neural network; that is, the model by which computers are trained to anticipate outcomes.

Predictive analytics, in a nutshell, saves time, effort, and money when forecasting business outcomes. Environmental factors, competitive intelligence, regulatory changes, and market conditions can all be factored into the mathematical calculation to provide more comprehensive views at a lower cost.

Quantum Algorithms based on qubits often take my attention. Although Quantum Computing is not exactly a reality today, I do believe in its potential for daily usage being important as time goes on. 

I have given a 10 minute seminar on "Convergence of Quantum Computing and IoT" next month in my class (based on literature review / study from IEEE standard scientific papers). Report and presentation for the same would be uploaded soon here and on Youtube respectively.

I've always had a dream and passion for this field. When I first heard about cloud technology, I related it as telepathy amongst computers or machines. This led me to a dream - followed with a few attempts to try and realise this concepts. Although I haven't made substantial progress, I feel satisfied to know that there are projects undertaken in major universities such as Massachusetts MIT and that, there's a scope in this field for me someday into near future.