IntroductionHypothesis generation requires you to have structured thinking whereas data exploration requires patience to slice and dice data in multiple ways. In this article, I will focus on the steps required to clean and understand data in a comprehensive way.To improve your structured thinking, I would suggest you to check out the flawless post written by Kunal – “Tools to Improve structure Thinking“.7 Steps of Data Exploration and Preparation (Part 1)Remember the quality of your inputs
A few days back, one of my friend was building a model to predict propensity of conversion of leads procured through an Online Sales partner. While presenting his findings to stakeholders, one of the insights he mentioned lead to a very involved discussion in the room. The insight was as follows:The higher the number of times a lead is shared by partner, higher are its chances of conversion.Following arguments were presented during the debate which ensued:Group 1 (Pro-insight) main hypothesis:
Skills for Data Scientist:
Data scientist way of thinking:
In early stages, when we have a problem, physicist or chemist understand the problem and come up with theory. And, engineers took these equation and change few parameters to…
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GBM & AdaBoost are boosting algorithms used when we deal with plenty of data to make a prediction with high prediction power. Boosting is an ensemble learning algorithm which combines the prediction of s…
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In the last 4-5 years, there has been an exponential increase in data capturing at every possible stages. Corporates/ Government Agencies/ Research organisations are not only coming with new sourc…
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