Basically, it’s a form of creative thinking. So, the existence of these tools derive from the concept of ‘mind-mapping’. Mind Mapping isn’t a new concept. I too had this question when I first heard about these tools. This article is not meant to promote any tool commercially.
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Links for access / download are provided. Note: Majority of the tools listed below can be availed for FREE. Once you have gone through this article, you can bring these tools in your work flow process. These tools will enhance your breadth of thinking and your ability to comprehend and retain more information. These tools provide an excellent way of generating ideas in a creative way. In this article, I’ve listed down some amazing mind mapping tools useful for a data scientists. When I give the problem mentioned above to people, more than 70% of them don’t put a structure to it. Sadly, people don’t realize the importance of structured thinking or go back to their unstructured instincts as soon as they face an unstructured problem. Having a structure not only helps an analyst understand the problem at a macro level, it also helps by identifying areas which require deeper understanding. Structured Thinking allows us to map our ideas in structured fashion, thereby enabling us to identify which areas need the most attention. Structured thinking is a process of putting a framework to an unstructured problem. But, the ability to think and ideate in a structured manner is what makes a data scientist special.
If you are not sure that you have captured most of the factors or your list looks like a simple to do list without any framework or structure, this article should help you tremendously.Įveryone has the ability to think simultaneously in all directions. So, how was the exercise? Was it easy or difficult? How sure are you that you have written all possible factors as part of this thought capture?
Once you have written at least a few factors, let’s move ahead. What are the possible factors / changes you would make in your store? You have been appointed as a store manager for our worst performing store. Let us start this with a simple exercise, the kind of which every data scientist faces regularly:
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