Contains a list of colleges world-wide offering decision sciences course. Decision scientist considers data as a tool to make decisions and solve business problems. By now, we're accustomed to the what a great deal being a Data Scientist is. The demand for Data Scientists is only skyrocketing and will be go on maybe till aliens take over.
When I first came across this term and tried to learn about it, I was overwhelmed by the information. Everyone has picked it up and have written pages on end about it. There was one thing in common though.
We all have read many articles, blogs and seen posts of ‘decision science vs data science’. We haven’t actually tried to think why do we compare the two. There is more congruence between the two. I would say that the only main difference is that data science delivers the results and decision science helps us take calculative steps based on the results.
What if I asked you to compare the sports, chess and boxing. Many would argue chess as an activity rather than a sport but that is a discussion for some other time. Both require attention and patience and practice and pattern recognition in the opponent. Yet, they are quite different and does not require to set up a comparison between the two. The missing piece that we failed to notice was that they did not affect each other. Whereas, decision science depends a lot on data science.
Imagine this, you are a data scientist dumped with a huge database containing all sorts of data. Your superior asks you to comb through the data and provide him with your findings. We have all been there where based on certain results we can easily decide what to do. That is where the decision scientist comes in. It is not as easy as it sounds.
A decision scientist doesn’t just look at the data provided. There are factors like ‘past-experiences, variety of cognitive biases, individual differences, commitment and some more’. Data Science is a tool by which correct decisions can be made. Read more here.
Whoever said that data science is for the math majors or the computer genius in the class. Basic analysis is required in every field. You do not have to be Alan Turing to go about it. Psychology students learn distributions in their bachelors, biology majors have to understand the normal curve too. In short if data science is everywhere, so is decision science and has been so always.
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The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.