Data is ever present in the world today. Data can help predict presidential elections, Super Bowl champions, and even the weather. However, it's very hard, if not impossible, to predict how people feel unless they tell us. This is when impulse spending with data comes in handy. Companies are constantly…
Data is ever present in the world today. Data can help predict presidential elections, Super Bowl champions, and even the weather. However, it's very hard, if not impossible, to predict how people feel unless they tell us. This is when impulse spending with data comes in handy. Companies are constantly looking for ways to get honest feedback when they are doing market research. Often, the research obtained ends up being unreliable or biased in some way. Allowing users to make impulse purchases with survey data is the answer. Companies can still gather the data that they need to do market research and customers can get more features or lives for their favorite games. It becomes a win-win for both users and companies. By adding the option to pay with information instead of money, companies can still get value out of frugal players. Established companies might not care so much about the impulse spending for purchases made in the application, however they would find a great deal of value in hearing about what customers think of their product or upcoming event. The real value from getting data from customers is the ability to train analytics models so that companies can make better predictions about consumer behavior. More accurate predictions can lead to companies being better prepared to meet the needs to the customer. Impulse spending with data provides the foundation to creating a software that can create value from all types of users regardless of whether the user is willing to spend money in the application.
This paper describes a project involving the optimization of the analysis process of FreeSurfer and ANTS Registration for neuroscience analytics of patients at risk of cognitive decline using Nipype. The paper details the process of discovering more about Nipype, learning to use a supercomputer, and implementing the open-source python code…
This paper describes a project involving the optimization of the analysis process of FreeSurfer and ANTS Registration for neuroscience analytics of patients at risk of cognitive decline using Nipype. The paper details the process of discovering more about Nipype, learning to use a supercomputer, and implementing the open-source python code to fit the needs of the research lab. Nipype is a python-based initiative to unify the various software packages used within the neuroscience community for data analysis. This paper also serves as documentation of the steps taken to complete the project so that future students are able to continue the optimization process to result in one cohesive workflow in which data is able to flow through a unified pipeline of analysis in the future.
