There’s segmentation in the insurance industry. Depending on the underwriting done by the insurance companies, individuals or companies who are looking for insurance are segmented into different groups depending […]

The sum of squared error measures the distances of the points in a cluster to its center. If more points are closer to the center it means that there is cohesion which means more accurate data. If the points are […]

Two important parts of clustering is cohesion and separation. Cohesion is how close the data is while separation is how far the data is from each other.

There is an app called Liveplasma that is a music and movie visualization app. If you type in a movie, actor, song, etc., it’ll show you a visualized map of related people or items.

http://www.liveplasma.com/

The guest lecture was useful. There were a lot of things I learned out of which these are three.
1. How huge of a role data plays in decision making in corporations.
2. Data is an invaluable asset.
3. The usage […]

An example of a data driven decision making is when wawa decided what products to sell and what not to in their stores. I believe that sometimes a gut driven approach is good sometimes but in this day and age […]

I am a Legal Studies and Risk Management major. Risk management is all about knowing the risks and the insurances given based on those risks and the data that is collected that helps predict the risks. My job […]

It seems to me as though Spotcrime is more of a data analytics website because the visitors to the site have direct access to the raw information of the crime occurring in each neighborhood.

Data mining is useful when an insurance company is trying to calculate estimated loss. For example if a hospital is insured and has had a particular # of liability suits each month and this is recorded, this would […]

A business question that one could answer using a decision tree would be to play or not to play based on weather. The probabilities would be based on the weather for the day, week, or for whichever days in […]

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Data warehouse – Takes many forms, really is just a repository for data
Data mart – More focused, specially designed for analysis
Data cube – Organization of data as a “multidimensional matrix”

During this week we delved more into the idea of decision trees. The point is to group the data as close and similarly as possible. The thing about it is, the way we create the decision tree can affect the […]

This week I learned how to use a whole lot of data and to figure out the possibilities of the future from it. The decision trees were the most interesting. We were taught how to ‘classify data according to a […]

This week I learned how to work with functions in Excel. I’ve got to admit it’s something that’s a lot more easier to pick up on than MySQL or maybe I just never had the head for that software. The in-class […]

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During week 4 I was still in the process of catching up with the work in class. I was finally able to understand the complexity of the ERD diagrams which turned out to be very simple. We were introduced to MySQL […]

There are 3 types of customer segments:
1. Whales – these are the people who spend thousands to millions on the gaming floor
—— Customer interaction is extremely high for whales because the hosts try to make […]

Comment on the following with respect to the MGM case: “you cannot become a whale just by eating at the buffet.” Also, explain whether this makes business sense for the firms.

Whales are those people who visit […]