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Take that initial first step with our services at today and you'll be pleased with the results! question 1: business applications of data mining web/library research list some business applications of data mining techniques. See for example: 8qskhk?opendocument amp site spss amp cty en_us o this is just one example of a place to find case studies and success stories. Question 2: the data mining process describe the industry standard crisp dm data mining process model sass semma model choose one of your business applications from question 1, illustrate the usage of either crisp dm or semma for that application: e.g.

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In the data preparation phase of crispdm describe the specific data sources you would use for that application, etc. Further instructions for assignment 1 for question 1, you should format your answer as a table with the headings business objective, companies that do or could pursue this data mining objective , data mining algorithm/technique, sample output, and web address as requested in the question. For example, a business objective of provide payment processing solutions is not specific enough rather say produce a model to score transactions and identify the transactions most likely to be fraudulent. O similarly, for outputs of data mining, company lowers percentage of fraudulent transactions is fine as a general goal, but give me more details of possible specific outputs: e.g. Give example rules that could be produced like large transactions by people in queens who have held accounts for less than 6 weeks are likely to be fraudulent.

For question 2, on the application of data mining techniques, make sure to describe both the crisp dm and semma processes. O the goal is to provide good descriptions of how specifically to apply each of the 6 stages of the process to your particular case. O more importantly, you should give specific actions for each phase of the data mining process when explaining how the process could be applied to your particular case. For example, understand data is not specific enough and would not earn you any of the points for the application part of the question instead, under the heading data understanding give details like gather customer data from internal database, including customer identifier, age, purchase history. Instead, under the heading data preparation, write bin the customers into 5 equal bins by income attribute compute aggregates for past 3 months, past 6 months, and past 12 months customer purchases. Under the heading evaluation you might explain that a model that picks fraudulent transactions with 98% recall, and 80% precision is probably sufficient and that low recall is costly because each fraudulent transaction that falls through our checks is expensive, whereas low precision is not too costly as transactions that were rejected falsely do not lose us a lot of profits.

Model evaluation is dealt with more detail in lectures after the assignment is due you should have picked up knowledge about evaluation criteria from reading the two crows reading. O for the deployment phase of crisp dm , you should explain how the company could exploit profit from the model produced and what specific actions they did or place this order or a similar order with us today and get an amazing discount the doctoral degree program in operations research enables phd candidates to contribute to advanced research and state of the art knowledge in a selected field. The program provides both a thorough background in the theory of or as well as in developing and applying or methods in practice. Many graduates of the doctoral degree program assume academic positions in the us and abroad others work usually as researchers or consultants in business and industry. The formal course work and other requirements provide a foundation for the student to undertake original and creative research in a topic area selected in consultation with a thesis advisor. A student may wish to focus solely on the theory and methodology of operations research or, alternatively, on the application of or methods and models in a particular area. In addition to this track the orc offers two optional tracks specializing in the areas of operations management and networked systems.

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For more information on the specific requirements for each of these tracks, please see our phd tracks page. At least two of these subjects must be taken in optimization from the list below, at least two in applied probability, at least one in statistics and at least one in or modeling. These subjects must be taken by the end of the student's 6th semester and students must maintain a gpa of 4.5 or better.

Logistical and transportation planning methods 6.254 game theory with engineering applications 6.268 network science and models 15.071 the analytics edge 15.764.1 inventory theory and supply chains 15.764.2 revenue management and pricing hands on experience ndash doctoral degree student must satisfy a hands on experience requirement. This requirement can be satisfied in three ways, namely: option 1: the student engages in a summer internship during which the student builds or models that address a real world application. The student submits a a brief one two page report that outlines what has been done to be submitted electronically via email , and b a letter, from the internship supervisor, outlining the extent to which or models have been used to address a real world application and the student's role in it. Option 2: undertake a hands on project with an orc faculty member, either as part of a supervised research activity or an extra part of a regular subject. The student and the faculty member should submit documentation of the project and the work he/she has performed. Option 3: the student completes, with at least a grade of b, the special seminar course 15.099 or in the real world in which students build and implement models that address real world or applications or course 15.071 the analytics edge. Students taking these courses may choose to not have it fulfill their hands on requirement if they would prefer to satisfy it via option 1 or 2.